CaesarBot.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/DrivenIdeaLab/LLM-Zero-to-Hundred/blob/master/CaesarBot.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "-NnT9bKne4fT"
      },
      "outputs": [],
      "source": [
        "import os\n",
        "os.kill(os.getpid(), 9)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0n3OiP8serLC",
        "outputId": "f69855f7-0208-4833-8780-85e2ff745429"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
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            "Installing collected packages: tokenizers, safetensors, huggingface-hub, transformers\n",
            "Successfully installed huggingface-hub-0.17.2 safetensors-0.3.3 tokenizers-0.13.3 transformers-4.33.2\n",
            "Collecting accelerate\n",
            "  Downloading accelerate-0.23.0-py3-none-any.whl (258 kB)\n",
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            "Installing collected packages: accelerate\n",
            "Successfully installed accelerate-0.23.0\n",
            "Looking in indexes: https://test.pypi.org/simple/\n",
            "Collecting bitsandbytes\n",
            "  Downloading https://test-files.pythonhosted.org/packages/5c/e0/597d593ec3b6cf5ea7eb4894a545045bd95611de8a316a2a1eaa838a2459/bitsandbytes-0.39.0-py3-none-any.whl (95.8 MB)\n",
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            "\u001b[?25hInstalling collected packages: bitsandbytes\n",
            "Successfully installed bitsandbytes-0.39.0\n",
            "Collecting flask-ngrok\n",
            "  Downloading flask_ngrok-0.0.25-py3-none-any.whl (3.1 kB)\n",
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            "Installing collected packages: flask-ngrok\n",
            "Successfully installed flask-ngrok-0.0.25\n"
          ]
        }
      ],
      "source": [
        "!pip install transformers\n",
        "!pip install accelerate\n",
        "!pip install -i https://test.pypi.org/simple/ bitsandbytes\n",
        "!pip install flask-ngrok"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-B0WgRLcezV_",
        "outputId": "f8f601cc-deb6-4674-83b0-ecf0633044c5"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "    _|    _|  _|    _|    _|_|_|    _|_|_|  _|_|_|  _|      _|    _|_|_|      _|_|_|_|    _|_|      _|_|_|  _|_|_|_|\n",
            "    _|    _|  _|    _|  _|        _|          _|    _|_|    _|  _|            _|        _|    _|  _|        _|\n",
            "    _|_|_|_|  _|    _|  _|  _|_|  _|  _|_|    _|    _|  _|  _|  _|  _|_|      _|_|_|    _|_|_|_|  _|        _|_|_|\n",
            "    _|    _|  _|    _|  _|    _|  _|    _|    _|    _|    _|_|  _|    _|      _|        _|    _|  _|        _|\n",
            "    _|    _|    _|_|      _|_|_|    _|_|_|  _|_|_|  _|      _|    _|_|_|      _|        _|    _|    _|_|_|  _|_|_|_|\n",
            "    \n",
            "    To login, `huggingface_hub` requires a token generated from https://huggingface.co/settings/tokens .\n",
            "Token: \n",
            "Add token as git credential? (Y/n) Y\n",
            "Token is valid (permission: read).\n",
            "\u001b[1m\u001b[31mCannot authenticate through git-credential as no helper is defined on your machine.\n",
            "You might have to re-authenticate when pushing to the Hugging Face Hub.\n",
            "Run the following command in your terminal in case you want to set the 'store' credential helper as default.\n",
            "\n",
            "git config --global credential.helper store\n",
            "\n",
            "Read https://git-scm.com/book/en/v2/Git-Tools-Credential-Storage for more details.\u001b[0m\n",
            "Token has not been saved to git credential helper.\n",
            "Your token has been saved to /root/.cache/huggingface/token\n",
            "Login successful\n"
          ]
        }
      ],
      "source": [
        "!huggingface-cli login"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "-ZB4GOqRe1Q5"
      },
      "outputs": [],
      "source": [
        "import locale\n",
        "locale.getpreferredencoding = lambda: \"UTF-8\""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eKTTm6T0e2uA",
        "outputId": "7ddd7e46-3aeb-4047-a6de-b17476d31b6b"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "===================================BUG REPORT===================================\n",
            "Welcome to bitsandbytes. For bug reports, please run\n",
            "\n",
            "python -m bitsandbytes\n",
            "\n",
            " and submit this information together with your error trace to: https://github.com/TimDettmers/bitsandbytes/issues\n",
            "================================================================================\n",
            "CUDA_SETUP: WARNING! libcudart.so not found in any environmental path. Searching in backup paths...\n",
            "CUDA SETUP: CUDA runtime path found: /usr/local/cuda/lib64/libcudart.so\n",
            "CUDA SETUP: Highest compute capability among GPUs detected: 7.0\n",
            "CUDA SETUP: Detected CUDA version 118\n",
            "CUDA SETUP: Loading binary /usr/local/lib/python3.10/dist-packages/bitsandbytes/libbitsandbytes_cuda118_nocublaslt.so...\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/bitsandbytes/cuda_setup/main.py:147: UserWarning: /usr/lib64-nvidia did not contain ['libcudart.so', 'libcudart.so.11.0', 'libcudart.so.12.0'] as expected! Searching further paths...\n",
            "  warn(msg)\n",
            "/usr/local/lib/python3.10/dist-packages/bitsandbytes/cuda_setup/main.py:147: UserWarning: WARNING: The following directories listed in your path were found to be non-existent: {PosixPath('/sys/fs/cgroup/memory.events /var/colab/cgroup/jupyter-children/memory.events')}\n",
            "  warn(msg)\n",
            "/usr/local/lib/python3.10/dist-packages/bitsandbytes/cuda_setup/main.py:147: UserWarning: WARNING: The following directories listed in your path were found to be non-existent: {PosixPath('//172.28.0.1'), PosixPath('http'), PosixPath('8013')}\n",
            "  warn(msg)\n",
            "/usr/local/lib/python3.10/dist-packages/bitsandbytes/cuda_setup/main.py:147: UserWarning: WARNING: The following directories listed in your path were found to be non-existent: {PosixPath('--logtostderr --listen_host=172.28.0.12 --target_host=172.28.0.12 --tunnel_background_save_url=https'), PosixPath('//colab.research.google.com/tun/m/cc48301118ce562b961b3c22d803539adc1e0c19/gpu-v100-hm-3gxtvq09x7j09 --tunnel_background_save_delay=10s --tunnel_periodic_background_save_frequency=30m0s --enable_output_coalescing=true --output_coalescing_required=true')}\n",
            "  warn(msg)\n",
            "/usr/local/lib/python3.10/dist-packages/bitsandbytes/cuda_setup/main.py:147: UserWarning: WARNING: The following directories listed in your path were found to be non-existent: {PosixPath('/env/python')}\n",
            "  warn(msg)\n",
            "/usr/local/lib/python3.10/dist-packages/bitsandbytes/cuda_setup/main.py:147: UserWarning: WARNING: The following directories listed in your path were found to be non-existent: {PosixPath('module'), PosixPath('//ipykernel.pylab.backend_inline')}\n",
            "  warn(msg)\n",
            "/usr/local/lib/python3.10/dist-packages/bitsandbytes/cuda_setup/main.py:147: UserWarning: Found duplicate ['libcudart.so', 'libcudart.so.11.0', 'libcudart.so.12.0'] files: {PosixPath('/usr/local/cuda/lib64/libcudart.so'), PosixPath('/usr/local/cuda/lib64/libcudart.so.11.0')}.. We'll flip a coin and try one of these, in order to fail forward.\n",
            "Either way, this might cause trouble in the future:\n",
            "If you get `CUDA error: invalid device function` errors, the above might be the cause and the solution is to make sure only one ['libcudart.so', 'libcudart.so.11.0', 'libcudart.so.12.0'] in the paths that we search based on your env.\n",
            "  warn(msg)\n",
            "/usr/local/lib/python3.10/dist-packages/bitsandbytes/cuda_setup/main.py:147: UserWarning: WARNING: Compute capability < 7.5 detected! Only slow 8-bit matmul is supported for your GPU!\n",
            "  warn(msg)\n"
          ]
        }
      ],
      "source": [
        "from transformers import AutoTokenizer, AutoModelForCausalLM, GenerationConfig, pipeline\n",
        "import torch\n",
        "from accelerate import Accelerator"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
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            ]
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      ],
      "source": [
        "model_name=\"meta-llama/Llama-2-7b-chat-hf\"\n",
        "tokenizer = AutoTokenizer.from_pretrained(model_name)\n",
        "pipeline = pipeline(\n",
        "    \"text-generation\",\n",
        "    model=model_name,\n",
        "    torch_dtype=torch.float16,\n",
        "    device_map={\"\": Accelerator().process_index},\n",
        "    trust_remote_code=True\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "NrR7cWlRe9JP"
      },
      "outputs": [],
      "source": [
        "class LLM:\n",
        "  def __init__(self):\n",
        "    self.pipeline = pipeline\n",
        "    self.tokenizer = tokenizer\n",
        "\n",
        "  def generate(self, prompt):\n",
        "    sequences = self.pipeline(\n",
        "        prompt,\n",
        "        do_sample=True,\n",
        "        top_k=40,\n",
        "        num_return_sequences=1,\n",
        "        eos_token_id=self.tokenizer.eos_token_id,\n",
        "        max_length=10000,\n",
        "    )\n",
        "    return sequences[0]['generated_text']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "DnMCc5CBfEAh"
      },
      "outputs": [],
      "source": [
        "llm = LLM()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "id": "1RWsTE7qsseE",
        "outputId": "09d59a22-c738-4c48-814f-d678b35cc9e0"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "https://xocrdubj7j-496ff2e9c6d22116-5000-colab.googleusercontent.com/\n"
          ]
        }
      ],
      "source": [
        "from google.colab.output import eval_js\n",
        "print(eval_js(\"google.colab.kernel.proxyPort(5000)\"))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true,
          "base_uri": "https://localhost:8080/"
        },
        "id": "HtrYBx3ofJwB",
        "outputId": "b44f4397-a4f9-4272-fa73-fa19c2b770c0"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            " * Serving Flask app '__main__'\n",
            " * Debug mode: off\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "INFO:werkzeug:\u001b[31m\u001b[1mWARNING: This is a development server. Do not use it in a production deployment. Use a production WSGI server instead.\u001b[0m\n",
            " * Running on http://127.0.0.1:5000\n",
            "INFO:werkzeug:\u001b[33mPress CTRL+C to quit\u001b[0m\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            " * Running on http://f535-34-32-241-160.ngrok.io\n",
            " * Traffic stats available on http://127.0.0.1:4040\n",
            "Generating results\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "INFO:werkzeug:127.0.0.1 - - [19/Sep/2023 05:38:53] \"POST /generate HTTP/1.1\" 200 -\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Generating results\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "INFO:werkzeug:127.0.0.1 - - [19/Sep/2023 05:39:34] \"POST /generate HTTP/1.1\" 200 -\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Generating results\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "INFO:werkzeug:127.0.0.1 - - [19/Sep/2023 05:41:05] \"POST /generate HTTP/1.1\" 200 -\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Generating results\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "INFO:werkzeug:127.0.0.1 - - [19/Sep/2023 05:41:52] \"POST /generate HTTP/1.1\" 200 -\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Generating results\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "INFO:werkzeug:127.0.0.1 - - [19/Sep/2023 05:42:46] \"POST /generate HTTP/1.1\" 200 -\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Generating results\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "INFO:werkzeug:127.0.0.1 - - [19/Sep/2023 05:43:15] \"POST /generate HTTP/1.1\" 200 -\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Generating results\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "INFO:werkzeug:127.0.0.1 - - [19/Sep/2023 05:43:40] \"POST /generate HTTP/1.1\" 200 -\n",
            "INFO:werkzeug:127.0.0.1 - - [19/Sep/2023 05:44:16] \"POST /generate HTTP/1.1\" 200 -\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Generating results\n",
            "Generating results\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "INFO:werkzeug:127.0.0.1 - - [19/Sep/2023 05:44:34] \"POST /generate HTTP/1.1\" 200 -\n"
          ]
        }
      ],
      "source": [
        "import os\n",
        "import string\n",
        "import random\n",
        "import logging\n",
        "import math\n",
        "\n",
        "from flask import Flask, jsonify, request, send_from_directory\n",
        "from flask_ngrok import run_with_ngrok\n",
        "\n",
        "\n",
        "app = Flask(__name__)\n",
        "run_with_ngrok(app)\n",
        "\n",
        "@app.route('/generate', methods=['POST'])\n",
        "def intiator():\n",
        "  json = request.json\n",
        "\n",
        "  prompt = json['prompt']\n",
        "  print(\"Generating results\")\n",
        "\n",
        "  o = llm.generate(prompt)\n",
        "  o = o[o.index(\"[/INST]\")+len(\"[/INST]\"):].strip()\n",
        "\n",
        "  return jsonify({ \"output\": o })\n",
        "\n",
        "app.run()"
      ]
    }
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