demo_llava_gradio.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "1fd4Ib0AKHGp",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 123933,
     "status": "ok",
     "timestamp": 1721459472617,
     "user": {
      "displayName": "hiro",
      "userId": "15725092376508535253"
     },
     "user_tz": -540
    },
    "id": "1fd4Ib0AKHGp",
    "outputId": "28c62713-5d4d-438b-a009-799c734bb39b",
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting git+https://github.com/hibikaze-git/LLaVA-JP@feature/tanuki-moe\n",
      "  Cloning https://github.com/hibikaze-git/LLaVA-JP (to revision feature/tanuki-moe) to /tmp/pip-req-build-dghverta\n",
      "  Running command git clone --filter=blob:none --quiet https://github.com/hibikaze-git/LLaVA-JP /tmp/pip-req-build-dghverta\n",
      "  Running command git checkout -b feature/tanuki-moe --track origin/feature/tanuki-moe\n",
      "  Switched to a new branch 'feature/tanuki-moe'\n",
      "  Branch 'feature/tanuki-moe' set up to track remote branch 'feature/tanuki-moe' from 'origin'.\n",
      "  Resolved https://github.com/hibikaze-git/LLaVA-JP to commit 01581ccdaca7f4dd6ca0f20ba0b11021ddf1fdcf\n",
      "  Installing build dependencies ... \u001b[?25ldone\n",
      "\u001b[?25h  Getting requirements to build wheel ... \u001b[?25ldone\n",
      "\u001b[?25h  Preparing metadata (pyproject.toml) ... \u001b[?25ldone\n",
      "\u001b[?25hRequirement already satisfied: torch in /tmp/own/conda/envs/Dev/lib/python3.10/site-packages (from llavajp==0.1) (2.1.2+cu121)\n",
      "Requirement already satisfied: transformers in /tmp/own/conda/envs/Dev/lib/python3.10/site-packages (from llavajp==0.1) (4.43.1)\n",
      "Requirement already satisfied: sentencepiece in /tmp/own/conda/envs/Dev/lib/python3.10/site-packages (from llavajp==0.1) (0.2.0)\n",
      "Requirement already satisfied: accelerate in /tmp/own/conda/envs/Dev/lib/python3.10/site-packages (from llavajp==0.1) (0.33.0)\n",
      "Requirement already satisfied: peft in /tmp/own/conda/envs/Dev/lib/python3.10/site-packages (from llavajp==0.1) (0.11.1)\n",
      "Requirement already satisfied: bitsandbytes in /tmp/own/conda/envs/Dev/lib/python3.10/site-packages (from llavajp==0.1) (0.43.2)\n",
      "Requirement already satisfied: numpy in /tmp/own/conda/envs/Dev/lib/python3.10/site-packages (from llavajp==0.1) (1.26.3)\n",
      "Requirement already satisfied: scikit-learn in /tmp/own/conda/envs/Dev/lib/python3.10/site-packages (from llavajp==0.1) (1.5.1)\n",
      "Requirement already satisfied: einops in /tmp/own/conda/envs/Dev/lib/python3.10/site-packages (from llavajp==0.1) (0.8.0)\n",
      "Requirement already satisfied: tqdm in /tmp/own/conda/envs/Dev/lib/python3.10/site-packages (from llavajp==0.1) (4.66.4)\n",
      "Requirement already satisfied: wandb in /tmp/own/conda/envs/Dev/lib/python3.10/site-packages (from llavajp==0.1) (0.17.5)\n",
      "Requirement already satisfied: gradio in /tmp/own/conda/envs/Dev/lib/python3.10/site-packages (from llavajp==0.1) (4.39.0)\n",
      "Requirement already satisfied: packaging>=20.0 in /tmp/own/conda/envs/Dev/lib/python3.10/site-packages (from accelerate->llavajp==0.1) (24.1)\n",
      "Requirement already satisfied: psutil in /tmp/own/conda/envs/Dev/lib/python3.10/site-packages (from accelerate->llavajp==0.1) (6.0.0)\n",
      "Requirement already satisfied: pyyaml in /tmp/own/conda/envs/Dev/lib/python3.10/site-packages (from accelerate->llavajp==0.1) (6.0.1)\n",
      "Requirement already satisfied: huggingface-hub>=0.21.0 in /tmp/own/conda/envs/Dev/lib/python3.10/site-packages (from accelerate->llavajp==0.1) (0.24.1)\n",
      "Requirement already satisfied: safetensors>=0.3.1 in /tmp/own/conda/envs/Dev/lib/python3.10/site-packages (from accelerate->llavajp==0.1) (0.4.3)\n",
      "Requirement already satisfied: filelock in /tmp/own/conda/envs/Dev/lib/python3.10/site-packages (from torch->llavajp==0.1) (3.13.1)\n",
      "Requirement already satisfied: typing-extensions in /tmp/own/conda/envs/Dev/lib/python3.10/site-packages (from torch->llavajp==0.1) (4.9.0)\n",
      "Requirement already satisfied: sympy in /tmp/own/conda/envs/Dev/lib/python3.10/site-packages (from torch->llavajp==0.1) (1.12)\n",
      "Requirement already satisfied: networkx in /tmp/own/conda/envs/Dev/lib/python3.10/site-packages (from torch->llavajp==0.1) (3.2.1)\n",
      "Requirement already satisfied: jinja2 in /tmp/own/conda/envs/Dev/lib/python3.10/site-packages (from torch->llavajp==0.1) (3.1.3)\n",
      "Requirement already satisfied: fsspec in /tmp/own/conda/envs/Dev/lib/python3.10/site-packages (from torch->llavajp==0.1) (2024.2.0)\n",
      "Requirement already satisfied: triton==2.1.0 in /tmp/own/conda/envs/Dev/lib/python3.10/site-packages (from torch->llavajp==0.1) (2.1.0)\n",
      "Requirement already satisfied: aiofiles<24.0,>=22.0 in /tmp/own/conda/envs/Dev/lib/python3.10/site-packages (from gradio->llavajp==0.1) (23.2.1)\n",
      "Requirement already satisfied: anyio<5.0,>=3.0 in /tmp/own/conda/envs/Dev/lib/python3.10/site-packages (from gradio->llavajp==0.1) (4.4.0)\n",
      "Requirement already satisfied: fastapi in /tmp/own/conda/envs/Dev/lib/python3.10/site-packages (from gradio->llavajp==0.1) (0.111.1)\n",
      "Requirement already satisfied: ffmpy in /tmp/own/conda/envs/Dev/lib/python3.10/site-packages (from gradio->llavajp==0.1) (0.3.2)\n",
      "Requirement already satisfied: gradio-client==1.1.1 in /tmp/own/conda/envs/Dev/lib/python3.10/site-packages (from gradio->llavajp==0.1) (1.1.1)\n",
      "Requirement already satisfied: httpx>=0.24.1 in /tmp/own/conda/envs/Dev/lib/python3.10/site-packages (from gradio->llavajp==0.1) (0.27.0)\n",
      "Requirement already satisfied: importlib-resources<7.0,>=1.3 in /tmp/own/conda/envs/Dev/lib/python3.10/site-packages (from gradio->llavajp==0.1) (6.4.0)\n",
      "Requirement already satisfied: markupsafe~=2.0 in /tmp/own/conda/envs/Dev/lib/python3.10/site-packages (from gradio->llavajp==0.1) (2.1.5)\n",
      "Requirement already satisfied: matplotlib~=3.0 in /tmp/own/conda/envs/Dev/lib/python3.10/site-packages (from gradio->llavajp==0.1) (3.9.1)\n",
      "Requirement already satisfied: orjson~=3.0 in /tmp/own/conda/envs/Dev/lib/python3.10/site-packages (from gradio->llavajp==0.1) (3.10.6)\n",
      "Requirement already satisfied: pandas<3.0,>=1.0 in /tmp/own/conda/envs/Dev/lib/python3.10/site-packages (from gradio->llavajp==0.1) (2.2.2)\n",
      "Requirement already satisfied: pillow<11.0,>=8.0 in /tmp/own/conda/envs/Dev/lib/python3.10/site-packages (from gradio->llavajp==0.1) (10.2.0)\n",
      "Requirement already satisfied: pydantic>=2.0 in /tmp/own/conda/envs/Dev/lib/python3.10/site-packages (from gradio->llavajp==0.1) (2.8.2)\n",
      "Requirement already satisfied: pydub in /tmp/own/conda/envs/Dev/lib/python3.10/site-packages (from gradio->llavajp==0.1) (0.25.1)\n",
      "Requirement already satisfied: python-multipart>=0.0.9 in /tmp/own/conda/envs/Dev/lib/python3.10/site-packages (from gradio->llavajp==0.1) (0.0.9)\n",
      "Requirement already satisfied: ruff>=0.2.2 in /tmp/own/conda/envs/Dev/lib/python3.10/site-packages (from gradio->llavajp==0.1) (0.5.4)\n",
      "Requirement already satisfied: semantic-version~=2.0 in /tmp/own/conda/envs/Dev/lib/python3.10/site-packages (from gradio->llavajp==0.1) (2.10.0)\n",
      "Requirement already satisfied: tomlkit==0.12.0 in /tmp/own/conda/envs/Dev/lib/python3.10/site-packages (from gradio->llavajp==0.1) (0.12.0)\n",
      "Requirement already satisfied: typer<1.0,>=0.12 in /tmp/own/conda/envs/Dev/lib/python3.10/site-packages (from gradio->llavajp==0.1) (0.12.3)\n",
      "Requirement already satisfied: urllib3~=2.0 in /tmp/own/conda/envs/Dev/lib/python3.10/site-packages (from gradio->llavajp==0.1) (2.2.2)\n",
      "Requirement already satisfied: uvicorn>=0.14.0 in /tmp/own/conda/envs/Dev/lib/python3.10/site-packages (from gradio->llavajp==0.1) (0.30.3)\n",
      "Requirement already satisfied: websockets<12.0,>=10.0 in /tmp/own/conda/envs/Dev/lib/python3.10/site-packages (from gradio-client==1.1.1->gradio->llavajp==0.1) (11.0.3)\n",
      "Requirement already satisfied: scipy>=1.6.0 in /tmp/own/conda/envs/Dev/lib/python3.10/site-packages (from scikit-learn->llavajp==0.1) (1.14.0)\n",
      "Requirement already satisfied: joblib>=1.2.0 in /tmp/own/conda/envs/Dev/lib/python3.10/site-packages (from scikit-learn->llavajp==0.1) (1.4.2)\n",
      "Requirement already satisfied: threadpoolctl>=3.1.0 in /tmp/own/conda/envs/Dev/lib/python3.10/site-packages (from scikit-learn->llavajp==0.1) (3.5.0)\n",
      "Requirement already satisfied: regex!=2019.12.17 in /tmp/own/conda/envs/Dev/lib/python3.10/site-packages (from transformers->llavajp==0.1) (2024.5.15)\n",
      "Requirement already satisfied: requests in /tmp/own/conda/envs/Dev/lib/python3.10/site-packages (from transformers->llavajp==0.1) (2.32.3)\n",
      "Requirement already satisfied: tokenizers<0.20,>=0.19 in /tmp/own/conda/envs/Dev/lib/python3.10/site-packages (from transformers->llavajp==0.1) (0.19.1)\n",
      "Requirement already satisfied: click!=8.0.0,>=7.1 in /tmp/own/conda/envs/Dev/lib/python3.10/site-packages (from wandb->llavajp==0.1) (8.1.7)\n",
      "Requirement already satisfied: docker-pycreds>=0.4.0 in /tmp/own/conda/envs/Dev/lib/python3.10/site-packages (from wandb->llavajp==0.1) (0.4.0)\n",
      "Requirement already satisfied: gitpython!=3.1.29,>=1.0.0 in /tmp/own/conda/envs/Dev/lib/python3.10/site-packages (from wandb->llavajp==0.1) (3.1.43)\n",
      "Requirement already satisfied: platformdirs in /tmp/own/conda/envs/Dev/lib/python3.10/site-packages (from wandb->llavajp==0.1) (4.2.2)\n",
      "Requirement already satisfied: protobuf!=4.21.0,<6,>=3.19.0 in /tmp/own/conda/envs/Dev/lib/python3.10/site-packages (from wandb->llavajp==0.1) (4.25.3)\n",
      "Requirement already satisfied: sentry-sdk>=1.0.0 in /tmp/own/conda/envs/Dev/lib/python3.10/site-packages (from wandb->llavajp==0.1) (2.11.0)\n",
      "Requirement already satisfied: setproctitle in /tmp/own/conda/envs/Dev/lib/python3.10/site-packages (from wandb->llavajp==0.1) (1.3.3)\n",
      "Requirement already satisfied: setuptools in /tmp/own/conda/envs/Dev/lib/python3.10/site-packages (from wandb->llavajp==0.1) (69.5.1)\n",
      "Requirement already satisfied: idna>=2.8 in /tmp/own/conda/envs/Dev/lib/python3.10/site-packages (from anyio<5.0,>=3.0->gradio->llavajp==0.1) (3.4)\n",
      "Requirement already satisfied: sniffio>=1.1 in /tmp/own/conda/envs/Dev/lib/python3.10/site-packages (from anyio<5.0,>=3.0->gradio->llavajp==0.1) (1.3.1)\n",
      "Requirement already satisfied: exceptiongroup>=1.0.2 in /tmp/own/conda/envs/Dev/lib/python3.10/site-packages (from anyio<5.0,>=3.0->gradio->llavajp==0.1) (1.2.2)\n",
      "Requirement already satisfied: six>=1.4.0 in /tmp/own/conda/envs/Dev/lib/python3.10/site-packages (from docker-pycreds>=0.4.0->wandb->llavajp==0.1) (1.16.0)\n",
      "Requirement already satisfied: gitdb<5,>=4.0.1 in /tmp/own/conda/envs/Dev/lib/python3.10/site-packages (from gitpython!=3.1.29,>=1.0.0->wandb->llavajp==0.1) (4.0.11)\n",
      "Requirement already satisfied: certifi in /tmp/own/conda/envs/Dev/lib/python3.10/site-packages (from httpx>=0.24.1->gradio->llavajp==0.1) (2022.12.7)\n",
      "Requirement already satisfied: httpcore==1.* in /tmp/own/conda/envs/Dev/lib/python3.10/site-packages (from httpx>=0.24.1->gradio->llavajp==0.1) (1.0.5)\n",
      "Requirement already satisfied: h11<0.15,>=0.13 in /tmp/own/conda/envs/Dev/lib/python3.10/site-packages (from httpcore==1.*->httpx>=0.24.1->gradio->llavajp==0.1) (0.14.0)\n",
      "Requirement already satisfied: contourpy>=1.0.1 in /tmp/own/conda/envs/Dev/lib/python3.10/site-packages (from matplotlib~=3.0->gradio->llavajp==0.1) (1.2.1)\n",
      "Requirement already satisfied: cycler>=0.10 in /tmp/own/conda/envs/Dev/lib/python3.10/site-packages (from matplotlib~=3.0->gradio->llavajp==0.1) (0.12.1)\n",
      "Requirement already satisfied: fonttools>=4.22.0 in /tmp/own/conda/envs/Dev/lib/python3.10/site-packages (from matplotlib~=3.0->gradio->llavajp==0.1) (4.53.1)\n",
      "Requirement already satisfied: kiwisolver>=1.3.1 in /tmp/own/conda/envs/Dev/lib/python3.10/site-packages (from matplotlib~=3.0->gradio->llavajp==0.1) (1.4.5)\n",
      "Requirement already satisfied: pyparsing>=2.3.1 in /tmp/own/conda/envs/Dev/lib/python3.10/site-packages (from matplotlib~=3.0->gradio->llavajp==0.1) (3.1.2)\n",
      "Requirement already satisfied: python-dateutil>=2.7 in /tmp/own/conda/envs/Dev/lib/python3.10/site-packages (from matplotlib~=3.0->gradio->llavajp==0.1) (2.9.0.post0)\n",
      "Requirement already satisfied: pytz>=2020.1 in /tmp/own/conda/envs/Dev/lib/python3.10/site-packages (from pandas<3.0,>=1.0->gradio->llavajp==0.1) (2024.1)\n",
      "Requirement already satisfied: tzdata>=2022.7 in /tmp/own/conda/envs/Dev/lib/python3.10/site-packages (from pandas<3.0,>=1.0->gradio->llavajp==0.1) (2024.1)\n",
      "Requirement already satisfied: annotated-types>=0.4.0 in /tmp/own/conda/envs/Dev/lib/python3.10/site-packages (from pydantic>=2.0->gradio->llavajp==0.1) (0.7.0)\n",
      "Requirement already satisfied: pydantic-core==2.20.1 in /tmp/own/conda/envs/Dev/lib/python3.10/site-packages (from pydantic>=2.0->gradio->llavajp==0.1) (2.20.1)\n",
      "Requirement already satisfied: charset-normalizer<4,>=2 in /tmp/own/conda/envs/Dev/lib/python3.10/site-packages (from requests->transformers->llavajp==0.1) (2.1.1)\n",
      "Requirement already satisfied: shellingham>=1.3.0 in /tmp/own/conda/envs/Dev/lib/python3.10/site-packages (from typer<1.0,>=0.12->gradio->llavajp==0.1) (1.5.4)\n",
      "Requirement already satisfied: rich>=10.11.0 in /tmp/own/conda/envs/Dev/lib/python3.10/site-packages (from typer<1.0,>=0.12->gradio->llavajp==0.1) (13.7.1)\n",
      "Requirement already satisfied: starlette<0.38.0,>=0.37.2 in /tmp/own/conda/envs/Dev/lib/python3.10/site-packages (from fastapi->gradio->llavajp==0.1) (0.37.2)\n",
      "Requirement already satisfied: fastapi-cli>=0.0.2 in /tmp/own/conda/envs/Dev/lib/python3.10/site-packages (from fastapi->gradio->llavajp==0.1) (0.0.4)\n",
      "Requirement already satisfied: email_validator>=2.0.0 in /tmp/own/conda/envs/Dev/lib/python3.10/site-packages (from fastapi->gradio->llavajp==0.1) (2.2.0)\n",
      "Requirement already satisfied: mpmath>=0.19 in /tmp/own/conda/envs/Dev/lib/python3.10/site-packages (from sympy->torch->llavajp==0.1) (1.3.0)\n",
      "Requirement already satisfied: dnspython>=2.0.0 in /tmp/own/conda/envs/Dev/lib/python3.10/site-packages (from email_validator>=2.0.0->fastapi->gradio->llavajp==0.1) (2.6.1)\n",
      "Requirement already satisfied: smmap<6,>=3.0.1 in /tmp/own/conda/envs/Dev/lib/python3.10/site-packages (from gitdb<5,>=4.0.1->gitpython!=3.1.29,>=1.0.0->wandb->llavajp==0.1) (5.0.1)\n",
      "Requirement already satisfied: markdown-it-py>=2.2.0 in /tmp/own/conda/envs/Dev/lib/python3.10/site-packages (from rich>=10.11.0->typer<1.0,>=0.12->gradio->llavajp==0.1) (3.0.0)\n",
      "Requirement already satisfied: pygments<3.0.0,>=2.13.0 in /tmp/own/conda/envs/Dev/lib/python3.10/site-packages (from rich>=10.11.0->typer<1.0,>=0.12->gradio->llavajp==0.1) (2.18.0)\n",
      "Requirement already satisfied: httptools>=0.5.0 in /tmp/own/conda/envs/Dev/lib/python3.10/site-packages (from uvicorn[standard]>=0.12.0->fastapi->gradio->llavajp==0.1) (0.6.1)\n",
      "Requirement already satisfied: python-dotenv>=0.13 in /tmp/own/conda/envs/Dev/lib/python3.10/site-packages (from uvicorn[standard]>=0.12.0->fastapi->gradio->llavajp==0.1) (1.0.1)\n",
      "Requirement already satisfied: uvloop!=0.15.0,!=0.15.1,>=0.14.0 in /tmp/own/conda/envs/Dev/lib/python3.10/site-packages (from uvicorn[standard]>=0.12.0->fastapi->gradio->llavajp==0.1) (0.19.0)\n",
      "Requirement already satisfied: watchfiles>=0.13 in /tmp/own/conda/envs/Dev/lib/python3.10/site-packages (from uvicorn[standard]>=0.12.0->fastapi->gradio->llavajp==0.1) (0.22.0)\n",
      "Requirement already satisfied: mdurl~=0.1 in /tmp/own/conda/envs/Dev/lib/python3.10/site-packages (from markdown-it-py>=2.2.0->rich>=10.11.0->typer<1.0,>=0.12->gradio->llavajp==0.1) (0.1.2)\n",
      "Building wheels for collected packages: llavajp\n",
      "  Building wheel for llavajp (pyproject.toml) ... \u001b[?25ldone\n",
      "\u001b[?25h  Created wheel for llavajp: filename=llavajp-0.1-py3-none-any.whl size=56504 sha256=fd596143fac0a900306e779624d52ac4b46df1537ade3abcb003834c0a6b18e2\n",
      "  Stored in directory: /tmp/pip-ephem-wheel-cache-bcfv6gk0/wheels/f7/d3/54/b69381f665d000d910b1db76bf4e00ea3c928fe8f4e78c18e8\n",
      "Successfully built llavajp\n",
      "Installing collected packages: llavajp\n",
      "Successfully installed llavajp-0.1\n"
     ]
    }
   ],
   "source": [
    "!pip install git+https://github.com/hibikaze-git/LLaVA-JP@feature/tanuki-moe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "htyCsnCyTwOE",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 113,
     "referenced_widgets": [
      "519f085fa5be43118df44d267b5cab9d",
      "ac62ce63e1ab4d15b640c4976df34801",
      "78da69bff39a4520b15c254466fbe8d8",
      "8bee439ad1f746d49ee80eaca31a0c9c",
      "05bc32f63a0f411a8ef39cc64c860908",
      "2db75a49a5fd4064927a272548a43132",
      "9bfa61f017754173b603a6ada7fda84f",
      "4d06be51ecee42aba662bf321cff7f46",
      "e01fde47f6a7485e87e7da717d245ba6",
      "6c8b866f64964b699f392f3e9b864777",
      "76549284aceb40cda16b7c80d3b3eb51",
      "16c8159aa8dd49bab3854278f1160bb6",
      "cf9ac11864984bcbb7be3ade707dcfc9",
      "3ebfdbd7f5164e9895bcfe69121aadbb",
      "00250f0ad0d44e8a982fc11a7da78697",
      "ff80a1b1c5f24812832285a15cfc7c3a",
      "2c645ed2b60348fa95ed0b109efae66d",
      "7644216ac17748ba98b81d85c4b9ec6b",
      "4b14c26a068e414bb889f056b4547e8e",
      "fdc104a103c5433ab5748e37e3bc386f",
      "d39062e059884a7da765a150c50978c0",
      "fa672bb34ad8439d845449f5df64ecc0",
      "b9e85752def14af48fc01f6e3522e26f",
      "3fcfc05fc84340f09e3049cfe70e05b0",
      "367804f8dd434dedb86cc6b6038ecfa4",
      "5d9cf27a11a24ff4a30730271495defb",
      "e19b9ca8a019453291d84640d66f8bb1",
      "209bc5cba68a47adb43a822048c2b575",
      "3098ad983889422ca1372785089bbcd6"
     ]
    },
    "executionInfo": {
     "elapsed": 1298,
     "status": "ok",
     "timestamp": 1721459521279,
     "user": {
      "displayName": "hiro",
      "userId": "15725092376508535253"
     },
     "user_tz": -540
    },
    "id": "htyCsnCyTwOE",
    "outputId": "f3840d50-bf16-4842-cb7e-af5b9ed5321e"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "34190b3194024415b2794c27b8939801",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox(children=(HTML(value='<center> <img\\nsrc=https://huggingface.co/front/assets/huggingface_logo-noborder.sv…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from huggingface_hub import notebook_login\n",
    "# write権限のトークンを入力\n",
    "notebook_login()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "PeHGdOvX8nYb",
   "metadata": {
    "executionInfo": {
     "elapsed": 13221,
     "status": "ok",
     "timestamp": 1721459505640,
     "user": {
      "displayName": "hiro",
      "userId": "15725092376508535253"
     },
     "user_tz": -540
    },
    "id": "PeHGdOvX8nYb"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-08-04 15:41:58.284052: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:485] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n",
      "2024-08-04 15:41:58.295633: E external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:8454] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n",
      "2024-08-04 15:41:58.299092: E external/local_xla/xla/stream_executor/cuda/cuda_blas.cc:1452] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n",
      "2024-08-04 15:41:58.307673: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n",
      "To enable the following instructions: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
      "2024-08-04 15:41:58.982226: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\n"
     ]
    }
   ],
   "source": [
    "import gradio as gr\n",
    "import torch\n",
    "import transformers\n",
    "from transformers import BitsAndBytesConfig\n",
    "from llavajp.constants import DEFAULT_IMAGE_TOKEN, IMAGE_TOKEN_INDEX\n",
    "from llavajp.conversation import conv_templates\n",
    "from llavajp.model.llava_llama import LlavaLlamaForCausalLM\n",
    "from llavajp.train.dataset import tokenizer_image_token"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "8sFPZCIU8qYD",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000,
     "referenced_widgets": [
      "c59745a9c9934bdfafb36acdf0b76d6f",
      "9bf765dea2bf4e148d5888ba3ecbc532",
      "3ae8fb07b61442fb955a0cd04c5d9d71",
      "a31b383a10e44033844919b4f763b003",
      "92818ef1790e46fd92fd3ba8bdca2928",
      "88e702f408a94290aeb9a77f863aedc1",
      "ffef39ba35064df99d13daa1d8b781aa",
      "ce567f04446e4948b592f0e0f6674b3f",
      "04685c3d17784ad192c68a77e2d021fd",
      "f05e7d4f70684ea08f488713660fe8ff",
      "f58199d4cb9b4dc4938016aef61f90ab",
      "184daa6e2ddf404ead8af322d6ed152e",
      "e4e749dcf87040d8a29df01646d9a0c3",
      "c7c878d9fb6944d38fe088e2173cfbd4",
      "0f9e555a74e1422593d6e98b42224105",
      "bee0695fe4284becb27825cd0b482e9e",
      "d780a5b8295948dd94277024b94450c7",
      "ca52f779392e4a0db07b0b078b43af50",
      "888642c2e99648b3b3efc625222bb9e1",
      "f957398b44584ab8afe09e44bf6b5794",
      "663cae26acf5456c8010b5c0fc2c48f0",
      "8624f15b48074e91b843a3119eac2d30",
      "79701eb82b804678b28223a5f7c0822c",
      "3ac6f376a8c74bf9bdf33a5f43180d3f",
      "086d04ae7f5b4f689bf030b4a37eb4cc",
      "886d393760754776844014afd6a08398",
      "16b99d6509654fa1863501d74d5a5d85",
      "7d409a913eb541f99a26e2340083c699",
      "9063edd9bed442298a906e9d7637fad0",
      "f44332a81d62435481df9cb278c5c4e8",
      "ef648c232d4e4731ae1f1219c306c896",
      "d412a30e27234e3ebef17717f6f41707",
      "af96a3754cc74989ac66080499851f15",
      "b25249b8806b499b9681eacb8659f985",
      "60be614acfc14419abf5fd28a092a25b",
      "a5d939193bcc4b2ab9c091d6028eb1f7",
      "71f921d8b9f14f1aaf9e5acac801dcc7",
      "b0b7f84194734e37bf1c7aad1c260a10",
      "a9e0055d729145f9a96d6625db048c7c",
      "1c475305d6724da9b388377b49544592",
      "b722ffae626346ba8474957773ba56e5",
      "843f288ed695452eb22f9ab838ac8af6",
      "d0948d82b60d4258b7646337ac537b22",
      "9e678595580e4ef58078ffdca08a6325",
      "c8625f177f7348d4b604270535895ff6",
      "3333ea6679a84bb18d8e2e06ed829d1e",
      "ff2f24473f074dc5835b73a9eb60b543",
      "84e07752fa684ae5bdc651f3240e56cf",
      "ffa26635efac424cb74fe6ae71a35f80",
      "519544d96b084ae28f8619c28e2997a5",
      "ac45cf4d8a024756b81124d087f6c23c",
      "b2b7a74220714c13bf7cedcef663b171",
      "b0beadcec05744cca50ff17f9b864c3c",
      "624124c2b7c54ad8963245fc358829d1",
      "5b0f0f27a1d74db0b279d6da5d9bcb4e",
      "b172dadeb50b45608c1e43928336c3b4",
      "6753f4b11d49482cad94304136c3c314",
      "94a7f89dc99c4d8487a0b7e3e4314c06",
      "0bb9cb28c4d044a18f1e22afab4b6d7c",
      "850a4df88a3c45aa930ffcdb3906f394",
      "c914ce2927ce426786a0ff0cd1545215",
      "edee44fd2a324f60b42fcc42940f1196",
      "657dfd152dfd4f0dab2cf93f4a8bc849",
      "386a30cb9743499b85bdf0be592a7b0b",
      "379a027ca58442309c3f2d00c6ac2554",
      "b443f29aaeeb43f2b527fc79c03712bc",
      "84c9405fcb624c96ab215c4f9e39c3a9",
      "43caebb4fc8c4582b4db2d874eeafe5a",
      "a6ed3bdd1a604cb0ae188c59a71d73f7",
      "14311241232c455e9bc14405c0b86495",
      "e49ad9b2dbef4890a4525dfff3bf6961",
      "1a7cdeaef0f94571b49324a9148edb9d",
      "9e79a3b0d6d94fdb8e4146b31e84f1e3",
      "b4d1aa797f2445e1aba35dd1786db7f9",
      "dbb6d44f37a74edb9bb2b80e4b8af16a",
      "f92e2159a4344d368a09220d4151cf76",
      "75d7762287c94eb79a1777209c464106",
      "67ca07517f6e49d6a25fa5ac9a7c9544",
      "605cdd5e8c864cd4acdb8441d7c939da",
      "52f024a1567440d696026ea37680f21e",
      "940e6ac63a024220a8902c632972b926",
      "29229ba9e0a14c0d8c5f3e3de60be13a",
      "ed36240f86f5402cb630027e714f917b",
      "da8f2de0427d408cae355f2b35dd889b",
      "5dce4921a0ad4f6fa9da4a9caa9268ee",
      "8c114c3352384d49b182f3baaa57d51f",
      "cfea82b2647c46a9866f74cee7487a4d",
      "da5830bc484f40bd8f54ddb282e4554d",
      "f68988b27c7241abaebde105c35e9659",
      "beb43a085da345f1ac2d6bec7eb690b7",
      "e3d73b54071e400f8b2af56557fd2d76",
      "81df48ada913475e97ab13c6c81ef1fa",
      "e0927527d6da40a895ee96d7d357ad56",
      "7a17ce4a81924dd9a8237916ad44962f",
      "855dd916702847338ed071fe873846b2",
      "62dd346f895147429e54eb6cfe20b703",
      "16658978abf648338631fa8e6314b9a6",
      "012654fc0cce414f91c337c2445621c7",
      "1d31e4edcef041a68bef6ad037e07ef4",
      "8984e5e319ab4fee98d839121c4f6937",
      "f4f5257a4ac540a9bc74d2814f0d38af",
      "622564f91070479793cc53ddaeccf3b0",
      "e3c53ac8e7d343ea87759a40e6c4f86a",
      "217a78b4c1a9477197b3c529f19cb16b",
      "e91a09281d3d48cca34f260173da2d16",
      "8bbc41932a6e4ef2b5933a612d6c93d6",
      "4142dd2913b64c25b07e46b74e14efe2",
      "51a4567d16a0474485122159708ee34d",
      "fa3dbe5d8a9a49fcb2e4aee00453a61a",
      "0b74af83c3904abd9ef8848fb2983caf",
      "07af4367fea6465e9e1067f58bec2399",
      "f7d6ef365bbc4e479b7086dc3867f308",
      "a1f73f0c8bf44fdfad45e0222ef24a75",
      "123b16636c5d4e3390aa5eac47b2cdc9",
      "4587adc475f3445e890158eadc1432eb",
      "986c17897b0c446184fd9c44af5919c3",
      "8e7896f4d12c45159cf713a3a8682570",
      "d71260daa1114bfbbd04e9cc232dcf53",
      "9efd2bb0e5974bb5bc2b57e1c5cc1b42",
      "ccd3e7c23b0d45f8bf6faee185588e6a",
      "6ecfbfb69098454a99fd90aed8442963",
      "5b180a8083fe43d99e29176e859000b0",
      "9ccb33cf5cd048b79cee655323667cee",
      "7e95e2838b1441fcbfa12d10b0d156cb",
      "c54f0d93b2da449eab0bf9586d2f6257",
      "a2f5ed62f0ef454d83352660cb442af2",
      "5e7d3b25cdc14dada55b137140532e0d",
      "6d6503eec6a647dfb83deb916bf90076",
      "995890d9adbc4b5f99b477fd20ecd062",
      "ca0c772bb8b84b06aa7e0de01f1755b8",
      "8b972f13e66f4e74aad6ef81347d9adc",
      "2917c19ada3843cda23399ea6ec55ba7",
      "f4f68fbd0758458c8bf5a70fa2225ac2",
      "da5556423b704b938ae8e305b08daee6",
      "394ffb8ac93e4e34a495872df474fe93",
      "dc5501aa47a5423c92827bdf4e0d2c63",
      "e814a1aef5614ab5ba6c1d560e99969a",
      "24ed4fe143e24d6091304da92ad02193",
      "ee855a532280410eb998a8c446e761fa",
      "4930f889870b462687cf7f7d7092f7d0",
      "525225ff4e264c809fdec57c77d30e7e",
      "1ca53c44536946c29161ff07982a056f",
      "e0854a2ebd70436f90042da60172c422",
      "45ba28221666408e8ffe08cd75807ae5",
      "ff10e097306c472ca17a5b319425182d",
      "495dd14b19c4453ca90ccb3f027b64a5",
      "0aa617b9eb2243c1b7ff9464558f30a2",
      "d6f7f2257acc44bf854bf20b44c7596f",
      "8e26a4c0644c416184af2407f00dea81",
      "18fd651d8b22414d81d9b0dd9cfcb981",
      "9654c9a38fc1411eb5fb665e9044b930",
      "85cc249f9d33418eb652df1abd9fccc1",
      "aeaf0785fb0a4fbf83e274ed20a9dc42",
      "e5da6dd6937b49b59081e31b63466ac1",
      "79dd6b13d77e40e89a4925384213cb1a",
      "3930f6eb84614b0a800366ba87c0d3ff",
      "986e8cd55d4d42b89ff35df40550bd41",
      "1d3ac111550c4e1c8bb1af0a9aa02819",
      "d27b7d98259d48aaa2103fd95b47de6b",
      "eb5f03fa32514498b66edda1b5b8c0be",
      "df347a18ec3f4b5098f395a522391837",
      "e41b5070db864d259f9c0cd3f14742e0",
      "b4519db0d32a4ed180e12283b8ed94f0",
      "9cfe391a7bd44f1cae49d11d9a67c619",
      "6adb2cb7317a4152aa0dc3703fab3a57"
     ]
    },
    "executionInfo": {
     "elapsed": 1372903,
     "status": "ok",
     "timestamp": 1721460904810,
     "user": {
      "displayName": "hiro",
      "userId": "15725092376508535253"
     },
     "user_tz": -540
    },
    "id": "8sFPZCIU8qYD",
    "outputId": "8bb70fbc-42a8-4688-a54d-931b086cbc22"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "3a8f26b236fa4ebab10be8041ba80002",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Downloading shards:   0%|          | 0/4 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "f0c439d4ce4f4d828680894aeb2148ac",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "model-00001-of-00004.safetensors:  34%|###4      | 1.70G/4.98G [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "705f7eafe03f45729877019800f8c157",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "model-00002-of-00004.safetensors:   0%|          | 0.00/4.92G [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "acbe90ed928c4fa98b58aacb120b5858",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "model-00003-of-00004.safetensors:   0%|          | 0.00/5.00G [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "f5a1f777fef04a73a18c26d91c56537b",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "model-00004-of-00004.safetensors:   0%|          | 0.00/1.04G [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "5969d5a39bca49749e0d34b0b7922010",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "config.json:   0%|          | 0.00/576 [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "4a92b01bef7b41aca449c9cc05884668",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "preprocessor_config.json:   0%|          | 0.00/368 [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "54f09eb3721e4043b82a02f4994d40b4",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "model.safetensors:   0%|          | 0.00/3.51G [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.embeddings.patch_embedding.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.embeddings.patch_embedding.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.embeddings.position_embedding.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.0.self_attn.k_proj.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.0.self_attn.k_proj.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.0.self_attn.v_proj.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.0.self_attn.v_proj.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.0.self_attn.q_proj.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.0.self_attn.q_proj.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.0.self_attn.out_proj.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.0.self_attn.out_proj.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.0.layer_norm1.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.0.layer_norm1.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.0.mlp.fc1.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.0.mlp.fc1.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.0.mlp.fc2.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.0.mlp.fc2.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.0.layer_norm2.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.0.layer_norm2.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.1.self_attn.k_proj.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.1.self_attn.k_proj.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.1.self_attn.v_proj.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.1.self_attn.v_proj.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.1.self_attn.q_proj.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.1.self_attn.q_proj.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.1.self_attn.out_proj.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.1.self_attn.out_proj.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.1.layer_norm1.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.1.layer_norm1.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.1.mlp.fc1.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.1.mlp.fc1.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.1.mlp.fc2.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.1.mlp.fc2.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.1.layer_norm2.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.1.layer_norm2.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.2.self_attn.k_proj.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.2.self_attn.k_proj.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.2.self_attn.v_proj.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.2.self_attn.v_proj.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.2.self_attn.q_proj.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.2.self_attn.q_proj.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.2.self_attn.out_proj.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.2.self_attn.out_proj.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.2.layer_norm1.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.2.layer_norm1.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.2.mlp.fc1.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.2.mlp.fc1.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.2.mlp.fc2.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.2.mlp.fc2.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.2.layer_norm2.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.2.layer_norm2.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.3.self_attn.k_proj.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.3.self_attn.k_proj.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.3.self_attn.v_proj.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.3.self_attn.v_proj.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.3.self_attn.q_proj.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.3.self_attn.q_proj.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.3.self_attn.out_proj.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.3.self_attn.out_proj.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.3.layer_norm1.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.3.layer_norm1.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.3.mlp.fc1.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.3.mlp.fc1.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.3.mlp.fc2.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.3.mlp.fc2.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.3.layer_norm2.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.3.layer_norm2.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.4.self_attn.k_proj.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.4.self_attn.k_proj.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.4.self_attn.v_proj.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.4.self_attn.v_proj.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.4.self_attn.q_proj.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.4.self_attn.q_proj.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.4.self_attn.out_proj.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.4.self_attn.out_proj.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.4.layer_norm1.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.4.layer_norm1.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.4.mlp.fc1.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.4.mlp.fc1.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.4.mlp.fc2.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.4.mlp.fc2.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.4.layer_norm2.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.4.layer_norm2.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.5.self_attn.k_proj.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.5.self_attn.k_proj.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.5.self_attn.v_proj.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.5.self_attn.v_proj.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.5.self_attn.q_proj.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.5.self_attn.q_proj.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.5.self_attn.out_proj.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.5.self_attn.out_proj.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.5.layer_norm1.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.5.layer_norm1.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.5.mlp.fc1.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.5.mlp.fc1.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.5.mlp.fc2.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.5.mlp.fc2.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.5.layer_norm2.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.5.layer_norm2.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.6.self_attn.k_proj.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.6.self_attn.k_proj.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.6.self_attn.v_proj.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.6.self_attn.v_proj.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.6.self_attn.q_proj.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.6.self_attn.q_proj.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.6.self_attn.out_proj.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.6.self_attn.out_proj.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.6.layer_norm1.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.6.layer_norm1.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.6.mlp.fc1.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.6.mlp.fc1.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.6.mlp.fc2.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.6.mlp.fc2.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.6.layer_norm2.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.6.layer_norm2.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.7.self_attn.k_proj.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.7.self_attn.k_proj.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.7.self_attn.v_proj.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.7.self_attn.v_proj.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.7.self_attn.q_proj.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.7.self_attn.q_proj.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.7.self_attn.out_proj.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.7.self_attn.out_proj.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.7.layer_norm1.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.7.layer_norm1.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.7.mlp.fc1.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.7.mlp.fc1.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.7.mlp.fc2.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.7.mlp.fc2.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.7.layer_norm2.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.7.layer_norm2.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.8.self_attn.k_proj.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.8.self_attn.k_proj.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.8.self_attn.v_proj.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.8.self_attn.v_proj.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.8.self_attn.q_proj.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.8.self_attn.q_proj.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.8.self_attn.out_proj.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.8.self_attn.out_proj.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.8.layer_norm1.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.8.layer_norm1.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.8.mlp.fc1.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.8.mlp.fc1.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.8.mlp.fc2.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.8.mlp.fc2.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.8.layer_norm2.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.8.layer_norm2.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.9.self_attn.k_proj.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.9.self_attn.k_proj.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.9.self_attn.v_proj.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.9.self_attn.v_proj.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.9.self_attn.q_proj.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.9.self_attn.q_proj.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.9.self_attn.out_proj.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.9.self_attn.out_proj.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.9.layer_norm1.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.9.layer_norm1.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.9.mlp.fc1.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.9.mlp.fc1.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.9.mlp.fc2.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.9.mlp.fc2.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.9.layer_norm2.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.9.layer_norm2.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.10.self_attn.k_proj.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.10.self_attn.k_proj.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.10.self_attn.v_proj.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.10.self_attn.v_proj.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.10.self_attn.q_proj.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.10.self_attn.q_proj.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.10.self_attn.out_proj.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.10.self_attn.out_proj.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.10.layer_norm1.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.10.layer_norm1.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.10.mlp.fc1.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.10.mlp.fc1.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.10.mlp.fc2.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.10.mlp.fc2.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.10.layer_norm2.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.10.layer_norm2.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.11.self_attn.k_proj.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.11.self_attn.k_proj.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.11.self_attn.v_proj.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.11.self_attn.v_proj.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.11.self_attn.q_proj.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.11.self_attn.q_proj.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.11.self_attn.out_proj.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.11.self_attn.out_proj.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.11.layer_norm1.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.11.layer_norm1.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.11.mlp.fc1.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.11.mlp.fc1.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.11.mlp.fc2.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.11.mlp.fc2.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.11.layer_norm2.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.11.layer_norm2.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.12.self_attn.k_proj.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.12.self_attn.k_proj.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.12.self_attn.v_proj.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.12.self_attn.v_proj.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.12.self_attn.q_proj.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.12.self_attn.q_proj.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.12.self_attn.out_proj.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.12.self_attn.out_proj.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.12.layer_norm1.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.12.layer_norm1.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.12.mlp.fc1.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.12.mlp.fc1.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.12.mlp.fc2.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.12.mlp.fc2.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.12.layer_norm2.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.12.layer_norm2.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.13.self_attn.k_proj.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.13.self_attn.k_proj.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.13.self_attn.v_proj.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.13.self_attn.v_proj.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.13.self_attn.q_proj.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.13.self_attn.q_proj.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.13.self_attn.out_proj.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.13.self_attn.out_proj.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.13.layer_norm1.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.13.layer_norm1.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.13.mlp.fc1.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.13.mlp.fc1.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.13.mlp.fc2.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.13.mlp.fc2.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.13.layer_norm2.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.13.layer_norm2.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.14.self_attn.k_proj.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.14.self_attn.k_proj.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.14.self_attn.v_proj.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.14.self_attn.v_proj.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.14.self_attn.q_proj.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.14.self_attn.q_proj.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.14.self_attn.out_proj.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.14.self_attn.out_proj.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.14.layer_norm1.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.14.layer_norm1.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.14.mlp.fc1.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.14.mlp.fc1.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.14.mlp.fc2.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.14.mlp.fc2.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.14.layer_norm2.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.14.layer_norm2.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.15.self_attn.k_proj.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.15.self_attn.k_proj.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.15.self_attn.v_proj.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.15.self_attn.v_proj.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.15.self_attn.q_proj.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.15.self_attn.q_proj.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.15.self_attn.out_proj.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.15.self_attn.out_proj.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.15.layer_norm1.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.15.layer_norm1.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.15.mlp.fc1.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.15.mlp.fc1.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.15.mlp.fc2.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.15.mlp.fc2.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.15.layer_norm2.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.15.layer_norm2.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.16.self_attn.k_proj.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.16.self_attn.k_proj.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.16.self_attn.v_proj.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.16.self_attn.v_proj.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.16.self_attn.q_proj.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.16.self_attn.q_proj.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.16.self_attn.out_proj.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.16.self_attn.out_proj.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.16.layer_norm1.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.16.layer_norm1.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.16.mlp.fc1.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.16.mlp.fc1.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.16.mlp.fc2.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.16.mlp.fc2.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.16.layer_norm2.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.16.layer_norm2.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.17.self_attn.k_proj.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.17.self_attn.k_proj.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.17.self_attn.v_proj.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.17.self_attn.v_proj.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.17.self_attn.q_proj.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.17.self_attn.q_proj.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.17.self_attn.out_proj.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.17.self_attn.out_proj.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.17.layer_norm1.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.17.layer_norm1.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.17.mlp.fc1.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.17.mlp.fc1.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.17.mlp.fc2.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.17.mlp.fc2.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.17.layer_norm2.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.17.layer_norm2.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.18.self_attn.k_proj.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.18.self_attn.k_proj.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.18.self_attn.v_proj.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.18.self_attn.v_proj.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.18.self_attn.q_proj.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.18.self_attn.q_proj.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.18.self_attn.out_proj.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.18.self_attn.out_proj.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.18.layer_norm1.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.18.layer_norm1.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.18.mlp.fc1.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.18.mlp.fc1.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.18.mlp.fc2.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.18.mlp.fc2.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.18.layer_norm2.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.18.layer_norm2.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.19.self_attn.k_proj.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.19.self_attn.k_proj.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.19.self_attn.v_proj.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.19.self_attn.v_proj.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.19.self_attn.q_proj.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.19.self_attn.q_proj.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.19.self_attn.out_proj.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.19.self_attn.out_proj.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.19.layer_norm1.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.19.layer_norm1.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.19.mlp.fc1.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.19.mlp.fc1.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.19.mlp.fc2.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.19.mlp.fc2.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.19.layer_norm2.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.19.layer_norm2.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.20.self_attn.k_proj.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.20.self_attn.k_proj.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.20.self_attn.v_proj.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.20.self_attn.v_proj.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.20.self_attn.q_proj.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.20.self_attn.q_proj.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.20.self_attn.out_proj.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.20.self_attn.out_proj.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.20.layer_norm1.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.20.layer_norm1.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.20.mlp.fc1.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.20.mlp.fc1.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.20.mlp.fc2.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.20.mlp.fc2.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.20.layer_norm2.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.20.layer_norm2.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.21.self_attn.k_proj.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.21.self_attn.k_proj.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.21.self_attn.v_proj.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.21.self_attn.v_proj.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.21.self_attn.q_proj.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.21.self_attn.q_proj.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.21.self_attn.out_proj.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.21.self_attn.out_proj.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.21.layer_norm1.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.21.layer_norm1.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.21.mlp.fc1.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.21.mlp.fc1.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.21.mlp.fc2.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.21.mlp.fc2.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.21.layer_norm2.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.21.layer_norm2.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.22.self_attn.k_proj.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.22.self_attn.k_proj.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.22.self_attn.v_proj.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.22.self_attn.v_proj.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.22.self_attn.q_proj.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.22.self_attn.q_proj.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.22.self_attn.out_proj.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.22.self_attn.out_proj.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.22.layer_norm1.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.22.layer_norm1.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.22.mlp.fc1.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.22.mlp.fc1.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.22.mlp.fc2.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.22.mlp.fc2.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.22.layer_norm2.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.22.layer_norm2.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.23.self_attn.k_proj.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.23.self_attn.k_proj.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.23.self_attn.v_proj.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.23.self_attn.v_proj.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.23.self_attn.q_proj.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.23.self_attn.q_proj.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.23.self_attn.out_proj.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.23.self_attn.out_proj.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.23.layer_norm1.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.23.layer_norm1.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.23.mlp.fc1.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.23.mlp.fc1.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.23.mlp.fc2.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.23.mlp.fc2.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.23.layer_norm2.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.23.layer_norm2.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.24.self_attn.k_proj.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.24.self_attn.k_proj.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.24.self_attn.v_proj.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.24.self_attn.v_proj.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.24.self_attn.q_proj.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.24.self_attn.q_proj.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.24.self_attn.out_proj.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.24.self_attn.out_proj.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.24.layer_norm1.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.24.layer_norm1.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.24.mlp.fc1.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.24.mlp.fc1.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.24.mlp.fc2.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.24.mlp.fc2.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.24.layer_norm2.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.24.layer_norm2.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.25.self_attn.k_proj.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.25.self_attn.k_proj.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.25.self_attn.v_proj.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.25.self_attn.v_proj.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.25.self_attn.q_proj.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.25.self_attn.q_proj.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.25.self_attn.out_proj.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.25.self_attn.out_proj.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.25.layer_norm1.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.25.layer_norm1.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.25.mlp.fc1.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.25.mlp.fc1.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.25.mlp.fc2.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.25.mlp.fc2.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.25.layer_norm2.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.25.layer_norm2.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.26.self_attn.k_proj.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.26.self_attn.k_proj.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.26.self_attn.v_proj.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.26.self_attn.v_proj.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.26.self_attn.q_proj.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.26.self_attn.q_proj.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.26.self_attn.out_proj.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.26.self_attn.out_proj.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.26.layer_norm1.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.26.layer_norm1.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.26.mlp.fc1.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.26.mlp.fc1.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.26.mlp.fc2.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.26.mlp.fc2.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.26.layer_norm2.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.encoder.layers.26.layer_norm2.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.post_layernorm.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.post_layernorm.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.head.probe: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.head.attention.in_proj_weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.head.attention.in_proj_bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.head.attention.out_proj.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.head.attention.out_proj.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.head.layernorm.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.head.layernorm.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.head.mlp.fc1.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.head.mlp.fc1.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.head.mlp.fc2.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n",
      "/tmp/own/conda/envs/Dev/lib/python3.10/site-packages/torch/nn/modules/module.py:2025: UserWarning: for vision_model.head.mlp.fc2.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n",
      "  warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "6dc2e22892ef47b59fa745ebe7a89fcb",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Loading checkpoint shards:   0%|          | 0/4 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "99df5dcbd8964c4e8a9ffc35f26c5d87",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "generation_config.json:   0%|          | 0.00/162 [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "1984277dac4c41a7a7e0410d2062c21c",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "tokenizer_config.json:   0%|          | 0.00/2.48k [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "0640410276264899a1634a01dff5a642",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "tokenizer.json:   0%|          | 0.00/4.26M [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "b7ba2de46d4241e781f8c68d5a60dfe8",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "special_tokens_map.json:   0%|          | 0.00/904 [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model_path = \"team-hatakeyama-phase2/Tanuki-8B-vision-v4-checkpoint-26000\"\n",
    "\n",
    "# load model\n",
    "device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n",
    "torch_dtype = torch.bfloat16 if device == \"cuda\" else torch.float32\n",
    "\n",
    "bnb_model_from_pretrained_args = {}\n",
    "bnb_model_from_pretrained_args.update(dict(\n",
    "    device_map=\"auto\",\n",
    "    quantization_config=BitsAndBytesConfig(\n",
    "        load_in_8bit=True,\n",
    "        llm_int8_skip_modules=[\"mm_projector\", \"vision_tower\"],\n",
    "        llm_int8_threshold=6.0,\n",
    "        llm_int8_has_fp16_weight=False,\n",
    "    )\n",
    "))\n",
    "\n",
    "model = LlavaLlamaForCausalLM.from_pretrained(\n",
    "    model_path,\n",
    "    low_cpu_mem_usage=True,\n",
    "    use_safetensors=True,\n",
    "    **bnb_model_from_pretrained_args\n",
    ")\n",
    "\n",
    "tokenizer = transformers.AutoTokenizer.from_pretrained(\n",
    "    model_path,\n",
    "    model_max_length=8192,\n",
    "    padding_side=\"right\",\n",
    "    use_fast=False,\n",
    ")\n",
    "model.eval()\n",
    "conv_mode = \"v1\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "tyXn65OU_qkI",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 625
    },
    "executionInfo": {
     "elapsed": 3376,
     "status": "ok",
     "timestamp": 1721460954300,
     "user": {
      "displayName": "hiro",
      "userId": "15725092376508535253"
     },
     "user_tz": -540
    },
    "id": "tyXn65OU_qkI",
    "outputId": "a849232d-4524-4162-ce22-9ecc6f5b3180"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running on local URL:  http://0.0.0.0:7860\n",
      "\n",
      "To create a public link, set `share=True` in `launch()`.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div><iframe src=\"http://localhost:7860/\" width=\"100%\" height=\"500\" allow=\"autoplay; camera; microphone; clipboard-read; clipboard-write;\" frameborder=\"0\" allowfullscreen></iframe></div>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "@torch.inference_mode()\n",
    "def inference_fn(\n",
    "    image,\n",
    "    prompt,\n",
    "    max_len,\n",
    "    temperature,\n",
    "    top_p,\n",
    "    no_repeat_ngram_size\n",
    "):\n",
    "    # prepare inputs\n",
    "    # image pre-process\n",
    "    image_size = model.get_model().vision_tower.image_processor.size[\"height\"]\n",
    "    if model.get_model().vision_tower.scales is not None:\n",
    "        image_size = model.get_model().vision_tower.image_processor.size[\n",
    "            \"height\"\n",
    "        ] * len(model.get_model().vision_tower.scales)\n",
    "\n",
    "    if device == \"cuda\":\n",
    "        image_tensor = (\n",
    "            model.get_model()\n",
    "            .vision_tower.image_processor(\n",
    "                image,\n",
    "                return_tensors=\"pt\",\n",
    "                size={\"height\": image_size, \"width\": image_size},\n",
    "            )[\"pixel_values\"]\n",
    "            .half()\n",
    "            .cuda()\n",
    "            .to(torch_dtype)\n",
    "        )\n",
    "    else:\n",
    "        image_tensor = (\n",
    "            model.get_model()\n",
    "            .vision_tower.image_processor(\n",
    "                image,\n",
    "                return_tensors=\"pt\",\n",
    "                size={\"height\": image_size, \"width\": image_size},\n",
    "            )[\"pixel_values\"]\n",
    "            .to(torch_dtype)\n",
    "        )\n",
    "\n",
    "    # create prompt\n",
    "    inp = DEFAULT_IMAGE_TOKEN + \"\\n\" + prompt\n",
    "    conv = conv_templates[conv_mode].copy()\n",
    "    conv.append_message(conv.roles[0], inp)\n",
    "    conv.append_message(conv.roles[1], None)\n",
    "    prompt = conv.get_prompt()\n",
    "\n",
    "    input_ids = tokenizer_image_token(\n",
    "        prompt, tokenizer, IMAGE_TOKEN_INDEX, return_tensors=\"pt\"\n",
    "    ).unsqueeze(0)\n",
    "    if device == \"cuda\":\n",
    "        input_ids = input_ids.to(device)\n",
    "\n",
    "    input_ids = input_ids[:, :-1]  # </sep>がinputの最後に入るので削除する\n",
    "\n",
    "    # generate\n",
    "    output_ids = model.generate(\n",
    "        inputs=input_ids,\n",
    "        images=image_tensor,\n",
    "        do_sample=temperature != 0.0,\n",
    "        temperature=temperature,\n",
    "        top_p=top_p,\n",
    "        max_new_tokens=max_len,\n",
    "        repetition_penalty=1.0,\n",
    "        use_cache=False,\n",
    "        no_repeat_ngram_size=no_repeat_ngram_size\n",
    "    )\n",
    "\n",
    "    output_ids = [\n",
    "        token_id for token_id in output_ids.tolist()[0] if token_id != IMAGE_TOKEN_INDEX\n",
    "    ]\n",
    "\n",
    "    print(output_ids)\n",
    "    output = tokenizer.decode(output_ids, skip_special_tokens=True)\n",
    "\n",
    "    print(output)\n",
    "\n",
    "    target = \"システム: \"\n",
    "    idx = output.find(target)\n",
    "    output = output[idx + len(target) :]\n",
    "\n",
    "    return output\n",
    "\n",
    "\n",
    "with gr.Blocks() as demo:\n",
    "    gr.Markdown(\"# LLaVA-JP Demo\")\n",
    "\n",
    "    with gr.Row():\n",
    "        with gr.Column():\n",
    "            # input_instruction = gr.TextArea(label=\"instruction\", value=DEFAULT_INSTRUCTION)\n",
    "            input_image = gr.Image(type=\"pil\", label=\"image\")\n",
    "            prompt = gr.Textbox(label=\"prompt (optional)\", value=\"\")\n",
    "            with gr.Accordion(label=\"Configs\", open=False):\n",
    "                max_len = gr.Slider(\n",
    "                    minimum=10,\n",
    "                    maximum=256,\n",
    "                    value=200,\n",
    "                    step=5,\n",
    "                    interactive=True,\n",
    "                    label=\"Max New Tokens\",\n",
    "                )\n",
    "\n",
    "                temperature = gr.Slider(\n",
    "                    minimum=0.0,\n",
    "                    maximum=1.0,\n",
    "                    value=0.0,\n",
    "                    step=0.1,\n",
    "                    interactive=True,\n",
    "                    label=\"Temperature\",\n",
    "                )\n",
    "\n",
    "                top_p = gr.Slider(\n",
    "                    minimum=0.5,\n",
    "                    maximum=1.0,\n",
    "                    value=1.0,\n",
    "                    step=0.1,\n",
    "                    interactive=True,\n",
    "                    label=\"Top p\",\n",
    "                )\n",
    "\n",
    "                no_repeat_ngram_size = gr.Slider(\n",
    "                    minimum=0,\n",
    "                    maximum=4,\n",
    "                    value=3.0,\n",
    "                    step=1,\n",
    "                    interactive=True,\n",
    "                    label=\"No Repeat Ngram Size(1, 2にすると出力が狂います)\",\n",
    "                )\n",
    "            # button\n",
    "            input_button = gr.Button(value=\"Submit\")\n",
    "        with gr.Column():\n",
    "            output = gr.Textbox(label=\"Output\")\n",
    "\n",
    "    inputs = [input_image, prompt, max_len, temperature, top_p, no_repeat_ngram_size]\n",
    "    input_button.click(inference_fn, inputs=inputs, outputs=[output])\n",
    "    prompt.submit(inference_fn, inputs=inputs, outputs=[output])\n",
    "    img2txt_examples = gr.Examples(\n",
    "        examples=[\n",
    "            [\n",
    "                \"https://raw.githubusercontent.com/hibikaze-git/LLaVA-JP/feature/package/imgs/sample1.jpg\",\n",
    "                \"猫の隣には何がありますか？\",\n",
    "                128,\n",
    "                0.0,\n",
    "                1.0,\n",
    "                3.0\n",
    "            ],\n",
    "        ],\n",
    "        inputs=inputs,\n",
    "    )\n",
    "\n",
    "\n",
    "if __name__ == \"__main__\":\n",
    "    demo.queue().launch(server_name=\"0.0.0.0\")\n"
   ]
  }
 ],
 "metadata": {
  "accelerator": "GPU",
  "colab": {
   "gpuType": "T4",
   "provenance": [
    {
     "file_id": "130y9v18ey0xEQV4XNee9TDISWEkWXjVt",
     "timestamp": 1715308255628
    }
   ]
  },
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.14"
  },
  "widgets": {
   "application/vnd.jupyter.widget-state+json": {
    "00250f0ad0d44e8a982fc11a7da78697": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "ButtonStyleModel",
     "state": {
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "ButtonStyleModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "StyleView",
      "button_color": null,
      "font_weight": ""
     }
    },
    "012654fc0cce414f91c337c2445621c7": {
     "model_module": "@jupyter-widgets/base",
     "model_module_version": "1.2.0",
     "model_name": "LayoutModel",
     "state": {
      "_model_module": "@jupyter-widgets/base",
      "_model_module_version": "1.2.0",
      "_model_name": "LayoutModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "LayoutView",
      "align_content": null,
      "align_items": null,
      "align_self": null,
      "border": null,
      "bottom": null,
      "display": null,
      "flex": null,
      "flex_flow": null,
      "grid_area": null,
      "grid_auto_columns": null,
      "grid_auto_flow": null,
      "grid_auto_rows": null,
      "grid_column": null,
      "grid_gap": null,
      "grid_row": null,
      "grid_template_areas": null,
      "grid_template_columns": null,
      "grid_template_rows": null,
      "height": null,
      "justify_content": null,
      "justify_items": null,
      "left": null,
      "margin": null,
      "max_height": null,
      "max_width": null,
      "min_height": null,
      "min_width": null,
      "object_fit": null,
      "object_position": null,
      "order": null,
      "overflow": null,
      "overflow_x": null,
      "overflow_y": null,
      "padding": null,
      "right": null,
      "top": null,
      "visibility": null,
      "width": null
     }
    },
    "04685c3d17784ad192c68a77e2d021fd": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "ProgressStyleModel",
     "state": {
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "ProgressStyleModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "StyleView",
      "bar_color": null,
      "description_width": ""
     }
    },
    "05bc32f63a0f411a8ef39cc64c860908": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "ButtonModel",
     "state": {
      "_dom_classes": [],
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "ButtonModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/controls",
      "_view_module_version": "1.5.0",
      "_view_name": "ButtonView",
      "button_style": "",
      "description": "Login",
      "disabled": false,
      "icon": "",
      "layout": "IPY_MODEL_3ebfdbd7f5164e9895bcfe69121aadbb",
      "style": "IPY_MODEL_00250f0ad0d44e8a982fc11a7da78697",
      "tooltip": ""
     }
    },
    "07af4367fea6465e9e1067f58bec2399": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "HBoxModel",
     "state": {
      "_dom_classes": [],
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "HBoxModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/controls",
      "_view_module_version": "1.5.0",
      "_view_name": "HBoxView",
      "box_style": "",
      "children": [
       "IPY_MODEL_f7d6ef365bbc4e479b7086dc3867f308",
       "IPY_MODEL_a1f73f0c8bf44fdfad45e0222ef24a75",
       "IPY_MODEL_123b16636c5d4e3390aa5eac47b2cdc9"
      ],
      "layout": "IPY_MODEL_4587adc475f3445e890158eadc1432eb"
     }
    },
    "086d04ae7f5b4f689bf030b4a37eb4cc": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "FloatProgressModel",
     "state": {
      "_dom_classes": [],
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "FloatProgressModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/controls",
      "_view_module_version": "1.5.0",
      "_view_name": "ProgressView",
      "bar_style": "success",
      "description": "",
      "description_tooltip": null,
      "layout": "IPY_MODEL_f44332a81d62435481df9cb278c5c4e8",
      "max": 4,
      "min": 0,
      "orientation": "horizontal",
      "style": "IPY_MODEL_ef648c232d4e4731ae1f1219c306c896",
      "value": 4
     }
    },
    "0aa617b9eb2243c1b7ff9464558f30a2": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "HTMLModel",
     "state": {
      "_dom_classes": [],
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "HTMLModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/controls",
      "_view_module_version": "1.5.0",
      "_view_name": "HTMLView",
      "description": "",
      "description_tooltip": null,
      "layout": "IPY_MODEL_aeaf0785fb0a4fbf83e274ed20a9dc42",
      "placeholder": "​",
      "style": "IPY_MODEL_e5da6dd6937b49b59081e31b63466ac1",
      "value": " 4.26M/4.26M [00:00&lt;00:00, 42.1MB/s]"
     }
    },
    "0b74af83c3904abd9ef8848fb2983caf": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "DescriptionStyleModel",
     "state": {
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "DescriptionStyleModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "StyleView",
      "description_width": ""
     }
    },
    "0bb9cb28c4d044a18f1e22afab4b6d7c": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "HTMLModel",
     "state": {
      "_dom_classes": [],
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "HTMLModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/controls",
      "_view_module_version": "1.5.0",
      "_view_name": "HTMLView",
      "description": "",
      "description_tooltip": null,
      "layout": "IPY_MODEL_379a027ca58442309c3f2d00c6ac2554",
      "placeholder": "​",
      "style": "IPY_MODEL_b443f29aaeeb43f2b527fc79c03712bc",
      "value": " 5.00G/5.00G [11:58&lt;00:00, 10.7MB/s]"
     }
    },
    "0f9e555a74e1422593d6e98b42224105": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "HTMLModel",
     "state": {
      "_dom_classes": [],
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "HTMLModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/controls",
      "_view_module_version": "1.5.0",
      "_view_name": "HTMLView",
      "description": "",
      "description_tooltip": null,
      "layout": "IPY_MODEL_663cae26acf5456c8010b5c0fc2c48f0",
      "placeholder": "​",
      "style": "IPY_MODEL_8624f15b48074e91b843a3119eac2d30",
      "value": " 80.2k/80.2k [00:00&lt;00:00, 3.75MB/s]"
     }
    },
    "123b16636c5d4e3390aa5eac47b2cdc9": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "HTMLModel",
     "state": {
      "_dom_classes": [],
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "HTMLModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/controls",
      "_view_module_version": "1.5.0",
      "_view_name": "HTMLView",
      "description": "",
      "description_tooltip": null,
      "layout": "IPY_MODEL_ccd3e7c23b0d45f8bf6faee185588e6a",
      "placeholder": "​",
      "style": "IPY_MODEL_6ecfbfb69098454a99fd90aed8442963",
      "value": " 4/4 [01:17&lt;00:00, 16.77s/it]"
     }
    },
    "14311241232c455e9bc14405c0b86495": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "HTMLModel",
     "state": {
      "_dom_classes": [],
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "HTMLModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/controls",
      "_view_module_version": "1.5.0",
      "_view_name": "HTMLView",
      "description": "",
      "description_tooltip": null,
      "layout": "IPY_MODEL_f92e2159a4344d368a09220d4151cf76",
      "placeholder": "​",
      "style": "IPY_MODEL_75d7762287c94eb79a1777209c464106",
      "value": " 1.04G/1.04G [00:47&lt;00:00, 20.2MB/s]"
     }
    },
    "16658978abf648338631fa8e6314b9a6": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "ProgressStyleModel",
     "state": {
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "ProgressStyleModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "StyleView",
      "bar_color": null,
      "description_width": ""
     }
    },
    "16b99d6509654fa1863501d74d5a5d85": {
     "model_module": "@jupyter-widgets/base",
     "model_module_version": "1.2.0",
     "model_name": "LayoutModel",
     "state": {
      "_model_module": "@jupyter-widgets/base",
      "_model_module_version": "1.2.0",
      "_model_name": "LayoutModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "LayoutView",
      "align_content": null,
      "align_items": null,
      "align_self": null,
      "border": null,
      "bottom": null,
      "display": null,
      "flex": null,
      "flex_flow": null,
      "grid_area": null,
      "grid_auto_columns": null,
      "grid_auto_flow": null,
      "grid_auto_rows": null,
      "grid_column": null,
      "grid_gap": null,
      "grid_row": null,
      "grid_template_areas": null,
      "grid_template_columns": null,
      "grid_template_rows": null,
      "height": null,
      "justify_content": null,
      "justify_items": null,
      "left": null,
      "margin": null,
      "max_height": null,
      "max_width": null,
      "min_height": null,
      "min_width": null,
      "object_fit": null,
      "object_position": null,
      "order": null,
      "overflow": null,
      "overflow_x": null,
      "overflow_y": null,
      "padding": null,
      "right": null,
      "top": null,
      "visibility": null,
      "width": null
     }
    },
    "16c8159aa8dd49bab3854278f1160bb6": {
     "model_module": "@jupyter-widgets/base",
     "model_module_version": "1.2.0",
     "model_name": "LayoutModel",
     "state": {
      "_model_module": "@jupyter-widgets/base",
      "_model_module_version": "1.2.0",
      "_model_name": "LayoutModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "LayoutView",
      "align_content": null,
      "align_items": null,
      "align_self": null,
      "border": null,
      "bottom": null,
      "display": null,
      "flex": null,
      "flex_flow": null,
      "grid_area": null,
      "grid_auto_columns": null,
      "grid_auto_flow": null,
      "grid_auto_rows": null,
      "grid_column": null,
      "grid_gap": null,
      "grid_row": null,
      "grid_template_areas": null,
      "grid_template_columns": null,
      "grid_template_rows": null,
      "height": null,
      "justify_content": null,
      "justify_items": null,
      "left": null,
      "margin": null,
      "max_height": null,
      "max_width": null,
      "min_height": null,
      "min_width": null,
      "object_fit": null,
      "object_position": null,
      "order": null,
      "overflow": null,
      "overflow_x": null,
      "overflow_y": null,
      "padding": null,
      "right": null,
      "top": null,
      "visibility": null,
      "width": null
     }
    },
    "184daa6e2ddf404ead8af322d6ed152e": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "HBoxModel",
     "state": {
      "_dom_classes": [],
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "HBoxModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/controls",
      "_view_module_version": "1.5.0",
      "_view_name": "HBoxView",
      "box_style": "",
      "children": [
       "IPY_MODEL_e4e749dcf87040d8a29df01646d9a0c3",
       "IPY_MODEL_c7c878d9fb6944d38fe088e2173cfbd4",
       "IPY_MODEL_0f9e555a74e1422593d6e98b42224105"
      ],
      "layout": "IPY_MODEL_bee0695fe4284becb27825cd0b482e9e"
     }
    },
    "18fd651d8b22414d81d9b0dd9cfcb981": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "DescriptionStyleModel",
     "state": {
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "DescriptionStyleModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "StyleView",
      "description_width": ""
     }
    },
    "1a7cdeaef0f94571b49324a9148edb9d": {
     "model_module": "@jupyter-widgets/base",
     "model_module_version": "1.2.0",
     "model_name": "LayoutModel",
     "state": {
      "_model_module": "@jupyter-widgets/base",
      "_model_module_version": "1.2.0",
      "_model_name": "LayoutModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "LayoutView",
      "align_content": null,
      "align_items": null,
      "align_self": null,
      "border": null,
      "bottom": null,
      "display": null,
      "flex": null,
      "flex_flow": null,
      "grid_area": null,
      "grid_auto_columns": null,
      "grid_auto_flow": null,
      "grid_auto_rows": null,
      "grid_column": null,
      "grid_gap": null,
      "grid_row": null,
      "grid_template_areas": null,
      "grid_template_columns": null,
      "grid_template_rows": null,
      "height": null,
      "justify_content": null,
      "justify_items": null,
      "left": null,
      "margin": null,
      "max_height": null,
      "max_width": null,
      "min_height": null,
      "min_width": null,
      "object_fit": null,
      "object_position": null,
      "order": null,
      "overflow": null,
      "overflow_x": null,
      "overflow_y": null,
      "padding": null,
      "right": null,
      "top": null,
      "visibility": null,
      "width": null
     }
    },
    "1c475305d6724da9b388377b49544592": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "DescriptionStyleModel",
     "state": {
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "DescriptionStyleModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "StyleView",
      "description_width": ""
     }
    },
    "1ca53c44536946c29161ff07982a056f": {
     "model_module": "@jupyter-widgets/base",
     "model_module_version": "1.2.0",
     "model_name": "LayoutModel",
     "state": {
      "_model_module": "@jupyter-widgets/base",
      "_model_module_version": "1.2.0",
      "_model_name": "LayoutModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "LayoutView",
      "align_content": null,
      "align_items": null,
      "align_self": null,
      "border": null,
      "bottom": null,
      "display": null,
      "flex": null,
      "flex_flow": null,
      "grid_area": null,
      "grid_auto_columns": null,
      "grid_auto_flow": null,
      "grid_auto_rows": null,
      "grid_column": null,
      "grid_gap": null,
      "grid_row": null,
      "grid_template_areas": null,
      "grid_template_columns": null,
      "grid_template_rows": null,
      "height": null,
      "justify_content": null,
      "justify_items": null,
      "left": null,
      "margin": null,
      "max_height": null,
      "max_width": null,
      "min_height": null,
      "min_width": null,
      "object_fit": null,
      "object_position": null,
      "order": null,
      "overflow": null,
      "overflow_x": null,
      "overflow_y": null,
      "padding": null,
      "right": null,
      "top": null,
      "visibility": null,
      "width": null
     }
    },
    "1d31e4edcef041a68bef6ad037e07ef4": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "DescriptionStyleModel",
     "state": {
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "DescriptionStyleModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "StyleView",
      "description_width": ""
     }
    },
    "1d3ac111550c4e1c8bb1af0a9aa02819": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "HTMLModel",
     "state": {
      "_dom_classes": [],
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "HTMLModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/controls",
      "_view_module_version": "1.5.0",
      "_view_name": "HTMLView",
      "description": "",
      "description_tooltip": null,
      "layout": "IPY_MODEL_9cfe391a7bd44f1cae49d11d9a67c619",
      "placeholder": "​",
      "style": "IPY_MODEL_6adb2cb7317a4152aa0dc3703fab3a57",
      "value": " 904/904 [00:00&lt;00:00, 55.9kB/s]"
     }
    },
    "209bc5cba68a47adb43a822048c2b575": {
     "model_module": "@jupyter-widgets/base",
     "model_module_version": "1.2.0",
     "model_name": "LayoutModel",
     "state": {
      "_model_module": "@jupyter-widgets/base",
      "_model_module_version": "1.2.0",
      "_model_name": "LayoutModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "LayoutView",
      "align_content": null,
      "align_items": null,
      "align_self": null,
      "border": null,
      "bottom": null,
      "display": null,
      "flex": null,
      "flex_flow": null,
      "grid_area": null,
      "grid_auto_columns": null,
      "grid_auto_flow": null,
      "grid_auto_rows": null,
      "grid_column": null,
      "grid_gap": null,
      "grid_row": null,
      "grid_template_areas": null,
      "grid_template_columns": null,
      "grid_template_rows": null,
      "height": null,
      "justify_content": null,
      "justify_items": null,
      "left": null,
      "margin": null,
      "max_height": null,
      "max_width": null,
      "min_height": null,
      "min_width": null,
      "object_fit": null,
      "object_position": null,
      "order": null,
      "overflow": null,
      "overflow_x": null,
      "overflow_y": null,
      "padding": null,
      "right": null,
      "top": null,
      "visibility": null,
      "width": null
     }
    },
    "217a78b4c1a9477197b3c529f19cb16b": {
     "model_module": "@jupyter-widgets/base",
     "model_module_version": "1.2.0",
     "model_name": "LayoutModel",
     "state": {
      "_model_module": "@jupyter-widgets/base",
      "_model_module_version": "1.2.0",
      "_model_name": "LayoutModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "LayoutView",
      "align_content": null,
      "align_items": null,
      "align_self": null,
      "border": null,
      "bottom": null,
      "display": null,
      "flex": null,
      "flex_flow": null,
      "grid_area": null,
      "grid_auto_columns": null,
      "grid_auto_flow": null,
      "grid_auto_rows": null,
      "grid_column": null,
      "grid_gap": null,
      "grid_row": null,
      "grid_template_areas": null,
      "grid_template_columns": null,
      "grid_template_rows": null,
      "height": null,
      "justify_content": null,
      "justify_items": null,
      "left": null,
      "margin": null,
      "max_height": null,
      "max_width": null,
      "min_height": null,
      "min_width": null,
      "object_fit": null,
      "object_position": null,
      "order": null,
      "overflow": null,
      "overflow_x": null,
      "overflow_y": null,
      "padding": null,
      "right": null,
      "top": null,
      "visibility": null,
      "width": null
     }
    },
    "24ed4fe143e24d6091304da92ad02193": {
     "model_module": "@jupyter-widgets/base",
     "model_module_version": "1.2.0",
     "model_name": "LayoutModel",
     "state": {
      "_model_module": "@jupyter-widgets/base",
      "_model_module_version": "1.2.0",
      "_model_name": "LayoutModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "LayoutView",
      "align_content": null,
      "align_items": null,
      "align_self": null,
      "border": null,
      "bottom": null,
      "display": null,
      "flex": null,
      "flex_flow": null,
      "grid_area": null,
      "grid_auto_columns": null,
      "grid_auto_flow": null,
      "grid_auto_rows": null,
      "grid_column": null,
      "grid_gap": null,
      "grid_row": null,
      "grid_template_areas": null,
      "grid_template_columns": null,
      "grid_template_rows": null,
      "height": null,
      "justify_content": null,
      "justify_items": null,
      "left": null,
      "margin": null,
      "max_height": null,
      "max_width": null,
      "min_height": null,
      "min_width": null,
      "object_fit": null,
      "object_position": null,
      "order": null,
      "overflow": null,
      "overflow_x": null,
      "overflow_y": null,
      "padding": null,
      "right": null,
      "top": null,
      "visibility": null,
      "width": null
     }
    },
    "2917c19ada3843cda23399ea6ec55ba7": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "DescriptionStyleModel",
     "state": {
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "DescriptionStyleModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "StyleView",
      "description_width": ""
     }
    },
    "29229ba9e0a14c0d8c5f3e3de60be13a": {
     "model_module": "@jupyter-widgets/base",
     "model_module_version": "1.2.0",
     "model_name": "LayoutModel",
     "state": {
      "_model_module": "@jupyter-widgets/base",
      "_model_module_version": "1.2.0",
      "_model_name": "LayoutModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "LayoutView",
      "align_content": null,
      "align_items": null,
      "align_self": null,
      "border": null,
      "bottom": null,
      "display": null,
      "flex": null,
      "flex_flow": null,
      "grid_area": null,
      "grid_auto_columns": null,
      "grid_auto_flow": null,
      "grid_auto_rows": null,
      "grid_column": null,
      "grid_gap": null,
      "grid_row": null,
      "grid_template_areas": null,
      "grid_template_columns": null,
      "grid_template_rows": null,
      "height": null,
      "justify_content": null,
      "justify_items": null,
      "left": null,
      "margin": null,
      "max_height": null,
      "max_width": null,
      "min_height": null,
      "min_width": null,
      "object_fit": null,
      "object_position": null,
      "order": null,
      "overflow": null,
      "overflow_x": null,
      "overflow_y": null,
      "padding": null,
      "right": null,
      "top": null,
      "visibility": null,
      "width": null
     }
    },
    "2c645ed2b60348fa95ed0b109efae66d": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "DescriptionStyleModel",
     "state": {
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "DescriptionStyleModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "StyleView",
      "description_width": ""
     }
    },
    "2db75a49a5fd4064927a272548a43132": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "HTMLModel",
     "state": {
      "_dom_classes": [],
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "HTMLModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/controls",
      "_view_module_version": "1.5.0",
      "_view_name": "HTMLView",
      "description": "",
      "description_tooltip": null,
      "layout": "IPY_MODEL_ff80a1b1c5f24812832285a15cfc7c3a",
      "placeholder": "​",
      "style": "IPY_MODEL_2c645ed2b60348fa95ed0b109efae66d",
      "value": "\n<b>Pro Tip:</b> If you don't already have one, you can create a dedicated\n'notebooks' token with 'write' access, that you can then easily reuse for all\nnotebooks. </center>"
     }
    },
    "3098ad983889422ca1372785089bbcd6": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "DescriptionStyleModel",
     "state": {
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "DescriptionStyleModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "StyleView",
      "description_width": ""
     }
    },
    "3333ea6679a84bb18d8e2e06ed829d1e": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "HTMLModel",
     "state": {
      "_dom_classes": [],
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "HTMLModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/controls",
      "_view_module_version": "1.5.0",
      "_view_name": "HTMLView",
      "description": "",
      "description_tooltip": null,
      "layout": "IPY_MODEL_519544d96b084ae28f8619c28e2997a5",
      "placeholder": "​",
      "style": "IPY_MODEL_ac45cf4d8a024756b81124d087f6c23c",
      "value": "model-00002-of-00004.safetensors: 100%"
     }
    },
    "367804f8dd434dedb86cc6b6038ecfa4": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "DescriptionStyleModel",
     "state": {
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "DescriptionStyleModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "StyleView",
      "description_width": ""
     }
    },
    "379a027ca58442309c3f2d00c6ac2554": {
     "model_module": "@jupyter-widgets/base",
     "model_module_version": "1.2.0",
     "model_name": "LayoutModel",
     "state": {
      "_model_module": "@jupyter-widgets/base",
      "_model_module_version": "1.2.0",
      "_model_name": "LayoutModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "LayoutView",
      "align_content": null,
      "align_items": null,
      "align_self": null,
      "border": null,
      "bottom": null,
      "display": null,
      "flex": null,
      "flex_flow": null,
      "grid_area": null,
      "grid_auto_columns": null,
      "grid_auto_flow": null,
      "grid_auto_rows": null,
      "grid_column": null,
      "grid_gap": null,
      "grid_row": null,
      "grid_template_areas": null,
      "grid_template_columns": null,
      "grid_template_rows": null,
      "height": null,
      "justify_content": null,
      "justify_items": null,
      "left": null,
      "margin": null,
      "max_height": null,
      "max_width": null,
      "min_height": null,
      "min_width": null,
      "object_fit": null,
      "object_position": null,
      "order": null,
      "overflow": null,
      "overflow_x": null,
      "overflow_y": null,
      "padding": null,
      "right": null,
      "top": null,
      "visibility": null,
      "width": null
     }
    },
    "386a30cb9743499b85bdf0be592a7b0b": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "ProgressStyleModel",
     "state": {
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "ProgressStyleModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "StyleView",
      "bar_color": null,
      "description_width": ""
     }
    },
    "3930f6eb84614b0a800366ba87c0d3ff": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "HTMLModel",
     "state": {
      "_dom_classes": [],
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "HTMLModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/controls",
      "_view_module_version": "1.5.0",
      "_view_name": "HTMLView",
      "description": "",
      "description_tooltip": null,
      "layout": "IPY_MODEL_eb5f03fa32514498b66edda1b5b8c0be",
      "placeholder": "​",
      "style": "IPY_MODEL_df347a18ec3f4b5098f395a522391837",
      "value": "special_tokens_map.json: 100%"
     }
    },
    "394ffb8ac93e4e34a495872df474fe93": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "FloatProgressModel",
     "state": {
      "_dom_classes": [],
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "FloatProgressModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/controls",
      "_view_module_version": "1.5.0",
      "_view_name": "ProgressView",
      "bar_style": "success",
      "description": "",
      "description_tooltip": null,
      "layout": "IPY_MODEL_4930f889870b462687cf7f7d7092f7d0",
      "max": 2480,
      "min": 0,
      "orientation": "horizontal",
      "style": "IPY_MODEL_525225ff4e264c809fdec57c77d30e7e",
      "value": 2480
     }
    },
    "3ac6f376a8c74bf9bdf33a5f43180d3f": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "HTMLModel",
     "state": {
      "_dom_classes": [],
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "HTMLModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/controls",
      "_view_module_version": "1.5.0",
      "_view_name": "HTMLView",
      "description": "",
      "description_tooltip": null,
      "layout": "IPY_MODEL_7d409a913eb541f99a26e2340083c699",
      "placeholder": "​",
      "style": "IPY_MODEL_9063edd9bed442298a906e9d7637fad0",
      "value": "Downloading shards: 100%"
     }
    },
    "3ae8fb07b61442fb955a0cd04c5d9d71": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "FloatProgressModel",
     "state": {
      "_dom_classes": [],
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "FloatProgressModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/controls",
      "_view_module_version": "1.5.0",
      "_view_name": "ProgressView",
      "bar_style": "success",
      "description": "",
      "description_tooltip": null,
      "layout": "IPY_MODEL_ce567f04446e4948b592f0e0f6674b3f",
      "max": 1172,
      "min": 0,
      "orientation": "horizontal",
      "style": "IPY_MODEL_04685c3d17784ad192c68a77e2d021fd",
      "value": 1172
     }
    },
    "3ebfdbd7f5164e9895bcfe69121aadbb": {
     "model_module": "@jupyter-widgets/base",
     "model_module_version": "1.2.0",
     "model_name": "LayoutModel",
     "state": {
      "_model_module": "@jupyter-widgets/base",
      "_model_module_version": "1.2.0",
      "_model_name": "LayoutModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "LayoutView",
      "align_content": null,
      "align_items": null,
      "align_self": null,
      "border": null,
      "bottom": null,
      "display": null,
      "flex": null,
      "flex_flow": null,
      "grid_area": null,
      "grid_auto_columns": null,
      "grid_auto_flow": null,
      "grid_auto_rows": null,
      "grid_column": null,
      "grid_gap": null,
      "grid_row": null,
      "grid_template_areas": null,
      "grid_template_columns": null,
      "grid_template_rows": null,
      "height": null,
      "justify_content": null,
      "justify_items": null,
      "left": null,
      "margin": null,
      "max_height": null,
      "max_width": null,
      "min_height": null,
      "min_width": null,
      "object_fit": null,
      "object_position": null,
      "order": null,
      "overflow": null,
      "overflow_x": null,
      "overflow_y": null,
      "padding": null,
      "right": null,
      "top": null,
      "visibility": null,
      "width": null
     }
    },
    "3fcfc05fc84340f09e3049cfe70e05b0": {
     "model_module": "@jupyter-widgets/base",
     "model_module_version": "1.2.0",
     "model_name": "LayoutModel",
     "state": {
      "_model_module": "@jupyter-widgets/base",
      "_model_module_version": "1.2.0",
      "_model_name": "LayoutModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "LayoutView",
      "align_content": null,
      "align_items": null,
      "align_self": null,
      "border": null,
      "bottom": null,
      "display": null,
      "flex": null,
      "flex_flow": null,
      "grid_area": null,
      "grid_auto_columns": null,
      "grid_auto_flow": null,
      "grid_auto_rows": null,
      "grid_column": null,
      "grid_gap": null,
      "grid_row": null,
      "grid_template_areas": null,
      "grid_template_columns": null,
      "grid_template_rows": null,
      "height": null,
      "justify_content": null,
      "justify_items": null,
      "left": null,
      "margin": null,
      "max_height": null,
      "max_width": null,
      "min_height": null,
      "min_width": null,
      "object_fit": null,
      "object_position": null,
      "order": null,
      "overflow": null,
      "overflow_x": null,
      "overflow_y": null,
      "padding": null,
      "right": null,
      "top": null,
      "visibility": null,
      "width": null
     }
    },
    "4142dd2913b64c25b07e46b74e14efe2": {
     "model_module": "@jupyter-widgets/base",
     "model_module_version": "1.2.0",
     "model_name": "LayoutModel",
     "state": {
      "_model_module": "@jupyter-widgets/base",
      "_model_module_version": "1.2.0",
      "_model_name": "LayoutModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "LayoutView",
      "align_content": null,
      "align_items": null,
      "align_self": null,
      "border": null,
      "bottom": null,
      "display": null,
      "flex": null,
      "flex_flow": null,
      "grid_area": null,
      "grid_auto_columns": null,
      "grid_auto_flow": null,
      "grid_auto_rows": null,
      "grid_column": null,
      "grid_gap": null,
      "grid_row": null,
      "grid_template_areas": null,
      "grid_template_columns": null,
      "grid_template_rows": null,
      "height": null,
      "justify_content": null,
      "justify_items": null,
      "left": null,
      "margin": null,
      "max_height": null,
      "max_width": null,
      "min_height": null,
      "min_width": null,
      "object_fit": null,
      "object_position": null,
      "order": null,
      "overflow": null,
      "overflow_x": null,
      "overflow_y": null,
      "padding": null,
      "right": null,
      "top": null,
      "visibility": null,
      "width": null
     }
    },
    "43caebb4fc8c4582b4db2d874eeafe5a": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "HTMLModel",
     "state": {
      "_dom_classes": [],
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "HTMLModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/controls",
      "_view_module_version": "1.5.0",
      "_view_name": "HTMLView",
      "description": "",
      "description_tooltip": null,
      "layout": "IPY_MODEL_1a7cdeaef0f94571b49324a9148edb9d",
      "placeholder": "​",
      "style": "IPY_MODEL_9e79a3b0d6d94fdb8e4146b31e84f1e3",
      "value": "model-00004-of-00004.safetensors: 100%"
     }
    },
    "4587adc475f3445e890158eadc1432eb": {
     "model_module": "@jupyter-widgets/base",
     "model_module_version": "1.2.0",
     "model_name": "LayoutModel",
     "state": {
      "_model_module": "@jupyter-widgets/base",
      "_model_module_version": "1.2.0",
      "_model_name": "LayoutModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "LayoutView",
      "align_content": null,
      "align_items": null,
      "align_self": null,
      "border": null,
      "bottom": null,
      "display": null,
      "flex": null,
      "flex_flow": null,
      "grid_area": null,
      "grid_auto_columns": null,
      "grid_auto_flow": null,
      "grid_auto_rows": null,
      "grid_column": null,
      "grid_gap": null,
      "grid_row": null,
      "grid_template_areas": null,
      "grid_template_columns": null,
      "grid_template_rows": null,
      "height": null,
      "justify_content": null,
      "justify_items": null,
      "left": null,
      "margin": null,
      "max_height": null,
      "max_width": null,
      "min_height": null,
      "min_width": null,
      "object_fit": null,
      "object_position": null,
      "order": null,
      "overflow": null,
      "overflow_x": null,
      "overflow_y": null,
      "padding": null,
      "right": null,
      "top": null,
      "visibility": null,
      "width": null
     }
    },
    "45ba28221666408e8ffe08cd75807ae5": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "HBoxModel",
     "state": {
      "_dom_classes": [],
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "HBoxModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/controls",
      "_view_module_version": "1.5.0",
      "_view_name": "HBoxView",
      "box_style": "",
      "children": [
       "IPY_MODEL_ff10e097306c472ca17a5b319425182d",
       "IPY_MODEL_495dd14b19c4453ca90ccb3f027b64a5",
       "IPY_MODEL_0aa617b9eb2243c1b7ff9464558f30a2"
      ],
      "layout": "IPY_MODEL_d6f7f2257acc44bf854bf20b44c7596f"
     }
    },
    "4930f889870b462687cf7f7d7092f7d0": {
     "model_module": "@jupyter-widgets/base",
     "model_module_version": "1.2.0",
     "model_name": "LayoutModel",
     "state": {
      "_model_module": "@jupyter-widgets/base",
      "_model_module_version": "1.2.0",
      "_model_name": "LayoutModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "LayoutView",
      "align_content": null,
      "align_items": null,
      "align_self": null,
      "border": null,
      "bottom": null,
      "display": null,
      "flex": null,
      "flex_flow": null,
      "grid_area": null,
      "grid_auto_columns": null,
      "grid_auto_flow": null,
      "grid_auto_rows": null,
      "grid_column": null,
      "grid_gap": null,
      "grid_row": null,
      "grid_template_areas": null,
      "grid_template_columns": null,
      "grid_template_rows": null,
      "height": null,
      "justify_content": null,
      "justify_items": null,
      "left": null,
      "margin": null,
      "max_height": null,
      "max_width": null,
      "min_height": null,
      "min_width": null,
      "object_fit": null,
      "object_position": null,
      "order": null,
      "overflow": null,
      "overflow_x": null,
      "overflow_y": null,
      "padding": null,
      "right": null,
      "top": null,
      "visibility": null,
      "width": null
     }
    },
    "495dd14b19c4453ca90ccb3f027b64a5": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "FloatProgressModel",
     "state": {
      "_dom_classes": [],
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "FloatProgressModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/controls",
      "_view_module_version": "1.5.0",
      "_view_name": "ProgressView",
      "bar_style": "success",
      "description": "",
      "description_tooltip": null,
      "layout": "IPY_MODEL_9654c9a38fc1411eb5fb665e9044b930",
      "max": 4255466,
      "min": 0,
      "orientation": "horizontal",
      "style": "IPY_MODEL_85cc249f9d33418eb652df1abd9fccc1",
      "value": 4255466
     }
    },
    "4b14c26a068e414bb889f056b4547e8e": {
     "model_module": "@jupyter-widgets/base",
     "model_module_version": "1.2.0",
     "model_name": "LayoutModel",
     "state": {
      "_model_module": "@jupyter-widgets/base",
      "_model_module_version": "1.2.0",
      "_model_name": "LayoutModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "LayoutView",
      "align_content": null,
      "align_items": null,
      "align_self": null,
      "border": null,
      "bottom": null,
      "display": null,
      "flex": null,
      "flex_flow": null,
      "grid_area": null,
      "grid_auto_columns": null,
      "grid_auto_flow": null,
      "grid_auto_rows": null,
      "grid_column": null,
      "grid_gap": null,
      "grid_row": null,
      "grid_template_areas": null,
      "grid_template_columns": null,
      "grid_template_rows": null,
      "height": null,
      "justify_content": null,
      "justify_items": null,
      "left": null,
      "margin": null,
      "max_height": null,
      "max_width": null,
      "min_height": null,
      "min_width": null,
      "object_fit": null,
      "object_position": null,
      "order": null,
      "overflow": null,
      "overflow_x": null,
      "overflow_y": null,
      "padding": null,
      "right": null,
      "top": null,
      "visibility": null,
      "width": null
     }
    },
    "4d06be51ecee42aba662bf321cff7f46": {
     "model_module": "@jupyter-widgets/base",
     "model_module_version": "1.2.0",
     "model_name": "LayoutModel",
     "state": {
      "_model_module": "@jupyter-widgets/base",
      "_model_module_version": "1.2.0",
      "_model_name": "LayoutModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "LayoutView",
      "align_content": null,
      "align_items": null,
      "align_self": null,
      "border": null,
      "bottom": null,
      "display": null,
      "flex": null,
      "flex_flow": null,
      "grid_area": null,
      "grid_auto_columns": null,
      "grid_auto_flow": null,
      "grid_auto_rows": null,
      "grid_column": null,
      "grid_gap": null,
      "grid_row": null,
      "grid_template_areas": null,
      "grid_template_columns": null,
      "grid_template_rows": null,
      "height": null,
      "justify_content": null,
      "justify_items": null,
      "left": null,
      "margin": null,
      "max_height": null,
      "max_width": null,
      "min_height": null,
      "min_width": null,
      "object_fit": null,
      "object_position": null,
      "order": null,
      "overflow": null,
      "overflow_x": null,
      "overflow_y": null,
      "padding": null,
      "right": null,
      "top": null,
      "visibility": null,
      "width": null
     }
    },
    "519544d96b084ae28f8619c28e2997a5": {
     "model_module": "@jupyter-widgets/base",
     "model_module_version": "1.2.0",
     "model_name": "LayoutModel",
     "state": {
      "_model_module": "@jupyter-widgets/base",
      "_model_module_version": "1.2.0",
      "_model_name": "LayoutModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "LayoutView",
      "align_content": null,
      "align_items": null,
      "align_self": null,
      "border": null,
      "bottom": null,
      "display": null,
      "flex": null,
      "flex_flow": null,
      "grid_area": null,
      "grid_auto_columns": null,
      "grid_auto_flow": null,
      "grid_auto_rows": null,
      "grid_column": null,
      "grid_gap": null,
      "grid_row": null,
      "grid_template_areas": null,
      "grid_template_columns": null,
      "grid_template_rows": null,
      "height": null,
      "justify_content": null,
      "justify_items": null,
      "left": null,
      "margin": null,
      "max_height": null,
      "max_width": null,
      "min_height": null,
      "min_width": null,
      "object_fit": null,
      "object_position": null,
      "order": null,
      "overflow": null,
      "overflow_x": null,
      "overflow_y": null,
      "padding": null,
      "right": null,
      "top": null,
      "visibility": null,
      "width": null
     }
    },
    "519f085fa5be43118df44d267b5cab9d": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "VBoxModel",
     "state": {
      "_dom_classes": [],
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "VBoxModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/controls",
      "_view_module_version": "1.5.0",
      "_view_name": "VBoxView",
      "box_style": "",
      "children": [
       "IPY_MODEL_d39062e059884a7da765a150c50978c0",
       "IPY_MODEL_fa672bb34ad8439d845449f5df64ecc0",
       "IPY_MODEL_b9e85752def14af48fc01f6e3522e26f"
      ],
      "layout": "IPY_MODEL_9bfa61f017754173b603a6ada7fda84f"
     }
    },
    "51a4567d16a0474485122159708ee34d": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "ProgressStyleModel",
     "state": {
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "ProgressStyleModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "StyleView",
      "bar_color": null,
      "description_width": ""
     }
    },
    "525225ff4e264c809fdec57c77d30e7e": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "ProgressStyleModel",
     "state": {
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "ProgressStyleModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "StyleView",
      "bar_color": null,
      "description_width": ""
     }
    },
    "52f024a1567440d696026ea37680f21e": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "FloatProgressModel",
     "state": {
      "_dom_classes": [],
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "FloatProgressModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/controls",
      "_view_module_version": "1.5.0",
      "_view_name": "ProgressView",
      "bar_style": "success",
      "description": "",
      "description_tooltip": null,
      "layout": "IPY_MODEL_5dce4921a0ad4f6fa9da4a9caa9268ee",
      "max": 576,
      "min": 0,
      "orientation": "horizontal",
      "style": "IPY_MODEL_8c114c3352384d49b182f3baaa57d51f",
      "value": 576
     }
    },
    "5b0f0f27a1d74db0b279d6da5d9bcb4e": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "DescriptionStyleModel",
     "state": {
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "DescriptionStyleModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "StyleView",
      "description_width": ""
     }
    },
    "5b180a8083fe43d99e29176e859000b0": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "HBoxModel",
     "state": {
      "_dom_classes": [],
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "HBoxModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/controls",
      "_view_module_version": "1.5.0",
      "_view_name": "HBoxView",
      "box_style": "",
      "children": [
       "IPY_MODEL_9ccb33cf5cd048b79cee655323667cee",
       "IPY_MODEL_7e95e2838b1441fcbfa12d10b0d156cb",
       "IPY_MODEL_c54f0d93b2da449eab0bf9586d2f6257"
      ],
      "layout": "IPY_MODEL_a2f5ed62f0ef454d83352660cb442af2"
     }
    },
    "5d9cf27a11a24ff4a30730271495defb": {
     "model_module": "@jupyter-widgets/base",
     "model_module_version": "1.2.0",
     "model_name": "LayoutModel",
     "state": {
      "_model_module": "@jupyter-widgets/base",
      "_model_module_version": "1.2.0",
      "_model_name": "LayoutModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "LayoutView",
      "align_content": null,
      "align_items": null,
      "align_self": null,
      "border": null,
      "bottom": null,
      "display": null,
      "flex": null,
      "flex_flow": null,
      "grid_area": null,
      "grid_auto_columns": null,
      "grid_auto_flow": null,
      "grid_auto_rows": null,
      "grid_column": null,
      "grid_gap": null,
      "grid_row": null,
      "grid_template_areas": null,
      "grid_template_columns": null,
      "grid_template_rows": null,
      "height": null,
      "justify_content": null,
      "justify_items": null,
      "left": null,
      "margin": null,
      "max_height": null,
      "max_width": null,
      "min_height": null,
      "min_width": null,
      "object_fit": null,
      "object_position": null,
      "order": null,
      "overflow": null,
      "overflow_x": null,
      "overflow_y": null,
      "padding": null,
      "right": null,
      "top": null,
      "visibility": null,
      "width": null
     }
    },
    "5dce4921a0ad4f6fa9da4a9caa9268ee": {
     "model_module": "@jupyter-widgets/base",
     "model_module_version": "1.2.0",
     "model_name": "LayoutModel",
     "state": {
      "_model_module": "@jupyter-widgets/base",
      "_model_module_version": "1.2.0",
      "_model_name": "LayoutModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "LayoutView",
      "align_content": null,
      "align_items": null,
      "align_self": null,
      "border": null,
      "bottom": null,
      "display": null,
      "flex": null,
      "flex_flow": null,
      "grid_area": null,
      "grid_auto_columns": null,
      "grid_auto_flow": null,
      "grid_auto_rows": null,
      "grid_column": null,
      "grid_gap": null,
      "grid_row": null,
      "grid_template_areas": null,
      "grid_template_columns": null,
      "grid_template_rows": null,
      "height": null,
      "justify_content": null,
      "justify_items": null,
      "left": null,
      "margin": null,
      "max_height": null,
      "max_width": null,
      "min_height": null,
      "min_width": null,
      "object_fit": null,
      "object_position": null,
      "order": null,
      "overflow": null,
      "overflow_x": null,
      "overflow_y": null,
      "padding": null,
      "right": null,
      "top": null,
      "visibility": null,
      "width": null
     }
    },
    "5e7d3b25cdc14dada55b137140532e0d": {
     "model_module": "@jupyter-widgets/base",
     "model_module_version": "1.2.0",
     "model_name": "LayoutModel",
     "state": {
      "_model_module": "@jupyter-widgets/base",
      "_model_module_version": "1.2.0",
      "_model_name": "LayoutModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "LayoutView",
      "align_content": null,
      "align_items": null,
      "align_self": null,
      "border": null,
      "bottom": null,
      "display": null,
      "flex": null,
      "flex_flow": null,
      "grid_area": null,
      "grid_auto_columns": null,
      "grid_auto_flow": null,
      "grid_auto_rows": null,
      "grid_column": null,
      "grid_gap": null,
      "grid_row": null,
      "grid_template_areas": null,
      "grid_template_columns": null,
      "grid_template_rows": null,
      "height": null,
      "justify_content": null,
      "justify_items": null,
      "left": null,
      "margin": null,
      "max_height": null,
      "max_width": null,
      "min_height": null,
      "min_width": null,
      "object_fit": null,
      "object_position": null,
      "order": null,
      "overflow": null,
      "overflow_x": null,
      "overflow_y": null,
      "padding": null,
      "right": null,
      "top": null,
      "visibility": null,
      "width": null
     }
    },
    "605cdd5e8c864cd4acdb8441d7c939da": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "HTMLModel",
     "state": {
      "_dom_classes": [],
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "HTMLModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/controls",
      "_view_module_version": "1.5.0",
      "_view_name": "HTMLView",
      "description": "",
      "description_tooltip": null,
      "layout": "IPY_MODEL_ed36240f86f5402cb630027e714f917b",
      "placeholder": "​",
      "style": "IPY_MODEL_da8f2de0427d408cae355f2b35dd889b",
      "value": "config.json: 100%"
     }
    },
    "60be614acfc14419abf5fd28a092a25b": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "HTMLModel",
     "state": {
      "_dom_classes": [],
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "HTMLModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/controls",
      "_view_module_version": "1.5.0",
      "_view_name": "HTMLView",
      "description": "",
      "description_tooltip": null,
      "layout": "IPY_MODEL_a9e0055d729145f9a96d6625db048c7c",
      "placeholder": "​",
      "style": "IPY_MODEL_1c475305d6724da9b388377b49544592",
      "value": "model-00001-of-00004.safetensors: 100%"
     }
    },
    "622564f91070479793cc53ddaeccf3b0": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "FloatProgressModel",
     "state": {
      "_dom_classes": [],
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "FloatProgressModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/controls",
      "_view_module_version": "1.5.0",
      "_view_name": "ProgressView",
      "bar_style": "success",
      "description": "",
      "description_tooltip": null,
      "layout": "IPY_MODEL_4142dd2913b64c25b07e46b74e14efe2",
      "max": 3511950624,
      "min": 0,
      "orientation": "horizontal",
      "style": "IPY_MODEL_51a4567d16a0474485122159708ee34d",
      "value": 3511950624
     }
    },
    "624124c2b7c54ad8963245fc358829d1": {
     "model_module": "@jupyter-widgets/base",
     "model_module_version": "1.2.0",
     "model_name": "LayoutModel",
     "state": {
      "_model_module": "@jupyter-widgets/base",
      "_model_module_version": "1.2.0",
      "_model_name": "LayoutModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "LayoutView",
      "align_content": null,
      "align_items": null,
      "align_self": null,
      "border": null,
      "bottom": null,
      "display": null,
      "flex": null,
      "flex_flow": null,
      "grid_area": null,
      "grid_auto_columns": null,
      "grid_auto_flow": null,
      "grid_auto_rows": null,
      "grid_column": null,
      "grid_gap": null,
      "grid_row": null,
      "grid_template_areas": null,
      "grid_template_columns": null,
      "grid_template_rows": null,
      "height": null,
      "justify_content": null,
      "justify_items": null,
      "left": null,
      "margin": null,
      "max_height": null,
      "max_width": null,
      "min_height": null,
      "min_width": null,
      "object_fit": null,
      "object_position": null,
      "order": null,
      "overflow": null,
      "overflow_x": null,
      "overflow_y": null,
      "padding": null,
      "right": null,
      "top": null,
      "visibility": null,
      "width": null
     }
    },
    "62dd346f895147429e54eb6cfe20b703": {
     "model_module": "@jupyter-widgets/base",
     "model_module_version": "1.2.0",
     "model_name": "LayoutModel",
     "state": {
      "_model_module": "@jupyter-widgets/base",
      "_model_module_version": "1.2.0",
      "_model_name": "LayoutModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "LayoutView",
      "align_content": null,
      "align_items": null,
      "align_self": null,
      "border": null,
      "bottom": null,
      "display": null,
      "flex": null,
      "flex_flow": null,
      "grid_area": null,
      "grid_auto_columns": null,
      "grid_auto_flow": null,
      "grid_auto_rows": null,
      "grid_column": null,
      "grid_gap": null,
      "grid_row": null,
      "grid_template_areas": null,
      "grid_template_columns": null,
      "grid_template_rows": null,
      "height": null,
      "justify_content": null,
      "justify_items": null,
      "left": null,
      "margin": null,
      "max_height": null,
      "max_width": null,
      "min_height": null,
      "min_width": null,
      "object_fit": null,
      "object_position": null,
      "order": null,
      "overflow": null,
      "overflow_x": null,
      "overflow_y": null,
      "padding": null,
      "right": null,
      "top": null,
      "visibility": null,
      "width": null
     }
    },
    "657dfd152dfd4f0dab2cf93f4a8bc849": {
     "model_module": "@jupyter-widgets/base",
     "model_module_version": "1.2.0",
     "model_name": "LayoutModel",
     "state": {
      "_model_module": "@jupyter-widgets/base",
      "_model_module_version": "1.2.0",
      "_model_name": "LayoutModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "LayoutView",
      "align_content": null,
      "align_items": null,
      "align_self": null,
      "border": null,
      "bottom": null,
      "display": null,
      "flex": null,
      "flex_flow": null,
      "grid_area": null,
      "grid_auto_columns": null,
      "grid_auto_flow": null,
      "grid_auto_rows": null,
      "grid_column": null,
      "grid_gap": null,
      "grid_row": null,
      "grid_template_areas": null,
      "grid_template_columns": null,
      "grid_template_rows": null,
      "height": null,
      "justify_content": null,
      "justify_items": null,
      "left": null,
      "margin": null,
      "max_height": null,
      "max_width": null,
      "min_height": null,
      "min_width": null,
      "object_fit": null,
      "object_position": null,
      "order": null,
      "overflow": null,
      "overflow_x": null,
      "overflow_y": null,
      "padding": null,
      "right": null,
      "top": null,
      "visibility": null,
      "width": null
     }
    },
    "663cae26acf5456c8010b5c0fc2c48f0": {
     "model_module": "@jupyter-widgets/base",
     "model_module_version": "1.2.0",
     "model_name": "LayoutModel",
     "state": {
      "_model_module": "@jupyter-widgets/base",
      "_model_module_version": "1.2.0",
      "_model_name": "LayoutModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "LayoutView",
      "align_content": null,
      "align_items": null,
      "align_self": null,
      "border": null,
      "bottom": null,
      "display": null,
      "flex": null,
      "flex_flow": null,
      "grid_area": null,
      "grid_auto_columns": null,
      "grid_auto_flow": null,
      "grid_auto_rows": null,
      "grid_column": null,
      "grid_gap": null,
      "grid_row": null,
      "grid_template_areas": null,
      "grid_template_columns": null,
      "grid_template_rows": null,
      "height": null,
      "justify_content": null,
      "justify_items": null,
      "left": null,
      "margin": null,
      "max_height": null,
      "max_width": null,
      "min_height": null,
      "min_width": null,
      "object_fit": null,
      "object_position": null,
      "order": null,
      "overflow": null,
      "overflow_x": null,
      "overflow_y": null,
      "padding": null,
      "right": null,
      "top": null,
      "visibility": null,
      "width": null
     }
    },
    "6753f4b11d49482cad94304136c3c314": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "HTMLModel",
     "state": {
      "_dom_classes": [],
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "HTMLModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/controls",
      "_view_module_version": "1.5.0",
      "_view_name": "HTMLView",
      "description": "",
      "description_tooltip": null,
      "layout": "IPY_MODEL_c914ce2927ce426786a0ff0cd1545215",
      "placeholder": "​",
      "style": "IPY_MODEL_edee44fd2a324f60b42fcc42940f1196",
      "value": "model-00003-of-00004.safetensors: 100%"
     }
    },
    "67ca07517f6e49d6a25fa5ac9a7c9544": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "HBoxModel",
     "state": {
      "_dom_classes": [],
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "HBoxModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/controls",
      "_view_module_version": "1.5.0",
      "_view_name": "HBoxView",
      "box_style": "",
      "children": [
       "IPY_MODEL_605cdd5e8c864cd4acdb8441d7c939da",
       "IPY_MODEL_52f024a1567440d696026ea37680f21e",
       "IPY_MODEL_940e6ac63a024220a8902c632972b926"
      ],
      "layout": "IPY_MODEL_29229ba9e0a14c0d8c5f3e3de60be13a"
     }
    },
    "6adb2cb7317a4152aa0dc3703fab3a57": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "DescriptionStyleModel",
     "state": {
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "DescriptionStyleModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "StyleView",
      "description_width": ""
     }
    },
    "6c8b866f64964b699f392f3e9b864777": {
     "model_module": "@jupyter-widgets/base",
     "model_module_version": "1.2.0",
     "model_name": "LayoutModel",
     "state": {
      "_model_module": "@jupyter-widgets/base",
      "_model_module_version": "1.2.0",
      "_model_name": "LayoutModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "LayoutView",
      "align_content": null,
      "align_items": null,
      "align_self": null,
      "border": null,
      "bottom": null,
      "display": null,
      "flex": null,
      "flex_flow": null,
      "grid_area": null,
      "grid_auto_columns": null,
      "grid_auto_flow": null,
      "grid_auto_rows": null,
      "grid_column": null,
      "grid_gap": null,
      "grid_row": null,
      "grid_template_areas": null,
      "grid_template_columns": null,
      "grid_template_rows": null,
      "height": null,
      "justify_content": null,
      "justify_items": null,
      "left": null,
      "margin": null,
      "max_height": null,
      "max_width": null,
      "min_height": null,
      "min_width": null,
      "object_fit": null,
      "object_position": null,
      "order": null,
      "overflow": null,
      "overflow_x": null,
      "overflow_y": null,
      "padding": null,
      "right": null,
      "top": null,
      "visibility": null,
      "width": null
     }
    },
    "6d6503eec6a647dfb83deb916bf90076": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "DescriptionStyleModel",
     "state": {
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "DescriptionStyleModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "StyleView",
      "description_width": ""
     }
    },
    "6ecfbfb69098454a99fd90aed8442963": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "DescriptionStyleModel",
     "state": {
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "DescriptionStyleModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "StyleView",
      "description_width": ""
     }
    },
    "71f921d8b9f14f1aaf9e5acac801dcc7": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "HTMLModel",
     "state": {
      "_dom_classes": [],
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "HTMLModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/controls",
      "_view_module_version": "1.5.0",
      "_view_name": "HTMLView",
      "description": "",
      "description_tooltip": null,
      "layout": "IPY_MODEL_d0948d82b60d4258b7646337ac537b22",
      "placeholder": "​",
      "style": "IPY_MODEL_9e678595580e4ef58078ffdca08a6325",
      "value": " 4.98G/4.98G [03:37&lt;00:00, 24.1MB/s]"
     }
    },
    "75d7762287c94eb79a1777209c464106": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "DescriptionStyleModel",
     "state": {
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "DescriptionStyleModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "StyleView",
      "description_width": ""
     }
    },
    "7644216ac17748ba98b81d85c4b9ec6b": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "LabelModel",
     "state": {
      "_dom_classes": [],
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "LabelModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/controls",
      "_view_module_version": "1.5.0",
      "_view_name": "LabelView",
      "description": "",
      "description_tooltip": null,
      "layout": "IPY_MODEL_4b14c26a068e414bb889f056b4547e8e",
      "placeholder": "​",
      "style": "IPY_MODEL_fdc104a103c5433ab5748e37e3bc386f",
      "value": "Connecting..."
     }
    },
    "76549284aceb40cda16b7c80d3b3eb51": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "DescriptionStyleModel",
     "state": {
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "DescriptionStyleModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "StyleView",
      "description_width": ""
     }
    },
    "78da69bff39a4520b15c254466fbe8d8": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "PasswordModel",
     "state": {
      "_dom_classes": [],
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "PasswordModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/controls",
      "_view_module_version": "1.5.0",
      "_view_name": "PasswordView",
      "continuous_update": true,
      "description": "Token:",
      "description_tooltip": null,
      "disabled": false,
      "layout": "IPY_MODEL_6c8b866f64964b699f392f3e9b864777",
      "placeholder": "​",
      "style": "IPY_MODEL_76549284aceb40cda16b7c80d3b3eb51",
      "value": ""
     }
    },
    "79701eb82b804678b28223a5f7c0822c": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "HBoxModel",
     "state": {
      "_dom_classes": [],
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "HBoxModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/controls",
      "_view_module_version": "1.5.0",
      "_view_name": "HBoxView",
      "box_style": "",
      "children": [
       "IPY_MODEL_3ac6f376a8c74bf9bdf33a5f43180d3f",
       "IPY_MODEL_086d04ae7f5b4f689bf030b4a37eb4cc",
       "IPY_MODEL_886d393760754776844014afd6a08398"
      ],
      "layout": "IPY_MODEL_16b99d6509654fa1863501d74d5a5d85"
     }
    },
    "79dd6b13d77e40e89a4925384213cb1a": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "HBoxModel",
     "state": {
      "_dom_classes": [],
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "HBoxModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/controls",
      "_view_module_version": "1.5.0",
      "_view_name": "HBoxView",
      "box_style": "",
      "children": [
       "IPY_MODEL_3930f6eb84614b0a800366ba87c0d3ff",
       "IPY_MODEL_986e8cd55d4d42b89ff35df40550bd41",
       "IPY_MODEL_1d3ac111550c4e1c8bb1af0a9aa02819"
      ],
      "layout": "IPY_MODEL_d27b7d98259d48aaa2103fd95b47de6b"
     }
    },
    "7a17ce4a81924dd9a8237916ad44962f": {
     "model_module": "@jupyter-widgets/base",
     "model_module_version": "1.2.0",
     "model_name": "LayoutModel",
     "state": {
      "_model_module": "@jupyter-widgets/base",
      "_model_module_version": "1.2.0",
      "_model_name": "LayoutModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "LayoutView",
      "align_content": null,
      "align_items": null,
      "align_self": null,
      "border": null,
      "bottom": null,
      "display": null,
      "flex": null,
      "flex_flow": null,
      "grid_area": null,
      "grid_auto_columns": null,
      "grid_auto_flow": null,
      "grid_auto_rows": null,
      "grid_column": null,
      "grid_gap": null,
      "grid_row": null,
      "grid_template_areas": null,
      "grid_template_columns": null,
      "grid_template_rows": null,
      "height": null,
      "justify_content": null,
      "justify_items": null,
      "left": null,
      "margin": null,
      "max_height": null,
      "max_width": null,
      "min_height": null,
      "min_width": null,
      "object_fit": null,
      "object_position": null,
      "order": null,
      "overflow": null,
      "overflow_x": null,
      "overflow_y": null,
      "padding": null,
      "right": null,
      "top": null,
      "visibility": null,
      "width": null
     }
    },
    "7d409a913eb541f99a26e2340083c699": {
     "model_module": "@jupyter-widgets/base",
     "model_module_version": "1.2.0",
     "model_name": "LayoutModel",
     "state": {
      "_model_module": "@jupyter-widgets/base",
      "_model_module_version": "1.2.0",
      "_model_name": "LayoutModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "LayoutView",
      "align_content": null,
      "align_items": null,
      "align_self": null,
      "border": null,
      "bottom": null,
      "display": null,
      "flex": null,
      "flex_flow": null,
      "grid_area": null,
      "grid_auto_columns": null,
      "grid_auto_flow": null,
      "grid_auto_rows": null,
      "grid_column": null,
      "grid_gap": null,
      "grid_row": null,
      "grid_template_areas": null,
      "grid_template_columns": null,
      "grid_template_rows": null,
      "height": null,
      "justify_content": null,
      "justify_items": null,
      "left": null,
      "margin": null,
      "max_height": null,
      "max_width": null,
      "min_height": null,
      "min_width": null,
      "object_fit": null,
      "object_position": null,
      "order": null,
      "overflow": null,
      "overflow_x": null,
      "overflow_y": null,
      "padding": null,
      "right": null,
      "top": null,
      "visibility": null,
      "width": null
     }
    },
    "7e95e2838b1441fcbfa12d10b0d156cb": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "FloatProgressModel",
     "state": {
      "_dom_classes": [],
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "FloatProgressModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/controls",
      "_view_module_version": "1.5.0",
      "_view_name": "ProgressView",
      "bar_style": "success",
      "description": "",
      "description_tooltip": null,
      "layout": "IPY_MODEL_995890d9adbc4b5f99b477fd20ecd062",
      "max": 162,
      "min": 0,
      "orientation": "horizontal",
      "style": "IPY_MODEL_ca0c772bb8b84b06aa7e0de01f1755b8",
      "value": 162
     }
    },
    "81df48ada913475e97ab13c6c81ef1fa": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "HTMLModel",
     "state": {
      "_dom_classes": [],
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "HTMLModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/controls",
      "_view_module_version": "1.5.0",
      "_view_name": "HTMLView",
      "description": "",
      "description_tooltip": null,
      "layout": "IPY_MODEL_012654fc0cce414f91c337c2445621c7",
      "placeholder": "​",
      "style": "IPY_MODEL_1d31e4edcef041a68bef6ad037e07ef4",
      "value": " 368/368 [00:00&lt;00:00, 24.3kB/s]"
     }
    },
    "843f288ed695452eb22f9ab838ac8af6": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "ProgressStyleModel",
     "state": {
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "ProgressStyleModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "StyleView",
      "bar_color": null,
      "description_width": ""
     }
    },
    "84c9405fcb624c96ab215c4f9e39c3a9": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "HBoxModel",
     "state": {
      "_dom_classes": [],
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "HBoxModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/controls",
      "_view_module_version": "1.5.0",
      "_view_name": "HBoxView",
      "box_style": "",
      "children": [
       "IPY_MODEL_43caebb4fc8c4582b4db2d874eeafe5a",
       "IPY_MODEL_a6ed3bdd1a604cb0ae188c59a71d73f7",
       "IPY_MODEL_14311241232c455e9bc14405c0b86495"
      ],
      "layout": "IPY_MODEL_e49ad9b2dbef4890a4525dfff3bf6961"
     }
    },
    "84e07752fa684ae5bdc651f3240e56cf": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "HTMLModel",
     "state": {
      "_dom_classes": [],
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "HTMLModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/controls",
      "_view_module_version": "1.5.0",
      "_view_name": "HTMLView",
      "description": "",
      "description_tooltip": null,
      "layout": "IPY_MODEL_624124c2b7c54ad8963245fc358829d1",
      "placeholder": "​",
      "style": "IPY_MODEL_5b0f0f27a1d74db0b279d6da5d9bcb4e",
      "value": " 4.92G/4.92G [04:38&lt;00:00, 18.4MB/s]"
     }
    },
    "850a4df88a3c45aa930ffcdb3906f394": {
     "model_module": "@jupyter-widgets/base",
     "model_module_version": "1.2.0",
     "model_name": "LayoutModel",
     "state": {
      "_model_module": "@jupyter-widgets/base",
      "_model_module_version": "1.2.0",
      "_model_name": "LayoutModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "LayoutView",
      "align_content": null,
      "align_items": null,
      "align_self": null,
      "border": null,
      "bottom": null,
      "display": null,
      "flex": null,
      "flex_flow": null,
      "grid_area": null,
      "grid_auto_columns": null,
      "grid_auto_flow": null,
      "grid_auto_rows": null,
      "grid_column": null,
      "grid_gap": null,
      "grid_row": null,
      "grid_template_areas": null,
      "grid_template_columns": null,
      "grid_template_rows": null,
      "height": null,
      "justify_content": null,
      "justify_items": null,
      "left": null,
      "margin": null,
      "max_height": null,
      "max_width": null,
      "min_height": null,
      "min_width": null,
      "object_fit": null,
      "object_position": null,
      "order": null,
      "overflow": null,
      "overflow_x": null,
      "overflow_y": null,
      "padding": null,
      "right": null,
      "top": null,
      "visibility": null,
      "width": null
     }
    },
    "855dd916702847338ed071fe873846b2": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "DescriptionStyleModel",
     "state": {
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "DescriptionStyleModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "StyleView",
      "description_width": ""
     }
    },
    "85cc249f9d33418eb652df1abd9fccc1": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "ProgressStyleModel",
     "state": {
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "ProgressStyleModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "StyleView",
      "bar_color": null,
      "description_width": ""
     }
    },
    "8624f15b48074e91b843a3119eac2d30": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "DescriptionStyleModel",
     "state": {
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "DescriptionStyleModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "StyleView",
      "description_width": ""
     }
    },
    "886d393760754776844014afd6a08398": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "HTMLModel",
     "state": {
      "_dom_classes": [],
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "HTMLModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/controls",
      "_view_module_version": "1.5.0",
      "_view_name": "HTMLView",
      "description": "",
      "description_tooltip": null,
      "layout": "IPY_MODEL_d412a30e27234e3ebef17717f6f41707",
      "placeholder": "​",
      "style": "IPY_MODEL_af96a3754cc74989ac66080499851f15",
      "value": " 4/4 [21:04&lt;00:00, 301.28s/it]"
     }
    },
    "888642c2e99648b3b3efc625222bb9e1": {
     "model_module": "@jupyter-widgets/base",
     "model_module_version": "1.2.0",
     "model_name": "LayoutModel",
     "state": {
      "_model_module": "@jupyter-widgets/base",
      "_model_module_version": "1.2.0",
      "_model_name": "LayoutModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "LayoutView",
      "align_content": null,
      "align_items": null,
      "align_self": null,
      "border": null,
      "bottom": null,
      "display": null,
      "flex": null,
      "flex_flow": null,
      "grid_area": null,
      "grid_auto_columns": null,
      "grid_auto_flow": null,
      "grid_auto_rows": null,
      "grid_column": null,
      "grid_gap": null,
      "grid_row": null,
      "grid_template_areas": null,
      "grid_template_columns": null,
      "grid_template_rows": null,
      "height": null,
      "justify_content": null,
      "justify_items": null,
      "left": null,
      "margin": null,
      "max_height": null,
      "max_width": null,
      "min_height": null,
      "min_width": null,
      "object_fit": null,
      "object_position": null,
      "order": null,
      "overflow": null,
      "overflow_x": null,
      "overflow_y": null,
      "padding": null,
      "right": null,
      "top": null,
      "visibility": null,
      "width": null
     }
    },
    "88e702f408a94290aeb9a77f863aedc1": {
     "model_module": "@jupyter-widgets/base",
     "model_module_version": "1.2.0",
     "model_name": "LayoutModel",
     "state": {
      "_model_module": "@jupyter-widgets/base",
      "_model_module_version": "1.2.0",
      "_model_name": "LayoutModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "LayoutView",
      "align_content": null,
      "align_items": null,
      "align_self": null,
      "border": null,
      "bottom": null,
      "display": null,
      "flex": null,
      "flex_flow": null,
      "grid_area": null,
      "grid_auto_columns": null,
      "grid_auto_flow": null,
      "grid_auto_rows": null,
      "grid_column": null,
      "grid_gap": null,
      "grid_row": null,
      "grid_template_areas": null,
      "grid_template_columns": null,
      "grid_template_rows": null,
      "height": null,
      "justify_content": null,
      "justify_items": null,
      "left": null,
      "margin": null,
      "max_height": null,
      "max_width": null,
      "min_height": null,
      "min_width": null,
      "object_fit": null,
      "object_position": null,
      "order": null,
      "overflow": null,
      "overflow_x": null,
      "overflow_y": null,
      "padding": null,
      "right": null,
      "top": null,
      "visibility": null,
      "width": null
     }
    },
    "8984e5e319ab4fee98d839121c4f6937": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "HBoxModel",
     "state": {
      "_dom_classes": [],
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "HBoxModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/controls",
      "_view_module_version": "1.5.0",
      "_view_name": "HBoxView",
      "box_style": "",
      "children": [
       "IPY_MODEL_f4f5257a4ac540a9bc74d2814f0d38af",
       "IPY_MODEL_622564f91070479793cc53ddaeccf3b0",
       "IPY_MODEL_e3c53ac8e7d343ea87759a40e6c4f86a"
      ],
      "layout": "IPY_MODEL_217a78b4c1a9477197b3c529f19cb16b"
     }
    },
    "8b972f13e66f4e74aad6ef81347d9adc": {
     "model_module": "@jupyter-widgets/base",
     "model_module_version": "1.2.0",
     "model_name": "LayoutModel",
     "state": {
      "_model_module": "@jupyter-widgets/base",
      "_model_module_version": "1.2.0",
      "_model_name": "LayoutModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "LayoutView",
      "align_content": null,
      "align_items": null,
      "align_self": null,
      "border": null,
      "bottom": null,
      "display": null,
      "flex": null,
      "flex_flow": null,
      "grid_area": null,
      "grid_auto_columns": null,
      "grid_auto_flow": null,
      "grid_auto_rows": null,
      "grid_column": null,
      "grid_gap": null,
      "grid_row": null,
      "grid_template_areas": null,
      "grid_template_columns": null,
      "grid_template_rows": null,
      "height": null,
      "justify_content": null,
      "justify_items": null,
      "left": null,
      "margin": null,
      "max_height": null,
      "max_width": null,
      "min_height": null,
      "min_width": null,
      "object_fit": null,
      "object_position": null,
      "order": null,
      "overflow": null,
      "overflow_x": null,
      "overflow_y": null,
      "padding": null,
      "right": null,
      "top": null,
      "visibility": null,
      "width": null
     }
    },
    "8bbc41932a6e4ef2b5933a612d6c93d6": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "DescriptionStyleModel",
     "state": {
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "DescriptionStyleModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "StyleView",
      "description_width": ""
     }
    },
    "8bee439ad1f746d49ee80eaca31a0c9c": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "CheckboxModel",
     "state": {
      "_dom_classes": [],
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "CheckboxModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/controls",
      "_view_module_version": "1.5.0",
      "_view_name": "CheckboxView",
      "description": "Add token as git credential?",
      "description_tooltip": null,
      "disabled": false,
      "indent": true,
      "layout": "IPY_MODEL_16c8159aa8dd49bab3854278f1160bb6",
      "style": "IPY_MODEL_cf9ac11864984bcbb7be3ade707dcfc9",
      "value": false
     }
    },
    "8c114c3352384d49b182f3baaa57d51f": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "ProgressStyleModel",
     "state": {
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "ProgressStyleModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "StyleView",
      "bar_color": null,
      "description_width": ""
     }
    },
    "8e26a4c0644c416184af2407f00dea81": {
     "model_module": "@jupyter-widgets/base",
     "model_module_version": "1.2.0",
     "model_name": "LayoutModel",
     "state": {
      "_model_module": "@jupyter-widgets/base",
      "_model_module_version": "1.2.0",
      "_model_name": "LayoutModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "LayoutView",
      "align_content": null,
      "align_items": null,
      "align_self": null,
      "border": null,
      "bottom": null,
      "display": null,
      "flex": null,
      "flex_flow": null,
      "grid_area": null,
      "grid_auto_columns": null,
      "grid_auto_flow": null,
      "grid_auto_rows": null,
      "grid_column": null,
      "grid_gap": null,
      "grid_row": null,
      "grid_template_areas": null,
      "grid_template_columns": null,
      "grid_template_rows": null,
      "height": null,
      "justify_content": null,
      "justify_items": null,
      "left": null,
      "margin": null,
      "max_height": null,
      "max_width": null,
      "min_height": null,
      "min_width": null,
      "object_fit": null,
      "object_position": null,
      "order": null,
      "overflow": null,
      "overflow_x": null,
      "overflow_y": null,
      "padding": null,
      "right": null,
      "top": null,
      "visibility": null,
      "width": null
     }
    },
    "8e7896f4d12c45159cf713a3a8682570": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "DescriptionStyleModel",
     "state": {
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "DescriptionStyleModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "StyleView",
      "description_width": ""
     }
    },
    "9063edd9bed442298a906e9d7637fad0": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "DescriptionStyleModel",
     "state": {
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "DescriptionStyleModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "StyleView",
      "description_width": ""
     }
    },
    "92818ef1790e46fd92fd3ba8bdca2928": {
     "model_module": "@jupyter-widgets/base",
     "model_module_version": "1.2.0",
     "model_name": "LayoutModel",
     "state": {
      "_model_module": "@jupyter-widgets/base",
      "_model_module_version": "1.2.0",
      "_model_name": "LayoutModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "LayoutView",
      "align_content": null,
      "align_items": null,
      "align_self": null,
      "border": null,
      "bottom": null,
      "display": null,
      "flex": null,
      "flex_flow": null,
      "grid_area": null,
      "grid_auto_columns": null,
      "grid_auto_flow": null,
      "grid_auto_rows": null,
      "grid_column": null,
      "grid_gap": null,
      "grid_row": null,
      "grid_template_areas": null,
      "grid_template_columns": null,
      "grid_template_rows": null,
      "height": null,
      "justify_content": null,
      "justify_items": null,
      "left": null,
      "margin": null,
      "max_height": null,
      "max_width": null,
      "min_height": null,
      "min_width": null,
      "object_fit": null,
      "object_position": null,
      "order": null,
      "overflow": null,
      "overflow_x": null,
      "overflow_y": null,
      "padding": null,
      "right": null,
      "top": null,
      "visibility": null,
      "width": null
     }
    },
    "940e6ac63a024220a8902c632972b926": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "HTMLModel",
     "state": {
      "_dom_classes": [],
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "HTMLModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/controls",
      "_view_module_version": "1.5.0",
      "_view_name": "HTMLView",
      "description": "",
      "description_tooltip": null,
      "layout": "IPY_MODEL_cfea82b2647c46a9866f74cee7487a4d",
      "placeholder": "​",
      "style": "IPY_MODEL_da5830bc484f40bd8f54ddb282e4554d",
      "value": " 576/576 [00:00&lt;00:00, 39.1kB/s]"
     }
    },
    "94a7f89dc99c4d8487a0b7e3e4314c06": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "FloatProgressModel",
     "state": {
      "_dom_classes": [],
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "FloatProgressModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/controls",
      "_view_module_version": "1.5.0",
      "_view_name": "ProgressView",
      "bar_style": "success",
      "description": "",
      "description_tooltip": null,
      "layout": "IPY_MODEL_657dfd152dfd4f0dab2cf93f4a8bc849",
      "max": 4999110360,
      "min": 0,
      "orientation": "horizontal",
      "style": "IPY_MODEL_386a30cb9743499b85bdf0be592a7b0b",
      "value": 4999110360
     }
    },
    "9654c9a38fc1411eb5fb665e9044b930": {
     "model_module": "@jupyter-widgets/base",
     "model_module_version": "1.2.0",
     "model_name": "LayoutModel",
     "state": {
      "_model_module": "@jupyter-widgets/base",
      "_model_module_version": "1.2.0",
      "_model_name": "LayoutModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "LayoutView",
      "align_content": null,
      "align_items": null,
      "align_self": null,
      "border": null,
      "bottom": null,
      "display": null,
      "flex": null,
      "flex_flow": null,
      "grid_area": null,
      "grid_auto_columns": null,
      "grid_auto_flow": null,
      "grid_auto_rows": null,
      "grid_column": null,
      "grid_gap": null,
      "grid_row": null,
      "grid_template_areas": null,
      "grid_template_columns": null,
      "grid_template_rows": null,
      "height": null,
      "justify_content": null,
      "justify_items": null,
      "left": null,
      "margin": null,
      "max_height": null,
      "max_width": null,
      "min_height": null,
      "min_width": null,
      "object_fit": null,
      "object_position": null,
      "order": null,
      "overflow": null,
      "overflow_x": null,
      "overflow_y": null,
      "padding": null,
      "right": null,
      "top": null,
      "visibility": null,
      "width": null
     }
    },
    "986c17897b0c446184fd9c44af5919c3": {
     "model_module": "@jupyter-widgets/base",
     "model_module_version": "1.2.0",
     "model_name": "LayoutModel",
     "state": {
      "_model_module": "@jupyter-widgets/base",
      "_model_module_version": "1.2.0",
      "_model_name": "LayoutModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "LayoutView",
      "align_content": null,
      "align_items": null,
      "align_self": null,
      "border": null,
      "bottom": null,
      "display": null,
      "flex": null,
      "flex_flow": null,
      "grid_area": null,
      "grid_auto_columns": null,
      "grid_auto_flow": null,
      "grid_auto_rows": null,
      "grid_column": null,
      "grid_gap": null,
      "grid_row": null,
      "grid_template_areas": null,
      "grid_template_columns": null,
      "grid_template_rows": null,
      "height": null,
      "justify_content": null,
      "justify_items": null,
      "left": null,
      "margin": null,
      "max_height": null,
      "max_width": null,
      "min_height": null,
      "min_width": null,
      "object_fit": null,
      "object_position": null,
      "order": null,
      "overflow": null,
      "overflow_x": null,
      "overflow_y": null,
      "padding": null,
      "right": null,
      "top": null,
      "visibility": null,
      "width": null
     }
    },
    "986e8cd55d4d42b89ff35df40550bd41": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "FloatProgressModel",
     "state": {
      "_dom_classes": [],
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "FloatProgressModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/controls",
      "_view_module_version": "1.5.0",
      "_view_name": "ProgressView",
      "bar_style": "success",
      "description": "",
      "description_tooltip": null,
      "layout": "IPY_MODEL_e41b5070db864d259f9c0cd3f14742e0",
      "max": 904,
      "min": 0,
      "orientation": "horizontal",
      "style": "IPY_MODEL_b4519db0d32a4ed180e12283b8ed94f0",
      "value": 904
     }
    },
    "995890d9adbc4b5f99b477fd20ecd062": {
     "model_module": "@jupyter-widgets/base",
     "model_module_version": "1.2.0",
     "model_name": "LayoutModel",
     "state": {
      "_model_module": "@jupyter-widgets/base",
      "_model_module_version": "1.2.0",
      "_model_name": "LayoutModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "LayoutView",
      "align_content": null,
      "align_items": null,
      "align_self": null,
      "border": null,
      "bottom": null,
      "display": null,
      "flex": null,
      "flex_flow": null,
      "grid_area": null,
      "grid_auto_columns": null,
      "grid_auto_flow": null,
      "grid_auto_rows": null,
      "grid_column": null,
      "grid_gap": null,
      "grid_row": null,
      "grid_template_areas": null,
      "grid_template_columns": null,
      "grid_template_rows": null,
      "height": null,
      "justify_content": null,
      "justify_items": null,
      "left": null,
      "margin": null,
      "max_height": null,
      "max_width": null,
      "min_height": null,
      "min_width": null,
      "object_fit": null,
      "object_position": null,
      "order": null,
      "overflow": null,
      "overflow_x": null,
      "overflow_y": null,
      "padding": null,
      "right": null,
      "top": null,
      "visibility": null,
      "width": null
     }
    },
    "9bf765dea2bf4e148d5888ba3ecbc532": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "HTMLModel",
     "state": {
      "_dom_classes": [],
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "HTMLModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/controls",
      "_view_module_version": "1.5.0",
      "_view_name": "HTMLView",
      "description": "",
      "description_tooltip": null,
      "layout": "IPY_MODEL_88e702f408a94290aeb9a77f863aedc1",
      "placeholder": "​",
      "style": "IPY_MODEL_ffef39ba35064df99d13daa1d8b781aa",
      "value": "config.json: 100%"
     }
    },
    "9bfa61f017754173b603a6ada7fda84f": {
     "model_module": "@jupyter-widgets/base",
     "model_module_version": "1.2.0",
     "model_name": "LayoutModel",
     "state": {
      "_model_module": "@jupyter-widgets/base",
      "_model_module_version": "1.2.0",
      "_model_name": "LayoutModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "LayoutView",
      "align_content": null,
      "align_items": "center",
      "align_self": null,
      "border": null,
      "bottom": null,
      "display": "flex",
      "flex": null,
      "flex_flow": "column",
      "grid_area": null,
      "grid_auto_columns": null,
      "grid_auto_flow": null,
      "grid_auto_rows": null,
      "grid_column": null,
      "grid_gap": null,
      "grid_row": null,
      "grid_template_areas": null,
      "grid_template_columns": null,
      "grid_template_rows": null,
      "height": null,
      "justify_content": null,
      "justify_items": null,
      "left": null,
      "margin": null,
      "max_height": null,
      "max_width": null,
      "min_height": null,
      "min_width": null,
      "object_fit": null,
      "object_position": null,
      "order": null,
      "overflow": null,
      "overflow_x": null,
      "overflow_y": null,
      "padding": null,
      "right": null,
      "top": null,
      "visibility": null,
      "width": "50%"
     }
    },
    "9ccb33cf5cd048b79cee655323667cee": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "HTMLModel",
     "state": {
      "_dom_classes": [],
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "HTMLModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/controls",
      "_view_module_version": "1.5.0",
      "_view_name": "HTMLView",
      "description": "",
      "description_tooltip": null,
      "layout": "IPY_MODEL_5e7d3b25cdc14dada55b137140532e0d",
      "placeholder": "​",
      "style": "IPY_MODEL_6d6503eec6a647dfb83deb916bf90076",
      "value": "generation_config.json: 100%"
     }
    },
    "9cfe391a7bd44f1cae49d11d9a67c619": {
     "model_module": "@jupyter-widgets/base",
     "model_module_version": "1.2.0",
     "model_name": "LayoutModel",
     "state": {
      "_model_module": "@jupyter-widgets/base",
      "_model_module_version": "1.2.0",
      "_model_name": "LayoutModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "LayoutView",
      "align_content": null,
      "align_items": null,
      "align_self": null,
      "border": null,
      "bottom": null,
      "display": null,
      "flex": null,
      "flex_flow": null,
      "grid_area": null,
      "grid_auto_columns": null,
      "grid_auto_flow": null,
      "grid_auto_rows": null,
      "grid_column": null,
      "grid_gap": null,
      "grid_row": null,
      "grid_template_areas": null,
      "grid_template_columns": null,
      "grid_template_rows": null,
      "height": null,
      "justify_content": null,
      "justify_items": null,
      "left": null,
      "margin": null,
      "max_height": null,
      "max_width": null,
      "min_height": null,
      "min_width": null,
      "object_fit": null,
      "object_position": null,
      "order": null,
      "overflow": null,
      "overflow_x": null,
      "overflow_y": null,
      "padding": null,
      "right": null,
      "top": null,
      "visibility": null,
      "width": null
     }
    },
    "9e678595580e4ef58078ffdca08a6325": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "DescriptionStyleModel",
     "state": {
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "DescriptionStyleModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "StyleView",
      "description_width": ""
     }
    },
    "9e79a3b0d6d94fdb8e4146b31e84f1e3": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "DescriptionStyleModel",
     "state": {
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "DescriptionStyleModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "StyleView",
      "description_width": ""
     }
    },
    "9efd2bb0e5974bb5bc2b57e1c5cc1b42": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "ProgressStyleModel",
     "state": {
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "ProgressStyleModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "StyleView",
      "bar_color": null,
      "description_width": ""
     }
    },
    "a1f73f0c8bf44fdfad45e0222ef24a75": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "FloatProgressModel",
     "state": {
      "_dom_classes": [],
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "FloatProgressModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/controls",
      "_view_module_version": "1.5.0",
      "_view_name": "ProgressView",
      "bar_style": "success",
      "description": "",
      "description_tooltip": null,
      "layout": "IPY_MODEL_d71260daa1114bfbbd04e9cc232dcf53",
      "max": 4,
      "min": 0,
      "orientation": "horizontal",
      "style": "IPY_MODEL_9efd2bb0e5974bb5bc2b57e1c5cc1b42",
      "value": 4
     }
    },
    "a2f5ed62f0ef454d83352660cb442af2": {
     "model_module": "@jupyter-widgets/base",
     "model_module_version": "1.2.0",
     "model_name": "LayoutModel",
     "state": {
      "_model_module": "@jupyter-widgets/base",
      "_model_module_version": "1.2.0",
      "_model_name": "LayoutModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "LayoutView",
      "align_content": null,
      "align_items": null,
      "align_self": null,
      "border": null,
      "bottom": null,
      "display": null,
      "flex": null,
      "flex_flow": null,
      "grid_area": null,
      "grid_auto_columns": null,
      "grid_auto_flow": null,
      "grid_auto_rows": null,
      "grid_column": null,
      "grid_gap": null,
      "grid_row": null,
      "grid_template_areas": null,
      "grid_template_columns": null,
      "grid_template_rows": null,
      "height": null,
      "justify_content": null,
      "justify_items": null,
      "left": null,
      "margin": null,
      "max_height": null,
      "max_width": null,
      "min_height": null,
      "min_width": null,
      "object_fit": null,
      "object_position": null,
      "order": null,
      "overflow": null,
      "overflow_x": null,
      "overflow_y": null,
      "padding": null,
      "right": null,
      "top": null,
      "visibility": null,
      "width": null
     }
    },
    "a31b383a10e44033844919b4f763b003": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "HTMLModel",
     "state": {
      "_dom_classes": [],
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "HTMLModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/controls",
      "_view_module_version": "1.5.0",
      "_view_name": "HTMLView",
      "description": "",
      "description_tooltip": null,
      "layout": "IPY_MODEL_f05e7d4f70684ea08f488713660fe8ff",
      "placeholder": "​",
      "style": "IPY_MODEL_f58199d4cb9b4dc4938016aef61f90ab",
      "value": " 1.17k/1.17k [00:00&lt;00:00, 61.6kB/s]"
     }
    },
    "a5d939193bcc4b2ab9c091d6028eb1f7": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "FloatProgressModel",
     "state": {
      "_dom_classes": [],
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "FloatProgressModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/controls",
      "_view_module_version": "1.5.0",
      "_view_name": "ProgressView",
      "bar_style": "success",
      "description": "",
      "description_tooltip": null,
      "layout": "IPY_MODEL_b722ffae626346ba8474957773ba56e5",
      "max": 4978813696,
      "min": 0,
      "orientation": "horizontal",
      "style": "IPY_MODEL_843f288ed695452eb22f9ab838ac8af6",
      "value": 4978813696
     }
    },
    "a6ed3bdd1a604cb0ae188c59a71d73f7": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "FloatProgressModel",
     "state": {
      "_dom_classes": [],
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "FloatProgressModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/controls",
      "_view_module_version": "1.5.0",
      "_view_name": "ProgressView",
      "bar_style": "success",
      "description": "",
      "description_tooltip": null,
      "layout": "IPY_MODEL_b4d1aa797f2445e1aba35dd1786db7f9",
      "max": 1039689632,
      "min": 0,
      "orientation": "horizontal",
      "style": "IPY_MODEL_dbb6d44f37a74edb9bb2b80e4b8af16a",
      "value": 1039689632
     }
    },
    "a9e0055d729145f9a96d6625db048c7c": {
     "model_module": "@jupyter-widgets/base",
     "model_module_version": "1.2.0",
     "model_name": "LayoutModel",
     "state": {
      "_model_module": "@jupyter-widgets/base",
      "_model_module_version": "1.2.0",
      "_model_name": "LayoutModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "LayoutView",
      "align_content": null,
      "align_items": null,
      "align_self": null,
      "border": null,
      "bottom": null,
      "display": null,
      "flex": null,
      "flex_flow": null,
      "grid_area": null,
      "grid_auto_columns": null,
      "grid_auto_flow": null,
      "grid_auto_rows": null,
      "grid_column": null,
      "grid_gap": null,
      "grid_row": null,
      "grid_template_areas": null,
      "grid_template_columns": null,
      "grid_template_rows": null,
      "height": null,
      "justify_content": null,
      "justify_items": null,
      "left": null,
      "margin": null,
      "max_height": null,
      "max_width": null,
      "min_height": null,
      "min_width": null,
      "object_fit": null,
      "object_position": null,
      "order": null,
      "overflow": null,
      "overflow_x": null,
      "overflow_y": null,
      "padding": null,
      "right": null,
      "top": null,
      "visibility": null,
      "width": null
     }
    },
    "ac45cf4d8a024756b81124d087f6c23c": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "DescriptionStyleModel",
     "state": {
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "DescriptionStyleModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "StyleView",
      "description_width": ""
     }
    },
    "ac62ce63e1ab4d15b640c4976df34801": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "HTMLModel",
     "state": {
      "_dom_classes": [],
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "HTMLModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/controls",
      "_view_module_version": "1.5.0",
      "_view_name": "HTMLView",
      "description": "",
      "description_tooltip": null,
      "layout": "IPY_MODEL_4d06be51ecee42aba662bf321cff7f46",
      "placeholder": "​",
      "style": "IPY_MODEL_e01fde47f6a7485e87e7da717d245ba6",
      "value": "<center> <img\nsrc=https://huggingface.co/front/assets/huggingface_logo-noborder.svg\nalt='Hugging Face'> <br> Copy a token from <a\nhref=\"https://huggingface.co/settings/tokens\" target=\"_blank\">your Hugging Face\ntokens page</a> and paste it below. <br> Immediately click login after copying\nyour token or it might be stored in plain text in this notebook file. </center>"
     }
    },
    "aeaf0785fb0a4fbf83e274ed20a9dc42": {
     "model_module": "@jupyter-widgets/base",
     "model_module_version": "1.2.0",
     "model_name": "LayoutModel",
     "state": {
      "_model_module": "@jupyter-widgets/base",
      "_model_module_version": "1.2.0",
      "_model_name": "LayoutModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "LayoutView",
      "align_content": null,
      "align_items": null,
      "align_self": null,
      "border": null,
      "bottom": null,
      "display": null,
      "flex": null,
      "flex_flow": null,
      "grid_area": null,
      "grid_auto_columns": null,
      "grid_auto_flow": null,
      "grid_auto_rows": null,
      "grid_column": null,
      "grid_gap": null,
      "grid_row": null,
      "grid_template_areas": null,
      "grid_template_columns": null,
      "grid_template_rows": null,
      "height": null,
      "justify_content": null,
      "justify_items": null,
      "left": null,
      "margin": null,
      "max_height": null,
      "max_width": null,
      "min_height": null,
      "min_width": null,
      "object_fit": null,
      "object_position": null,
      "order": null,
      "overflow": null,
      "overflow_x": null,
      "overflow_y": null,
      "padding": null,
      "right": null,
      "top": null,
      "visibility": null,
      "width": null
     }
    },
    "af96a3754cc74989ac66080499851f15": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "DescriptionStyleModel",
     "state": {
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "DescriptionStyleModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "StyleView",
      "description_width": ""
     }
    },
    "b0b7f84194734e37bf1c7aad1c260a10": {
     "model_module": "@jupyter-widgets/base",
     "model_module_version": "1.2.0",
     "model_name": "LayoutModel",
     "state": {
      "_model_module": "@jupyter-widgets/base",
      "_model_module_version": "1.2.0",
      "_model_name": "LayoutModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "LayoutView",
      "align_content": null,
      "align_items": null,
      "align_self": null,
      "border": null,
      "bottom": null,
      "display": null,
      "flex": null,
      "flex_flow": null,
      "grid_area": null,
      "grid_auto_columns": null,
      "grid_auto_flow": null,
      "grid_auto_rows": null,
      "grid_column": null,
      "grid_gap": null,
      "grid_row": null,
      "grid_template_areas": null,
      "grid_template_columns": null,
      "grid_template_rows": null,
      "height": null,
      "justify_content": null,
      "justify_items": null,
      "left": null,
      "margin": null,
      "max_height": null,
      "max_width": null,
      "min_height": null,
      "min_width": null,
      "object_fit": null,
      "object_position": null,
      "order": null,
      "overflow": null,
      "overflow_x": null,
      "overflow_y": null,
      "padding": null,
      "right": null,
      "top": null,
      "visibility": null,
      "width": null
     }
    },
    "b0beadcec05744cca50ff17f9b864c3c": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "ProgressStyleModel",
     "state": {
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "ProgressStyleModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "StyleView",
      "bar_color": null,
      "description_width": ""
     }
    },
    "b172dadeb50b45608c1e43928336c3b4": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "HBoxModel",
     "state": {
      "_dom_classes": [],
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "HBoxModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/controls",
      "_view_module_version": "1.5.0",
      "_view_name": "HBoxView",
      "box_style": "",
      "children": [
       "IPY_MODEL_6753f4b11d49482cad94304136c3c314",
       "IPY_MODEL_94a7f89dc99c4d8487a0b7e3e4314c06",
       "IPY_MODEL_0bb9cb28c4d044a18f1e22afab4b6d7c"
      ],
      "layout": "IPY_MODEL_850a4df88a3c45aa930ffcdb3906f394"
     }
    },
    "b25249b8806b499b9681eacb8659f985": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "HBoxModel",
     "state": {
      "_dom_classes": [],
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "HBoxModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/controls",
      "_view_module_version": "1.5.0",
      "_view_name": "HBoxView",
      "box_style": "",
      "children": [
       "IPY_MODEL_60be614acfc14419abf5fd28a092a25b",
       "IPY_MODEL_a5d939193bcc4b2ab9c091d6028eb1f7",
       "IPY_MODEL_71f921d8b9f14f1aaf9e5acac801dcc7"
      ],
      "layout": "IPY_MODEL_b0b7f84194734e37bf1c7aad1c260a10"
     }
    },
    "b2b7a74220714c13bf7cedcef663b171": {
     "model_module": "@jupyter-widgets/base",
     "model_module_version": "1.2.0",
     "model_name": "LayoutModel",
     "state": {
      "_model_module": "@jupyter-widgets/base",
      "_model_module_version": "1.2.0",
      "_model_name": "LayoutModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "LayoutView",
      "align_content": null,
      "align_items": null,
      "align_self": null,
      "border": null,
      "bottom": null,
      "display": null,
      "flex": null,
      "flex_flow": null,
      "grid_area": null,
      "grid_auto_columns": null,
      "grid_auto_flow": null,
      "grid_auto_rows": null,
      "grid_column": null,
      "grid_gap": null,
      "grid_row": null,
      "grid_template_areas": null,
      "grid_template_columns": null,
      "grid_template_rows": null,
      "height": null,
      "justify_content": null,
      "justify_items": null,
      "left": null,
      "margin": null,
      "max_height": null,
      "max_width": null,
      "min_height": null,
      "min_width": null,
      "object_fit": null,
      "object_position": null,
      "order": null,
      "overflow": null,
      "overflow_x": null,
      "overflow_y": null,
      "padding": null,
      "right": null,
      "top": null,
      "visibility": null,
      "width": null
     }
    },
    "b443f29aaeeb43f2b527fc79c03712bc": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "DescriptionStyleModel",
     "state": {
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "DescriptionStyleModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "StyleView",
      "description_width": ""
     }
    },
    "b4519db0d32a4ed180e12283b8ed94f0": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "ProgressStyleModel",
     "state": {
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "ProgressStyleModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "StyleView",
      "bar_color": null,
      "description_width": ""
     }
    },
    "b4d1aa797f2445e1aba35dd1786db7f9": {
     "model_module": "@jupyter-widgets/base",
     "model_module_version": "1.2.0",
     "model_name": "LayoutModel",
     "state": {
      "_model_module": "@jupyter-widgets/base",
      "_model_module_version": "1.2.0",
      "_model_name": "LayoutModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "LayoutView",
      "align_content": null,
      "align_items": null,
      "align_self": null,
      "border": null,
      "bottom": null,
      "display": null,
      "flex": null,
      "flex_flow": null,
      "grid_area": null,
      "grid_auto_columns": null,
      "grid_auto_flow": null,
      "grid_auto_rows": null,
      "grid_column": null,
      "grid_gap": null,
      "grid_row": null,
      "grid_template_areas": null,
      "grid_template_columns": null,
      "grid_template_rows": null,
      "height": null,
      "justify_content": null,
      "justify_items": null,
      "left": null,
      "margin": null,
      "max_height": null,
      "max_width": null,
      "min_height": null,
      "min_width": null,
      "object_fit": null,
      "object_position": null,
      "order": null,
      "overflow": null,
      "overflow_x": null,
      "overflow_y": null,
      "padding": null,
      "right": null,
      "top": null,
      "visibility": null,
      "width": null
     }
    },
    "b722ffae626346ba8474957773ba56e5": {
     "model_module": "@jupyter-widgets/base",
     "model_module_version": "1.2.0",
     "model_name": "LayoutModel",
     "state": {
      "_model_module": "@jupyter-widgets/base",
      "_model_module_version": "1.2.0",
      "_model_name": "LayoutModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "LayoutView",
      "align_content": null,
      "align_items": null,
      "align_self": null,
      "border": null,
      "bottom": null,
      "display": null,
      "flex": null,
      "flex_flow": null,
      "grid_area": null,
      "grid_auto_columns": null,
      "grid_auto_flow": null,
      "grid_auto_rows": null,
      "grid_column": null,
      "grid_gap": null,
      "grid_row": null,
      "grid_template_areas": null,
      "grid_template_columns": null,
      "grid_template_rows": null,
      "height": null,
      "justify_content": null,
      "justify_items": null,
      "left": null,
      "margin": null,
      "max_height": null,
      "max_width": null,
      "min_height": null,
      "min_width": null,
      "object_fit": null,
      "object_position": null,
      "order": null,
      "overflow": null,
      "overflow_x": null,
      "overflow_y": null,
      "padding": null,
      "right": null,
      "top": null,
      "visibility": null,
      "width": null
     }
    },
    "b9e85752def14af48fc01f6e3522e26f": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "LabelModel",
     "state": {
      "_dom_classes": [],
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "LabelModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/controls",
      "_view_module_version": "1.5.0",
      "_view_name": "LabelView",
      "description": "",
      "description_tooltip": null,
      "layout": "IPY_MODEL_209bc5cba68a47adb43a822048c2b575",
      "placeholder": "​",
      "style": "IPY_MODEL_3098ad983889422ca1372785089bbcd6",
      "value": "Login successful"
     }
    },
    "beb43a085da345f1ac2d6bec7eb690b7": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "HTMLModel",
     "state": {
      "_dom_classes": [],
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "HTMLModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/controls",
      "_view_module_version": "1.5.0",
      "_view_name": "HTMLView",
      "description": "",
      "description_tooltip": null,
      "layout": "IPY_MODEL_7a17ce4a81924dd9a8237916ad44962f",
      "placeholder": "​",
      "style": "IPY_MODEL_855dd916702847338ed071fe873846b2",
      "value": "preprocessor_config.json: 100%"
     }
    },
    "bee0695fe4284becb27825cd0b482e9e": {
     "model_module": "@jupyter-widgets/base",
     "model_module_version": "1.2.0",
     "model_name": "LayoutModel",
     "state": {
      "_model_module": "@jupyter-widgets/base",
      "_model_module_version": "1.2.0",
      "_model_name": "LayoutModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "LayoutView",
      "align_content": null,
      "align_items": null,
      "align_self": null,
      "border": null,
      "bottom": null,
      "display": null,
      "flex": null,
      "flex_flow": null,
      "grid_area": null,
      "grid_auto_columns": null,
      "grid_auto_flow": null,
      "grid_auto_rows": null,
      "grid_column": null,
      "grid_gap": null,
      "grid_row": null,
      "grid_template_areas": null,
      "grid_template_columns": null,
      "grid_template_rows": null,
      "height": null,
      "justify_content": null,
      "justify_items": null,
      "left": null,
      "margin": null,
      "max_height": null,
      "max_width": null,
      "min_height": null,
      "min_width": null,
      "object_fit": null,
      "object_position": null,
      "order": null,
      "overflow": null,
      "overflow_x": null,
      "overflow_y": null,
      "padding": null,
      "right": null,
      "top": null,
      "visibility": null,
      "width": null
     }
    },
    "c54f0d93b2da449eab0bf9586d2f6257": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "HTMLModel",
     "state": {
      "_dom_classes": [],
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "HTMLModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/controls",
      "_view_module_version": "1.5.0",
      "_view_name": "HTMLView",
      "description": "",
      "description_tooltip": null,
      "layout": "IPY_MODEL_8b972f13e66f4e74aad6ef81347d9adc",
      "placeholder": "​",
      "style": "IPY_MODEL_2917c19ada3843cda23399ea6ec55ba7",
      "value": " 162/162 [00:00&lt;00:00, 10.2kB/s]"
     }
    },
    "c59745a9c9934bdfafb36acdf0b76d6f": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "HBoxModel",
     "state": {
      "_dom_classes": [],
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "HBoxModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/controls",
      "_view_module_version": "1.5.0",
      "_view_name": "HBoxView",
      "box_style": "",
      "children": [
       "IPY_MODEL_9bf765dea2bf4e148d5888ba3ecbc532",
       "IPY_MODEL_3ae8fb07b61442fb955a0cd04c5d9d71",
       "IPY_MODEL_a31b383a10e44033844919b4f763b003"
      ],
      "layout": "IPY_MODEL_92818ef1790e46fd92fd3ba8bdca2928"
     }
    },
    "c7c878d9fb6944d38fe088e2173cfbd4": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "FloatProgressModel",
     "state": {
      "_dom_classes": [],
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "FloatProgressModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/controls",
      "_view_module_version": "1.5.0",
      "_view_name": "ProgressView",
      "bar_style": "success",
      "description": "",
      "description_tooltip": null,
      "layout": "IPY_MODEL_888642c2e99648b3b3efc625222bb9e1",
      "max": 80194,
      "min": 0,
      "orientation": "horizontal",
      "style": "IPY_MODEL_f957398b44584ab8afe09e44bf6b5794",
      "value": 80194
     }
    },
    "c8625f177f7348d4b604270535895ff6": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "HBoxModel",
     "state": {
      "_dom_classes": [],
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "HBoxModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/controls",
      "_view_module_version": "1.5.0",
      "_view_name": "HBoxView",
      "box_style": "",
      "children": [
       "IPY_MODEL_3333ea6679a84bb18d8e2e06ed829d1e",
       "IPY_MODEL_ff2f24473f074dc5835b73a9eb60b543",
       "IPY_MODEL_84e07752fa684ae5bdc651f3240e56cf"
      ],
      "layout": "IPY_MODEL_ffa26635efac424cb74fe6ae71a35f80"
     }
    },
    "c914ce2927ce426786a0ff0cd1545215": {
     "model_module": "@jupyter-widgets/base",
     "model_module_version": "1.2.0",
     "model_name": "LayoutModel",
     "state": {
      "_model_module": "@jupyter-widgets/base",
      "_model_module_version": "1.2.0",
      "_model_name": "LayoutModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "LayoutView",
      "align_content": null,
      "align_items": null,
      "align_self": null,
      "border": null,
      "bottom": null,
      "display": null,
      "flex": null,
      "flex_flow": null,
      "grid_area": null,
      "grid_auto_columns": null,
      "grid_auto_flow": null,
      "grid_auto_rows": null,
      "grid_column": null,
      "grid_gap": null,
      "grid_row": null,
      "grid_template_areas": null,
      "grid_template_columns": null,
      "grid_template_rows": null,
      "height": null,
      "justify_content": null,
      "justify_items": null,
      "left": null,
      "margin": null,
      "max_height": null,
      "max_width": null,
      "min_height": null,
      "min_width": null,
      "object_fit": null,
      "object_position": null,
      "order": null,
      "overflow": null,
      "overflow_x": null,
      "overflow_y": null,
      "padding": null,
      "right": null,
      "top": null,
      "visibility": null,
      "width": null
     }
    },
    "ca0c772bb8b84b06aa7e0de01f1755b8": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "ProgressStyleModel",
     "state": {
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "ProgressStyleModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "StyleView",
      "bar_color": null,
      "description_width": ""
     }
    },
    "ca52f779392e4a0db07b0b078b43af50": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "DescriptionStyleModel",
     "state": {
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "DescriptionStyleModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "StyleView",
      "description_width": ""
     }
    },
    "ccd3e7c23b0d45f8bf6faee185588e6a": {
     "model_module": "@jupyter-widgets/base",
     "model_module_version": "1.2.0",
     "model_name": "LayoutModel",
     "state": {
      "_model_module": "@jupyter-widgets/base",
      "_model_module_version": "1.2.0",
      "_model_name": "LayoutModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "LayoutView",
      "align_content": null,
      "align_items": null,
      "align_self": null,
      "border": null,
      "bottom": null,
      "display": null,
      "flex": null,
      "flex_flow": null,
      "grid_area": null,
      "grid_auto_columns": null,
      "grid_auto_flow": null,
      "grid_auto_rows": null,
      "grid_column": null,
      "grid_gap": null,
      "grid_row": null,
      "grid_template_areas": null,
      "grid_template_columns": null,
      "grid_template_rows": null,
      "height": null,
      "justify_content": null,
      "justify_items": null,
      "left": null,
      "margin": null,
      "max_height": null,
      "max_width": null,
      "min_height": null,
      "min_width": null,
      "object_fit": null,
      "object_position": null,
      "order": null,
      "overflow": null,
      "overflow_x": null,
      "overflow_y": null,
      "padding": null,
      "right": null,
      "top": null,
      "visibility": null,
      "width": null
     }
    },
    "ce567f04446e4948b592f0e0f6674b3f": {
     "model_module": "@jupyter-widgets/base",
     "model_module_version": "1.2.0",
     "model_name": "LayoutModel",
     "state": {
      "_model_module": "@jupyter-widgets/base",
      "_model_module_version": "1.2.0",
      "_model_name": "LayoutModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "LayoutView",
      "align_content": null,
      "align_items": null,
      "align_self": null,
      "border": null,
      "bottom": null,
      "display": null,
      "flex": null,
      "flex_flow": null,
      "grid_area": null,
      "grid_auto_columns": null,
      "grid_auto_flow": null,
      "grid_auto_rows": null,
      "grid_column": null,
      "grid_gap": null,
      "grid_row": null,
      "grid_template_areas": null,
      "grid_template_columns": null,
      "grid_template_rows": null,
      "height": null,
      "justify_content": null,
      "justify_items": null,
      "left": null,
      "margin": null,
      "max_height": null,
      "max_width": null,
      "min_height": null,
      "min_width": null,
      "object_fit": null,
      "object_position": null,
      "order": null,
      "overflow": null,
      "overflow_x": null,
      "overflow_y": null,
      "padding": null,
      "right": null,
      "top": null,
      "visibility": null,
      "width": null
     }
    },
    "cf9ac11864984bcbb7be3ade707dcfc9": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "DescriptionStyleModel",
     "state": {
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "DescriptionStyleModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "StyleView",
      "description_width": ""
     }
    },
    "cfea82b2647c46a9866f74cee7487a4d": {
     "model_module": "@jupyter-widgets/base",
     "model_module_version": "1.2.0",
     "model_name": "LayoutModel",
     "state": {
      "_model_module": "@jupyter-widgets/base",
      "_model_module_version": "1.2.0",
      "_model_name": "LayoutModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "LayoutView",
      "align_content": null,
      "align_items": null,
      "align_self": null,
      "border": null,
      "bottom": null,
      "display": null,
      "flex": null,
      "flex_flow": null,
      "grid_area": null,
      "grid_auto_columns": null,
      "grid_auto_flow": null,
      "grid_auto_rows": null,
      "grid_column": null,
      "grid_gap": null,
      "grid_row": null,
      "grid_template_areas": null,
      "grid_template_columns": null,
      "grid_template_rows": null,
      "height": null,
      "justify_content": null,
      "justify_items": null,
      "left": null,
      "margin": null,
      "max_height": null,
      "max_width": null,
      "min_height": null,
      "min_width": null,
      "object_fit": null,
      "object_position": null,
      "order": null,
      "overflow": null,
      "overflow_x": null,
      "overflow_y": null,
      "padding": null,
      "right": null,
      "top": null,
      "visibility": null,
      "width": null
     }
    },
    "d0948d82b60d4258b7646337ac537b22": {
     "model_module": "@jupyter-widgets/base",
     "model_module_version": "1.2.0",
     "model_name": "LayoutModel",
     "state": {
      "_model_module": "@jupyter-widgets/base",
      "_model_module_version": "1.2.0",
      "_model_name": "LayoutModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "LayoutView",
      "align_content": null,
      "align_items": null,
      "align_self": null,
      "border": null,
      "bottom": null,
      "display": null,
      "flex": null,
      "flex_flow": null,
      "grid_area": null,
      "grid_auto_columns": null,
      "grid_auto_flow": null,
      "grid_auto_rows": null,
      "grid_column": null,
      "grid_gap": null,
      "grid_row": null,
      "grid_template_areas": null,
      "grid_template_columns": null,
      "grid_template_rows": null,
      "height": null,
      "justify_content": null,
      "justify_items": null,
      "left": null,
      "margin": null,
      "max_height": null,
      "max_width": null,
      "min_height": null,
      "min_width": null,
      "object_fit": null,
      "object_position": null,
      "order": null,
      "overflow": null,
      "overflow_x": null,
      "overflow_y": null,
      "padding": null,
      "right": null,
      "top": null,
      "visibility": null,
      "width": null
     }
    },
    "d27b7d98259d48aaa2103fd95b47de6b": {
     "model_module": "@jupyter-widgets/base",
     "model_module_version": "1.2.0",
     "model_name": "LayoutModel",
     "state": {
      "_model_module": "@jupyter-widgets/base",
      "_model_module_version": "1.2.0",
      "_model_name": "LayoutModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "LayoutView",
      "align_content": null,
      "align_items": null,
      "align_self": null,
      "border": null,
      "bottom": null,
      "display": null,
      "flex": null,
      "flex_flow": null,
      "grid_area": null,
      "grid_auto_columns": null,
      "grid_auto_flow": null,
      "grid_auto_rows": null,
      "grid_column": null,
      "grid_gap": null,
      "grid_row": null,
      "grid_template_areas": null,
      "grid_template_columns": null,
      "grid_template_rows": null,
      "height": null,
      "justify_content": null,
      "justify_items": null,
      "left": null,
      "margin": null,
      "max_height": null,
      "max_width": null,
      "min_height": null,
      "min_width": null,
      "object_fit": null,
      "object_position": null,
      "order": null,
      "overflow": null,
      "overflow_x": null,
      "overflow_y": null,
      "padding": null,
      "right": null,
      "top": null,
      "visibility": null,
      "width": null
     }
    },
    "d39062e059884a7da765a150c50978c0": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "LabelModel",
     "state": {
      "_dom_classes": [],
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "LabelModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/controls",
      "_view_module_version": "1.5.0",
      "_view_name": "LabelView",
      "description": "",
      "description_tooltip": null,
      "layout": "IPY_MODEL_3fcfc05fc84340f09e3049cfe70e05b0",
      "placeholder": "​",
      "style": "IPY_MODEL_367804f8dd434dedb86cc6b6038ecfa4",
      "value": "Token is valid (permission: write)."
     }
    },
    "d412a30e27234e3ebef17717f6f41707": {
     "model_module": "@jupyter-widgets/base",
     "model_module_version": "1.2.0",
     "model_name": "LayoutModel",
     "state": {
      "_model_module": "@jupyter-widgets/base",
      "_model_module_version": "1.2.0",
      "_model_name": "LayoutModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "LayoutView",
      "align_content": null,
      "align_items": null,
      "align_self": null,
      "border": null,
      "bottom": null,
      "display": null,
      "flex": null,
      "flex_flow": null,
      "grid_area": null,
      "grid_auto_columns": null,
      "grid_auto_flow": null,
      "grid_auto_rows": null,
      "grid_column": null,
      "grid_gap": null,
      "grid_row": null,
      "grid_template_areas": null,
      "grid_template_columns": null,
      "grid_template_rows": null,
      "height": null,
      "justify_content": null,
      "justify_items": null,
      "left": null,
      "margin": null,
      "max_height": null,
      "max_width": null,
      "min_height": null,
      "min_width": null,
      "object_fit": null,
      "object_position": null,
      "order": null,
      "overflow": null,
      "overflow_x": null,
      "overflow_y": null,
      "padding": null,
      "right": null,
      "top": null,
      "visibility": null,
      "width": null
     }
    },
    "d6f7f2257acc44bf854bf20b44c7596f": {
     "model_module": "@jupyter-widgets/base",
     "model_module_version": "1.2.0",
     "model_name": "LayoutModel",
     "state": {
      "_model_module": "@jupyter-widgets/base",
      "_model_module_version": "1.2.0",
      "_model_name": "LayoutModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "LayoutView",
      "align_content": null,
      "align_items": null,
      "align_self": null,
      "border": null,
      "bottom": null,
      "display": null,
      "flex": null,
      "flex_flow": null,
      "grid_area": null,
      "grid_auto_columns": null,
      "grid_auto_flow": null,
      "grid_auto_rows": null,
      "grid_column": null,
      "grid_gap": null,
      "grid_row": null,
      "grid_template_areas": null,
      "grid_template_columns": null,
      "grid_template_rows": null,
      "height": null,
      "justify_content": null,
      "justify_items": null,
      "left": null,
      "margin": null,
      "max_height": null,
      "max_width": null,
      "min_height": null,
      "min_width": null,
      "object_fit": null,
      "object_position": null,
      "order": null,
      "overflow": null,
      "overflow_x": null,
      "overflow_y": null,
      "padding": null,
      "right": null,
      "top": null,
      "visibility": null,
      "width": null
     }
    },
    "d71260daa1114bfbbd04e9cc232dcf53": {
     "model_module": "@jupyter-widgets/base",
     "model_module_version": "1.2.0",
     "model_name": "LayoutModel",
     "state": {
      "_model_module": "@jupyter-widgets/base",
      "_model_module_version": "1.2.0",
      "_model_name": "LayoutModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "LayoutView",
      "align_content": null,
      "align_items": null,
      "align_self": null,
      "border": null,
      "bottom": null,
      "display": null,
      "flex": null,
      "flex_flow": null,
      "grid_area": null,
      "grid_auto_columns": null,
      "grid_auto_flow": null,
      "grid_auto_rows": null,
      "grid_column": null,
      "grid_gap": null,
      "grid_row": null,
      "grid_template_areas": null,
      "grid_template_columns": null,
      "grid_template_rows": null,
      "height": null,
      "justify_content": null,
      "justify_items": null,
      "left": null,
      "margin": null,
      "max_height": null,
      "max_width": null,
      "min_height": null,
      "min_width": null,
      "object_fit": null,
      "object_position": null,
      "order": null,
      "overflow": null,
      "overflow_x": null,
      "overflow_y": null,
      "padding": null,
      "right": null,
      "top": null,
      "visibility": null,
      "width": null
     }
    },
    "d780a5b8295948dd94277024b94450c7": {
     "model_module": "@jupyter-widgets/base",
     "model_module_version": "1.2.0",
     "model_name": "LayoutModel",
     "state": {
      "_model_module": "@jupyter-widgets/base",
      "_model_module_version": "1.2.0",
      "_model_name": "LayoutModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "LayoutView",
      "align_content": null,
      "align_items": null,
      "align_self": null,
      "border": null,
      "bottom": null,
      "display": null,
      "flex": null,
      "flex_flow": null,
      "grid_area": null,
      "grid_auto_columns": null,
      "grid_auto_flow": null,
      "grid_auto_rows": null,
      "grid_column": null,
      "grid_gap": null,
      "grid_row": null,
      "grid_template_areas": null,
      "grid_template_columns": null,
      "grid_template_rows": null,
      "height": null,
      "justify_content": null,
      "justify_items": null,
      "left": null,
      "margin": null,
      "max_height": null,
      "max_width": null,
      "min_height": null,
      "min_width": null,
      "object_fit": null,
      "object_position": null,
      "order": null,
      "overflow": null,
      "overflow_x": null,
      "overflow_y": null,
      "padding": null,
      "right": null,
      "top": null,
      "visibility": null,
      "width": null
     }
    },
    "da5556423b704b938ae8e305b08daee6": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "HTMLModel",
     "state": {
      "_dom_classes": [],
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "HTMLModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/controls",
      "_view_module_version": "1.5.0",
      "_view_name": "HTMLView",
      "description": "",
      "description_tooltip": null,
      "layout": "IPY_MODEL_24ed4fe143e24d6091304da92ad02193",
      "placeholder": "​",
      "style": "IPY_MODEL_ee855a532280410eb998a8c446e761fa",
      "value": "tokenizer_config.json: 100%"
     }
    },
    "da5830bc484f40bd8f54ddb282e4554d": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "DescriptionStyleModel",
     "state": {
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "DescriptionStyleModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "StyleView",
      "description_width": ""
     }
    },
    "da8f2de0427d408cae355f2b35dd889b": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "DescriptionStyleModel",
     "state": {
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "DescriptionStyleModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "StyleView",
      "description_width": ""
     }
    },
    "dbb6d44f37a74edb9bb2b80e4b8af16a": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "ProgressStyleModel",
     "state": {
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "ProgressStyleModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "StyleView",
      "bar_color": null,
      "description_width": ""
     }
    },
    "dc5501aa47a5423c92827bdf4e0d2c63": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "HTMLModel",
     "state": {
      "_dom_classes": [],
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "HTMLModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/controls",
      "_view_module_version": "1.5.0",
      "_view_name": "HTMLView",
      "description": "",
      "description_tooltip": null,
      "layout": "IPY_MODEL_1ca53c44536946c29161ff07982a056f",
      "placeholder": "​",
      "style": "IPY_MODEL_e0854a2ebd70436f90042da60172c422",
      "value": " 2.48k/2.48k [00:00&lt;00:00, 161kB/s]"
     }
    },
    "df347a18ec3f4b5098f395a522391837": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "DescriptionStyleModel",
     "state": {
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "DescriptionStyleModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "StyleView",
      "description_width": ""
     }
    },
    "e01fde47f6a7485e87e7da717d245ba6": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "DescriptionStyleModel",
     "state": {
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "DescriptionStyleModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "StyleView",
      "description_width": ""
     }
    },
    "e0854a2ebd70436f90042da60172c422": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "DescriptionStyleModel",
     "state": {
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "DescriptionStyleModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "StyleView",
      "description_width": ""
     }
    },
    "e0927527d6da40a895ee96d7d357ad56": {
     "model_module": "@jupyter-widgets/base",
     "model_module_version": "1.2.0",
     "model_name": "LayoutModel",
     "state": {
      "_model_module": "@jupyter-widgets/base",
      "_model_module_version": "1.2.0",
      "_model_name": "LayoutModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "LayoutView",
      "align_content": null,
      "align_items": null,
      "align_self": null,
      "border": null,
      "bottom": null,
      "display": null,
      "flex": null,
      "flex_flow": null,
      "grid_area": null,
      "grid_auto_columns": null,
      "grid_auto_flow": null,
      "grid_auto_rows": null,
      "grid_column": null,
      "grid_gap": null,
      "grid_row": null,
      "grid_template_areas": null,
      "grid_template_columns": null,
      "grid_template_rows": null,
      "height": null,
      "justify_content": null,
      "justify_items": null,
      "left": null,
      "margin": null,
      "max_height": null,
      "max_width": null,
      "min_height": null,
      "min_width": null,
      "object_fit": null,
      "object_position": null,
      "order": null,
      "overflow": null,
      "overflow_x": null,
      "overflow_y": null,
      "padding": null,
      "right": null,
      "top": null,
      "visibility": null,
      "width": null
     }
    },
    "e19b9ca8a019453291d84640d66f8bb1": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "DescriptionStyleModel",
     "state": {
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "DescriptionStyleModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "StyleView",
      "description_width": ""
     }
    },
    "e3c53ac8e7d343ea87759a40e6c4f86a": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "HTMLModel",
     "state": {
      "_dom_classes": [],
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "HTMLModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/controls",
      "_view_module_version": "1.5.0",
      "_view_name": "HTMLView",
      "description": "",
      "description_tooltip": null,
      "layout": "IPY_MODEL_fa3dbe5d8a9a49fcb2e4aee00453a61a",
      "placeholder": "​",
      "style": "IPY_MODEL_0b74af83c3904abd9ef8848fb2983caf",
      "value": " 3.51G/3.51G [00:19&lt;00:00, 217MB/s]"
     }
    },
    "e3d73b54071e400f8b2af56557fd2d76": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "FloatProgressModel",
     "state": {
      "_dom_classes": [],
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "FloatProgressModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/controls",
      "_view_module_version": "1.5.0",
      "_view_name": "ProgressView",
      "bar_style": "success",
      "description": "",
      "description_tooltip": null,
      "layout": "IPY_MODEL_62dd346f895147429e54eb6cfe20b703",
      "max": 368,
      "min": 0,
      "orientation": "horizontal",
      "style": "IPY_MODEL_16658978abf648338631fa8e6314b9a6",
      "value": 368
     }
    },
    "e41b5070db864d259f9c0cd3f14742e0": {
     "model_module": "@jupyter-widgets/base",
     "model_module_version": "1.2.0",
     "model_name": "LayoutModel",
     "state": {
      "_model_module": "@jupyter-widgets/base",
      "_model_module_version": "1.2.0",
      "_model_name": "LayoutModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "LayoutView",
      "align_content": null,
      "align_items": null,
      "align_self": null,
      "border": null,
      "bottom": null,
      "display": null,
      "flex": null,
      "flex_flow": null,
      "grid_area": null,
      "grid_auto_columns": null,
      "grid_auto_flow": null,
      "grid_auto_rows": null,
      "grid_column": null,
      "grid_gap": null,
      "grid_row": null,
      "grid_template_areas": null,
      "grid_template_columns": null,
      "grid_template_rows": null,
      "height": null,
      "justify_content": null,
      "justify_items": null,
      "left": null,
      "margin": null,
      "max_height": null,
      "max_width": null,
      "min_height": null,
      "min_width": null,
      "object_fit": null,
      "object_position": null,
      "order": null,
      "overflow": null,
      "overflow_x": null,
      "overflow_y": null,
      "padding": null,
      "right": null,
      "top": null,
      "visibility": null,
      "width": null
     }
    },
    "e49ad9b2dbef4890a4525dfff3bf6961": {
     "model_module": "@jupyter-widgets/base",
     "model_module_version": "1.2.0",
     "model_name": "LayoutModel",
     "state": {
      "_model_module": "@jupyter-widgets/base",
      "_model_module_version": "1.2.0",
      "_model_name": "LayoutModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "LayoutView",
      "align_content": null,
      "align_items": null,
      "align_self": null,
      "border": null,
      "bottom": null,
      "display": null,
      "flex": null,
      "flex_flow": null,
      "grid_area": null,
      "grid_auto_columns": null,
      "grid_auto_flow": null,
      "grid_auto_rows": null,
      "grid_column": null,
      "grid_gap": null,
      "grid_row": null,
      "grid_template_areas": null,
      "grid_template_columns": null,
      "grid_template_rows": null,
      "height": null,
      "justify_content": null,
      "justify_items": null,
      "left": null,
      "margin": null,
      "max_height": null,
      "max_width": null,
      "min_height": null,
      "min_width": null,
      "object_fit": null,
      "object_position": null,
      "order": null,
      "overflow": null,
      "overflow_x": null,
      "overflow_y": null,
      "padding": null,
      "right": null,
      "top": null,
      "visibility": null,
      "width": null
     }
    },
    "e4e749dcf87040d8a29df01646d9a0c3": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "HTMLModel",
     "state": {
      "_dom_classes": [],
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "HTMLModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/controls",
      "_view_module_version": "1.5.0",
      "_view_name": "HTMLView",
      "description": "",
      "description_tooltip": null,
      "layout": "IPY_MODEL_d780a5b8295948dd94277024b94450c7",
      "placeholder": "​",
      "style": "IPY_MODEL_ca52f779392e4a0db07b0b078b43af50",
      "value": "model.safetensors.index.json: 100%"
     }
    },
    "e5da6dd6937b49b59081e31b63466ac1": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "DescriptionStyleModel",
     "state": {
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "DescriptionStyleModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "StyleView",
      "description_width": ""
     }
    },
    "e814a1aef5614ab5ba6c1d560e99969a": {
     "model_module": "@jupyter-widgets/base",
     "model_module_version": "1.2.0",
     "model_name": "LayoutModel",
     "state": {
      "_model_module": "@jupyter-widgets/base",
      "_model_module_version": "1.2.0",
      "_model_name": "LayoutModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "LayoutView",
      "align_content": null,
      "align_items": null,
      "align_self": null,
      "border": null,
      "bottom": null,
      "display": null,
      "flex": null,
      "flex_flow": null,
      "grid_area": null,
      "grid_auto_columns": null,
      "grid_auto_flow": null,
      "grid_auto_rows": null,
      "grid_column": null,
      "grid_gap": null,
      "grid_row": null,
      "grid_template_areas": null,
      "grid_template_columns": null,
      "grid_template_rows": null,
      "height": null,
      "justify_content": null,
      "justify_items": null,
      "left": null,
      "margin": null,
      "max_height": null,
      "max_width": null,
      "min_height": null,
      "min_width": null,
      "object_fit": null,
      "object_position": null,
      "order": null,
      "overflow": null,
      "overflow_x": null,
      "overflow_y": null,
      "padding": null,
      "right": null,
      "top": null,
      "visibility": null,
      "width": null
     }
    },
    "e91a09281d3d48cca34f260173da2d16": {
     "model_module": "@jupyter-widgets/base",
     "model_module_version": "1.2.0",
     "model_name": "LayoutModel",
     "state": {
      "_model_module": "@jupyter-widgets/base",
      "_model_module_version": "1.2.0",
      "_model_name": "LayoutModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "LayoutView",
      "align_content": null,
      "align_items": null,
      "align_self": null,
      "border": null,
      "bottom": null,
      "display": null,
      "flex": null,
      "flex_flow": null,
      "grid_area": null,
      "grid_auto_columns": null,
      "grid_auto_flow": null,
      "grid_auto_rows": null,
      "grid_column": null,
      "grid_gap": null,
      "grid_row": null,
      "grid_template_areas": null,
      "grid_template_columns": null,
      "grid_template_rows": null,
      "height": null,
      "justify_content": null,
      "justify_items": null,
      "left": null,
      "margin": null,
      "max_height": null,
      "max_width": null,
      "min_height": null,
      "min_width": null,
      "object_fit": null,
      "object_position": null,
      "order": null,
      "overflow": null,
      "overflow_x": null,
      "overflow_y": null,
      "padding": null,
      "right": null,
      "top": null,
      "visibility": null,
      "width": null
     }
    },
    "eb5f03fa32514498b66edda1b5b8c0be": {
     "model_module": "@jupyter-widgets/base",
     "model_module_version": "1.2.0",
     "model_name": "LayoutModel",
     "state": {
      "_model_module": "@jupyter-widgets/base",
      "_model_module_version": "1.2.0",
      "_model_name": "LayoutModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "LayoutView",
      "align_content": null,
      "align_items": null,
      "align_self": null,
      "border": null,
      "bottom": null,
      "display": null,
      "flex": null,
      "flex_flow": null,
      "grid_area": null,
      "grid_auto_columns": null,
      "grid_auto_flow": null,
      "grid_auto_rows": null,
      "grid_column": null,
      "grid_gap": null,
      "grid_row": null,
      "grid_template_areas": null,
      "grid_template_columns": null,
      "grid_template_rows": null,
      "height": null,
      "justify_content": null,
      "justify_items": null,
      "left": null,
      "margin": null,
      "max_height": null,
      "max_width": null,
      "min_height": null,
      "min_width": null,
      "object_fit": null,
      "object_position": null,
      "order": null,
      "overflow": null,
      "overflow_x": null,
      "overflow_y": null,
      "padding": null,
      "right": null,
      "top": null,
      "visibility": null,
      "width": null
     }
    },
    "ed36240f86f5402cb630027e714f917b": {
     "model_module": "@jupyter-widgets/base",
     "model_module_version": "1.2.0",
     "model_name": "LayoutModel",
     "state": {
      "_model_module": "@jupyter-widgets/base",
      "_model_module_version": "1.2.0",
      "_model_name": "LayoutModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "LayoutView",
      "align_content": null,
      "align_items": null,
      "align_self": null,
      "border": null,
      "bottom": null,
      "display": null,
      "flex": null,
      "flex_flow": null,
      "grid_area": null,
      "grid_auto_columns": null,
      "grid_auto_flow": null,
      "grid_auto_rows": null,
      "grid_column": null,
      "grid_gap": null,
      "grid_row": null,
      "grid_template_areas": null,
      "grid_template_columns": null,
      "grid_template_rows": null,
      "height": null,
      "justify_content": null,
      "justify_items": null,
      "left": null,
      "margin": null,
      "max_height": null,
      "max_width": null,
      "min_height": null,
      "min_width": null,
      "object_fit": null,
      "object_position": null,
      "order": null,
      "overflow": null,
      "overflow_x": null,
      "overflow_y": null,
      "padding": null,
      "right": null,
      "top": null,
      "visibility": null,
      "width": null
     }
    },
    "edee44fd2a324f60b42fcc42940f1196": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "DescriptionStyleModel",
     "state": {
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "DescriptionStyleModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "StyleView",
      "description_width": ""
     }
    },
    "ee855a532280410eb998a8c446e761fa": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "DescriptionStyleModel",
     "state": {
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "DescriptionStyleModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "StyleView",
      "description_width": ""
     }
    },
    "ef648c232d4e4731ae1f1219c306c896": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "ProgressStyleModel",
     "state": {
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "ProgressStyleModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "StyleView",
      "bar_color": null,
      "description_width": ""
     }
    },
    "f05e7d4f70684ea08f488713660fe8ff": {
     "model_module": "@jupyter-widgets/base",
     "model_module_version": "1.2.0",
     "model_name": "LayoutModel",
     "state": {
      "_model_module": "@jupyter-widgets/base",
      "_model_module_version": "1.2.0",
      "_model_name": "LayoutModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "LayoutView",
      "align_content": null,
      "align_items": null,
      "align_self": null,
      "border": null,
      "bottom": null,
      "display": null,
      "flex": null,
      "flex_flow": null,
      "grid_area": null,
      "grid_auto_columns": null,
      "grid_auto_flow": null,
      "grid_auto_rows": null,
      "grid_column": null,
      "grid_gap": null,
      "grid_row": null,
      "grid_template_areas": null,
      "grid_template_columns": null,
      "grid_template_rows": null,
      "height": null,
      "justify_content": null,
      "justify_items": null,
      "left": null,
      "margin": null,
      "max_height": null,
      "max_width": null,
      "min_height": null,
      "min_width": null,
      "object_fit": null,
      "object_position": null,
      "order": null,
      "overflow": null,
      "overflow_x": null,
      "overflow_y": null,
      "padding": null,
      "right": null,
      "top": null,
      "visibility": null,
      "width": null
     }
    },
    "f44332a81d62435481df9cb278c5c4e8": {
     "model_module": "@jupyter-widgets/base",
     "model_module_version": "1.2.0",
     "model_name": "LayoutModel",
     "state": {
      "_model_module": "@jupyter-widgets/base",
      "_model_module_version": "1.2.0",
      "_model_name": "LayoutModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "LayoutView",
      "align_content": null,
      "align_items": null,
      "align_self": null,
      "border": null,
      "bottom": null,
      "display": null,
      "flex": null,
      "flex_flow": null,
      "grid_area": null,
      "grid_auto_columns": null,
      "grid_auto_flow": null,
      "grid_auto_rows": null,
      "grid_column": null,
      "grid_gap": null,
      "grid_row": null,
      "grid_template_areas": null,
      "grid_template_columns": null,
      "grid_template_rows": null,
      "height": null,
      "justify_content": null,
      "justify_items": null,
      "left": null,
      "margin": null,
      "max_height": null,
      "max_width": null,
      "min_height": null,
      "min_width": null,
      "object_fit": null,
      "object_position": null,
      "order": null,
      "overflow": null,
      "overflow_x": null,
      "overflow_y": null,
      "padding": null,
      "right": null,
      "top": null,
      "visibility": null,
      "width": null
     }
    },
    "f4f5257a4ac540a9bc74d2814f0d38af": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "HTMLModel",
     "state": {
      "_dom_classes": [],
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "HTMLModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/controls",
      "_view_module_version": "1.5.0",
      "_view_name": "HTMLView",
      "description": "",
      "description_tooltip": null,
      "layout": "IPY_MODEL_e91a09281d3d48cca34f260173da2d16",
      "placeholder": "​",
      "style": "IPY_MODEL_8bbc41932a6e4ef2b5933a612d6c93d6",
      "value": "model.safetensors: 100%"
     }
    },
    "f4f68fbd0758458c8bf5a70fa2225ac2": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "HBoxModel",
     "state": {
      "_dom_classes": [],
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "HBoxModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/controls",
      "_view_module_version": "1.5.0",
      "_view_name": "HBoxView",
      "box_style": "",
      "children": [
       "IPY_MODEL_da5556423b704b938ae8e305b08daee6",
       "IPY_MODEL_394ffb8ac93e4e34a495872df474fe93",
       "IPY_MODEL_dc5501aa47a5423c92827bdf4e0d2c63"
      ],
      "layout": "IPY_MODEL_e814a1aef5614ab5ba6c1d560e99969a"
     }
    },
    "f58199d4cb9b4dc4938016aef61f90ab": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "DescriptionStyleModel",
     "state": {
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "DescriptionStyleModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "StyleView",
      "description_width": ""
     }
    },
    "f68988b27c7241abaebde105c35e9659": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "HBoxModel",
     "state": {
      "_dom_classes": [],
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "HBoxModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/controls",
      "_view_module_version": "1.5.0",
      "_view_name": "HBoxView",
      "box_style": "",
      "children": [
       "IPY_MODEL_beb43a085da345f1ac2d6bec7eb690b7",
       "IPY_MODEL_e3d73b54071e400f8b2af56557fd2d76",
       "IPY_MODEL_81df48ada913475e97ab13c6c81ef1fa"
      ],
      "layout": "IPY_MODEL_e0927527d6da40a895ee96d7d357ad56"
     }
    },
    "f7d6ef365bbc4e479b7086dc3867f308": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "HTMLModel",
     "state": {
      "_dom_classes": [],
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "HTMLModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/controls",
      "_view_module_version": "1.5.0",
      "_view_name": "HTMLView",
      "description": "",
      "description_tooltip": null,
      "layout": "IPY_MODEL_986c17897b0c446184fd9c44af5919c3",
      "placeholder": "​",
      "style": "IPY_MODEL_8e7896f4d12c45159cf713a3a8682570",
      "value": "Loading checkpoint shards: 100%"
     }
    },
    "f92e2159a4344d368a09220d4151cf76": {
     "model_module": "@jupyter-widgets/base",
     "model_module_version": "1.2.0",
     "model_name": "LayoutModel",
     "state": {
      "_model_module": "@jupyter-widgets/base",
      "_model_module_version": "1.2.0",
      "_model_name": "LayoutModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "LayoutView",
      "align_content": null,
      "align_items": null,
      "align_self": null,
      "border": null,
      "bottom": null,
      "display": null,
      "flex": null,
      "flex_flow": null,
      "grid_area": null,
      "grid_auto_columns": null,
      "grid_auto_flow": null,
      "grid_auto_rows": null,
      "grid_column": null,
      "grid_gap": null,
      "grid_row": null,
      "grid_template_areas": null,
      "grid_template_columns": null,
      "grid_template_rows": null,
      "height": null,
      "justify_content": null,
      "justify_items": null,
      "left": null,
      "margin": null,
      "max_height": null,
      "max_width": null,
      "min_height": null,
      "min_width": null,
      "object_fit": null,
      "object_position": null,
      "order": null,
      "overflow": null,
      "overflow_x": null,
      "overflow_y": null,
      "padding": null,
      "right": null,
      "top": null,
      "visibility": null,
      "width": null
     }
    },
    "f957398b44584ab8afe09e44bf6b5794": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "ProgressStyleModel",
     "state": {
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "ProgressStyleModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "StyleView",
      "bar_color": null,
      "description_width": ""
     }
    },
    "fa3dbe5d8a9a49fcb2e4aee00453a61a": {
     "model_module": "@jupyter-widgets/base",
     "model_module_version": "1.2.0",
     "model_name": "LayoutModel",
     "state": {
      "_model_module": "@jupyter-widgets/base",
      "_model_module_version": "1.2.0",
      "_model_name": "LayoutModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "LayoutView",
      "align_content": null,
      "align_items": null,
      "align_self": null,
      "border": null,
      "bottom": null,
      "display": null,
      "flex": null,
      "flex_flow": null,
      "grid_area": null,
      "grid_auto_columns": null,
      "grid_auto_flow": null,
      "grid_auto_rows": null,
      "grid_column": null,
      "grid_gap": null,
      "grid_row": null,
      "grid_template_areas": null,
      "grid_template_columns": null,
      "grid_template_rows": null,
      "height": null,
      "justify_content": null,
      "justify_items": null,
      "left": null,
      "margin": null,
      "max_height": null,
      "max_width": null,
      "min_height": null,
      "min_width": null,
      "object_fit": null,
      "object_position": null,
      "order": null,
      "overflow": null,
      "overflow_x": null,
      "overflow_y": null,
      "padding": null,
      "right": null,
      "top": null,
      "visibility": null,
      "width": null
     }
    },
    "fa672bb34ad8439d845449f5df64ecc0": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "LabelModel",
     "state": {
      "_dom_classes": [],
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "LabelModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/controls",
      "_view_module_version": "1.5.0",
      "_view_name": "LabelView",
      "description": "",
      "description_tooltip": null,
      "layout": "IPY_MODEL_5d9cf27a11a24ff4a30730271495defb",
      "placeholder": "​",
      "style": "IPY_MODEL_e19b9ca8a019453291d84640d66f8bb1",
      "value": "Your token has been saved to /root/.cache/huggingface/token"
     }
    },
    "fdc104a103c5433ab5748e37e3bc386f": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "DescriptionStyleModel",
     "state": {
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "DescriptionStyleModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "StyleView",
      "description_width": ""
     }
    },
    "ff10e097306c472ca17a5b319425182d": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "HTMLModel",
     "state": {
      "_dom_classes": [],
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "HTMLModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/controls",
      "_view_module_version": "1.5.0",
      "_view_name": "HTMLView",
      "description": "",
      "description_tooltip": null,
      "layout": "IPY_MODEL_8e26a4c0644c416184af2407f00dea81",
      "placeholder": "​",
      "style": "IPY_MODEL_18fd651d8b22414d81d9b0dd9cfcb981",
      "value": "tokenizer.json: 100%"
     }
    },
    "ff2f24473f074dc5835b73a9eb60b543": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "FloatProgressModel",
     "state": {
      "_dom_classes": [],
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "FloatProgressModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/controls",
      "_view_module_version": "1.5.0",
      "_view_name": "ProgressView",
      "bar_style": "success",
      "description": "",
      "description_tooltip": null,
      "layout": "IPY_MODEL_b2b7a74220714c13bf7cedcef663b171",
      "max": 4915916184,
      "min": 0,
      "orientation": "horizontal",
      "style": "IPY_MODEL_b0beadcec05744cca50ff17f9b864c3c",
      "value": 4915916184
     }
    },
    "ff80a1b1c5f24812832285a15cfc7c3a": {
     "model_module": "@jupyter-widgets/base",
     "model_module_version": "1.2.0",
     "model_name": "LayoutModel",
     "state": {
      "_model_module": "@jupyter-widgets/base",
      "_model_module_version": "1.2.0",
      "_model_name": "LayoutModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "LayoutView",
      "align_content": null,
      "align_items": null,
      "align_self": null,
      "border": null,
      "bottom": null,
      "display": null,
      "flex": null,
      "flex_flow": null,
      "grid_area": null,
      "grid_auto_columns": null,
      "grid_auto_flow": null,
      "grid_auto_rows": null,
      "grid_column": null,
      "grid_gap": null,
      "grid_row": null,
      "grid_template_areas": null,
      "grid_template_columns": null,
      "grid_template_rows": null,
      "height": null,
      "justify_content": null,
      "justify_items": null,
      "left": null,
      "margin": null,
      "max_height": null,
      "max_width": null,
      "min_height": null,
      "min_width": null,
      "object_fit": null,
      "object_position": null,
      "order": null,
      "overflow": null,
      "overflow_x": null,
      "overflow_y": null,
      "padding": null,
      "right": null,
      "top": null,
      "visibility": null,
      "width": null
     }
    },
    "ffa26635efac424cb74fe6ae71a35f80": {
     "model_module": "@jupyter-widgets/base",
     "model_module_version": "1.2.0",
     "model_name": "LayoutModel",
     "state": {
      "_model_module": "@jupyter-widgets/base",
      "_model_module_version": "1.2.0",
      "_model_name": "LayoutModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "LayoutView",
      "align_content": null,
      "align_items": null,
      "align_self": null,
      "border": null,
      "bottom": null,
      "display": null,
      "flex": null,
      "flex_flow": null,
      "grid_area": null,
      "grid_auto_columns": null,
      "grid_auto_flow": null,
      "grid_auto_rows": null,
      "grid_column": null,
      "grid_gap": null,
      "grid_row": null,
      "grid_template_areas": null,
      "grid_template_columns": null,
      "grid_template_rows": null,
      "height": null,
      "justify_content": null,
      "justify_items": null,
      "left": null,
      "margin": null,
      "max_height": null,
      "max_width": null,
      "min_height": null,
      "min_width": null,
      "object_fit": null,
      "object_position": null,
      "order": null,
      "overflow": null,
      "overflow_x": null,
      "overflow_y": null,
      "padding": null,
      "right": null,
      "top": null,
      "visibility": null,
      "width": null
     }
    },
    "ffef39ba35064df99d13daa1d8b781aa": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "DescriptionStyleModel",
     "state": {
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "DescriptionStyleModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "StyleView",
      "description_width": ""
     }
    }
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}