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A family of lightweight multimodal models.

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Bunny: A family of lightweight multimodal models

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📖 Technical report | 🤗 Model | 🤖 Model | 🧠 Model | 🤗 Data | 🤖 Data | 🐰 Demo

Bunny is a family of lightweight but powerful multimodal models. It offers multiple plug-and-play vision encoders, like EVA-CLIP, SigLIP and language backbones, including Phi-1.5, StableLM-2, Qwen1.5 and Phi-2. To compensate for the decrease in model size, we construct more informative training data by curated selection from a broader data source. Remarkably, our Bunny-v1.0-3B model built upon SigLIP and Phi-2 outperforms the state-of-the-art MLLMs, not only in comparison with models of similar size but also against larger MLLMs (7B), and even achieves performance on par with 13B models.

comparison

News and Updates

  • 2024.03.15 🔥 Bunny-v1.0-2B-zh, focusing on Chinese, is released! It is built upon SigLIP and Qwen1.5-1.8B. Check more details in HuggingFace, ModelScope, and wisemodel! The benchmark results are in the Model Zoo.

  • 2024.03.06 🔥 Bunny training data is released! Check more details about Bunny-v1.0-data in HuggingFace or ModelScope!

  • 2024.02.20 🔥 Bunny technical report is ready! Check more details about Bunny here!

  • 2024.02.07 🔥 Bunny is released! Bunny-v1.0-3B built upon SigLIP and Phi-2 outperforms the state-of-the-art MLLMs, not only in comparison with models of similar size but also against larger MLLMs (7B), and even achieves performance on par with LLaVA-13B!

Quickstart

HuggingFace transformers

Here we show a code snippet to show you how to use Bunny-v1.0-3B and Bunny-v1.0-2B-zh with HuggingFace transformers.

Before running the snippet, you need to install the following dependencies:

pip install torch transformers accelerate pillow
import torch
import transformers
from transformers import AutoModelForCausalLM, AutoTokenizer
from PIL import Image
import warnings

# disable some warnings
transformers.logging.set_verbosity_error()
transformers.logging.disable_progress_bar()
warnings.filterwarnings('ignore')

# set device
torch.set_default_device('cpu')  # or 'cuda'

model_name = 'BAAI/Bunny-v1_0-3B' # or 'BAAI/Bunny-v1_0-2B-zh'
# create model
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.float16,
    device_map='auto',
    trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained(
    model_name,
    trust_remote_code=True)

# text prompt
prompt = 'Why is the image funny?'
text = f"A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: <image>\n{prompt} ASSISTANT:"
text_chunks = [tokenizer(chunk).input_ids for chunk in text.split('<image>')]
input_ids = torch.tensor(text_chunks[0] + [-200] + text_chunks[1], dtype=torch.long).unsqueeze(0)

# image, sample images can be found in https://huggingface.co/BAAI/Bunny-v1_0-3B/tree/main/images
image = Image.open('example_2.png')
image_tensor = model.process_images([image], model.config).to(dtype=model.dtype)

# generate
output_ids = model.generate(
    input_ids,
    images=image_tensor,
    max_new_tokens=100,
    use_cache=True)[0]

print(tokenizer.decode(output_ids[input_ids.shape[1]:], skip_special_tokens=True).strip())

ModelScope

We advise users especially those in Chinese mainland to use ModelScope. snapshot_download can help you solve issues concerning downloading checkpoints.

Expand to see the snippet

Before running the snippet, you need to install the following dependencies:

pip install torch modelscope transformers accelerate pillow
import torch
import transformers
from modelscope import AutoTokenizer, AutoModelForCausalLM
from modelscope.hub.snapshot_download import snapshot_download
from PIL import Image
import warnings

# disable some warnings
transformers.logging.set_verbosity_error()
transformers.logging.disable_progress_bar()
warnings.filterwarnings('ignore')

# set device
torch.set_default_device('cpu')  # or 'cuda'

model_name = 'BAAI/Bunny-v1.0-3B' # or 'BAAI/Bunny-v1.0-2B-zh'
# create model
snapshot_download(model_id='thomas/siglip-so400m-patch14-384')
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.float16,
    device_map='auto',
    trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained(
    model_name,
    trust_remote_code=True)

# text prompt
prompt = 'Why is the image funny?'
text = f"A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: <image>\n{prompt} ASSISTANT:"
text_chunks = [tokenizer(chunk).input_ids for chunk in text.split('<image>')]
input_ids = torch.tensor(text_chunks[0] + [-200] + text_chunks[1], dtype=torch.long).unsqueeze(0)

# image, sample images can be found in images folder on https://www.modelscope.cn/models/BAAI/Bunny-v1.0-3B/files
image = Image.open('example_2.png')
image_tensor = model.process_images([image], model.config).to(dtype=model.dtype)

# generate
output_ids = model.generate(
    input_ids,
    images=image_tensor,
    max_new_tokens=100,
    use_cache=True)[0]

print(tokenizer.decode(output_ids[input_ids.shape[1]:], skip_special_tokens=True).strip())

Model Zoo

  • Evaluation
Checkpoint MME$^\text{P}$ MME$^\text{C}$ MMB$^{\text{T}/\text{D}}$ MMB-CN$^{\text{T}/ \text{D}}$ SEED MMMU$^{\text{V}/\text{T}}$ VQA$^\text{v2}$ GQA SQA$^\text{I}$ POPE
bunny-phi-1.5-eva-lora 1213.7 278.9 60.9/56.8 - 56.4 30.0/28.4 76.5 60.4 58.2 86.1
bunny-stablelm-2-eva-lora 1301.0 235.0 58.4/56.4 - 55.3 29.8/29.4 74.6 56.7 60.0 84.8
bunny-phi-2-eva-lora 1421.0 285.4 68.6/67.4 - 62.2 35.9/32.6 78.9 62.3 69.1 87.1
bunny-phi-1.5-siglip-lora 1230.0 237.5 61.2/59.7 - 57.7 30.0/29.1 78.0 61.1 61.3 85.8
bunny-stablelm-2-siglip-lora 1366.8 236.1 65.1/62.8 - 58.8 29.9/29.8 78.9 60.9 61.1 85.9
Bunny-v1.0-2B-zh/bunny-qwen1.5-1.8b-siglip 1300.8 254.3 59.8/59.1 59.5/58.5 55.4 34.4/30.4 76.6 59.6 64.6 85.8
Bunny-v1.0-3B/bunny-phi-2-siglip 1488.8 289.3 69.2/68.6 - 62.5 38.2/33.0 79.8 62.5 70.9 86.8

The model with the best performance is denoted as Bunny-v1.0-3B or bunny-phi-2-siglip, whose merged weights can be found here and the LoRA weights can be found here.

The model focuses on Chinese is denoted as Bunny-v1.0-2B-zh or bunny-qwen1.5-1.8b-siglip, whose merged weights can be found here and the LoRA weights can be found here.

  • Training details
Checkpoint Vision Encoder LLM Pretrain lr Pretrain weights
bunny-phi-1.5-eva-lora EVA02_CLIP_L_336_psz14_s6B microsoft/phi-1_5 1e-3 bunny-pretrain-phi-1.5-eva
bunny-stablelm-2-eva-lora EVA02_CLIP_L_336_psz14_s6B stabilityai/stablelm-2-1_6b 1e-3 bunny-pretrain-stablelm-2-eva
bunny-phi-2-eva-lora EVA02_CLIP_L_336_psz14_s6B microsoft/phi-2 5e-5 bunny-pretrain-phi-2-eva
bunny-phi-1.5-siglip-lora siglip-so400m-patch14-384 microsoft/phi-1_5 5e-4 bunny-pretrain-phi-1.5-siglip
bunny-stablelm-2-siglip-lora siglip-so400m-patch14-384 stabilityai/stablelm-2-1_6b 5e-4 bunny-pretrain-stablelm-2-siglip
bunny-qwen1.5-1.8b-siglip-lora siglip-so400m-patch14-384 Qwen/Qwen1.5-1.8B 5e-4 bunny-pretrain-qwen1.5-1.8b-siglip
bunny-phi-2-siglip-lora siglip-so400m-patch14-384 microsoft/phi-2 5e-4 bunny-pretrain-phi-2-siglip

Install

  • CUDA and cuDNN

    We use CUDA 11.8 and cuDNN 8.7.0. We actually use the CUDA docker by NVIDIA: docker pull nvcr.io/nvidia/cuda:11.8.0-cudnn8-devel-ubuntu20.04. CUDA 12 is fine, too.

  • Create a conda virtual environment and activate it:

    conda create -n bunny python=3.10
    conda activate bunny
  • Basic requirements

    pip install --upgrade pip  # enable PEP 660 support
    pip install transformers
    pip install torch torchvision xformers --index-url https://download.pytorch.org/whl/cu118
  • Install apex

    # https://github.com/NVIDIA/apex#from-source
    pip install ninja
    git clone https://github.com/NVIDIA/apex
    cd apex
    # if pip >= 23.1 (ref: https://pip.pypa.io/en/stable/news/#v23-1) which supports multiple `--config-settings` with the same key...
    pip install -v --disable-pip-version-check --no-cache-dir --no-build-isolation --config-settings "--build-option=--cpp_ext" --config-settings "--build-option=--cuda_ext" ./
    # otherwise
    pip install -v --disable-pip-version-check --no-cache-dir --no-build-isolation --global-option="--cpp_ext" --global-option="--cuda_ext" ./
  • Install flash-attention

    # https://github.com/Dao-AILab/flash-attention?tab=readme-ov-file#installation-and-features
    pip install packaging
    pip install flash-attn --no-build-isolation
  • Install bunny and other requirements

    git clone https://github.com/BAAI-DCAI/Bunny.git
    cd Bunny
    pip install -e .

Training

Bunny training consists of two stages: (1) pretrain stage: use data to connect a frozen pretrained vision encoder to a frozen LLM, and only the connector is trained; (2) visual instruction tuning stage: use data to teach the model to follow multimodal instructions, where the connector and learnable LLM parameters are updated.

Bunny is trained on 8 A100 GPUs. Under other circumstances, you can reduce the per_device_train_batch_size and increase the gradient_accumulation_steps accordingly. Always keep the global batch size the same: global_batch_size = per_device_train_batch_size $\times$ gradient_accumulation_steps $\times$ num_gpus.

Support Models

Currently, we support several vision encoders and LLMs.

For vision encoders, we support CLIP, EVA-CLIP and SigLIP.

Vision Encoders Download Link
clip-vit-large-patch14-336 openai/clip-vit-large-patch14-336
EVA02_CLIP_L_336_psz14_s6B QuanSun/EVA-CLIP
siglip-so400m-patch14-384 google/siglip-so400m-patch14-384

For LLMs, we support phi-1.5, stablelm-2, qwen1.5-1.8b and phi-2.

MODEL_TYPE LLM Download Link
phi-1.5 phi-1_5 microsoft/phi-1_5
stablelm-2 stablelm-2-1_6b stabilityai/stablelm-2-1_6b
qwen1.5-1.8b Qwen1.5-1.8B Qwen/Qwen1.5-1.8B
phi-2 phi-2 microsoft/phi-2

Note that there are many variants of above models. We build and test our code based on the exact versions mentioned above. More models will be supported in the future!

Pretrain

  • Data preparation

    We use a high-quality coreset with less duplicates and more informative samples of LAION-2B built by this work. We randomly sample 2 million image-text pairs from the coreset and convert them to training format. The dataset is available here.

  • Run

    Update --model_name_or_path and --vision_tower to the paths of the LLM and vision encoder, respectively. Update MODEL_TYPE and OUTPUT_DIR accordingly. The global batch size is 256. The optimal learning rate varies for different settings and we list the lr in our experiments in the Model Zoo.

    sh script/train/pretrain.sh

Visual Instruction Tuning

  • Data preparation

    We build Bunny-695K by modifying SVIT-mix-665K for finetuning. The dataset is available here.

  • Run

    Update --model_name_or_path and --vision_tower to the paths of the LLM and vision encoder, respectively. Update MODEL_TYPE, PRETRAIN_DIR and OUTPUT_DIR accordingly. The global batch size is 128.

    # full-parameter tuning
    sh script/train/finetune_full.sh
    
    # LoRA tuning
    sh script/train/finetune_lora.sh

Demo

Gradio Web UI

  • Starting the Controller

    First, start the controller. This service orchestrates communication between the web server and model workers.

    python -m bunny.serve.controller \
    	--host 0.0.0.0 \
    	--port 10000
  • Launching the Gradio Web Server

    To interact with the models through a web interface, start the Gradio web server.

    Basic start:

    python -m bunny.serve.gradio_web_server \
    	--controller http://localhost:10000 \
    	--model-list-mode reload

    If you want to share your web server with others, use --share option. Note that frpc_linux_amd64_v0.2 may be missing and you can fix it following instructions printed on the screen.

    python -m bunny.serve.gradio_web_server \
    	--controller http://localhost:10000 \
    	--model-list-mode reload \
    	--share

    Now, you can open the web interface with the URL printed on the screen. You may notice that there is no model in the model list. Do not worry, as we have not launched any model worker yet. It will be automatically updated when you launch a model worker.

  • Launching Model Workers

    Model workers handle the processing of model inferences. Configure each worker with the appropriate model and start it.

    • For full-parameter tuning models

      python -m bunny.serve.model_worker \
        --host 0.0.0.0 \
        --controller http://localhost:10000 \
        --port 40000 \
        --worker http://localhost:40000 \
        --model-path /path/to/bunny/model \
        --model-type phi-2 (or stablelm-2 or phi-1.5 or qwen1.5-1.8b)
    • For LoRA tuning models

      You can use script/merge_lora_weights.py to merge the LoRA weights and base LLM, and use it as above.

      python script/merge_lora_weights.py \
        --model-path /path/to/bunny_lora_weights \
        --model-base /path/to/base_llm_model \
        --model-type phi-2 (or stablelm-2 or phi-1.5 or qwen1.5-1.8b) \
        --save-model-path /path/to/merged_model

      Or you can use it without merging as below.

      python -m bunny.serve.model_worker \
        --host 0.0.0.0 \
        --controller http://localhost:10000 \
        --port 40000 \
        --worker http://localhost:40000 \
        --model-path /path/to/bunny_lora_weights \
        --model-base /path/to/base_llm_model \
        --model-type phi-2 (or stablelm-2 or phi-1.5 or qwen1.5-1.8b)

CLI Inference (Without Gradio Interface)

For CLI-based inference without using the Gradio interface, use the following command:

  • For full-parameter tuning models

    python -m bunny.serve.cli \
    	--model-path /path/to/bunny/model \
    	--model-type phi-2 (or stablelm-2 or phi-1.5 or qwen1.5-1.8b) \
    	--image-file /path/to/the/test/image
  • For LoRA tuning models

    You can use script/merge_lora_weights.py to merge the LoRA weights and base LLM, and use it as above.

    python script/merge_lora_weights.py \
    	--model-path /path/to/bunny_lora_weights \
    	--model-base /path/to/base_llm_model \
    	--model-type phi-2 (or stablelm-2 or phi-1.5 or qwen1.5-1.8b) \
    	--save-model-path /path/to/merged_model

    Or you can use it without merging as below.

    python -m bunny.serve.cli \
    	--model-path /path/to/bunny_lora_weights \
    	--model-base /path/to/base_llm_model \
    	--model-type phi-2 (or stablelm-2 or phi-1.5 or qwen1.5-1.8b) \
    	--image-file /path/to/the/test/image

Evaluation

For full-parameter tuning models, see evaluation_full.md.

For LoRA tuning models, see evaluation_lora.md.

Citation

If you find this repository helpful, please cite the paper below.

@article{he2024bunny,
      title={Efficient Multimodal Learning from Data-centric Perspective}, 
      author={He, Muyang and Liu, Yexin and Wu, Boya and Yuan, Jianhao and Wang, Yueze and Huang, Tiejun and Zhao, Bo},
      journal={arXiv preprint arXiv:2402.11530},
      year={2024}
}

License

This project utilizes certain datasets and checkpoints that are subject to their respective original licenses. Users must comply with all terms and conditions of these original licenses. The content of this project itself is licensed under the Apache license 2.0.

Acknowledgement

We build our project based on LLaVA: Large Language and Vision Assistant.

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