TinyLLaVA-2.0B

TinyLLaVA: A Framework of Small-scale Large Multimodal Models

🎉 News

• [2024.02.25] Update evaluation scripts and docs!
• [2024.02.25] Data descriptions out. Release TinyLLaVA-1.5B and TinyLLaVA-2.0B!
• [2024.02.24] Example code on inference and model loading added!
• [2024.02.23] Evaluation code and scripts released!
• [2024.02.21] Creating the TinyLLaVABench repository on GitHub!
• [2024.02.21] Our paper: TinyLLaVA: A Framework of Small-scale Large Multimodal Models is out!
• [2024.01.11] Our fist model TinyLLaVA-1.4B is out!

⌛ TODO

• Add support for Ollama and llama.cpp.
• Developers’ guide / How to build demo locally.
• Model Zoo descriptions.
• Examples and inference.
• Release code for training.
• Add descriptions for evaluation.
• Add descriptions for data preparation.
• Release TinyLLaVA-1.5B and TinyLLaVA-2.0B.
• Release TinyLLaVA-3.1B.
• Release the evaluation code and weights today(2024.2.23).

🔥 High performance, but with fewer parameters

• Our best model, TinyLLaVA-3.1B, achieves better overall performance against existing 7B models such as LLaVA-1.5 and Qwen-VL.

🐳 Model Zoo

Legacy Model

• tiny-llava-hf

Pretrained Models

• TinyLLaVA-3.1B
• TinyLLaVA-2.0B
• TinyLLaVA-1.5B

Model Details

🔧 Requirements and Installation

We recommend the requirements as follows.

1. Clone this repository and navigate to LLaVA folder

git clone https://github.com/DLCV-BUAA/TinyLLaVABench.git
cd TinyLLaVABench

2. Install Package

conda create -n tinyllava python=3.10 -y
conda activate tinyllava
pip install --upgrade pip  # enable PEP 660 support
pip install -e .

3. Install additional packages for training cases

pip install -e ".[train]"
pip install flash-attn --no-build-isolation

Upgrade to latest code base

git pull
pip install -e .
# if you see some import errors when you upgrade, please try running the command below (without #)
# pip install flash-attn --no-build-isolation --no-cache-dir

🔧 Quick Start

from tinyllava.model.builder import load_pretrained_model
from tinyllava.mm_utils import get_model_name_from_path
from tinyllava.eval.run_tiny_llava import eval_model
model_path = "bczhou/TinyLLaVA-3.1B"
tokenizer, model, image_processor, context_len = load_pretrained_model(
    model_path=model_path,
    model_base=None,
    model_name=get_model_name_from_path(model_path)
)

## 🔧 Run Inference
Here’s an example of running inference with [TinyLLaVA-3.1B](https://huggingface.co/bczhou/TinyLLaVA-3.1B)

from tinyllava.model.builder import load_pretrained_model
from tinyllava.mm_utils import get_model_name_from_path
from tinyllava.eval.run_tiny_llava import eval_model
model_path = "bczhou/TinyLLaVA-3.1B"
prompt = "What are the things I should be cautious about when I visit here?"
image_file = "https://llava-vl.github.io/static/images/view.jpg"

args = type('Args', (), {
    "model_path": model_path,
    "model_base": None,
    "model_name": get_model_name_from_path(model_path),
    "query": prompt,
    "conv_mode": "phi",
    "image_file": image_file,
    "sep": ",",
    "temperature": 0,
    "top_p": None,
    "num_beams": 1,
    "max_new_tokens": 512
})()
eval_model(args)

### Important
We use different `conv_mode` for different models. Replace the `conv_mode` in `args` according to this table:
| model | conv_mode |
|——————-|—————|
| TinyLLaVA-3.1B | phi |
| TinyLLaVA-2.0B | phi |
| TinyLLaVA-1.5B | v1 |

Evaluation

To ensure the reproducibility, we evaluate the models with greedy decoding.

See Evaluation.md

✏ Citation

If you find our paper and code useful in your research, please consider giving a star :star: and citation :pencil:.

@misc{zhou2024tinyllava,
      title={TinyLLaVA: A Framework of Small-scale Large Multimodal Models}, 
      author={Baichuan Zhou and Ying Hu and Xi Weng and Junlong Jia and Jie Luo and Xien Liu and Ji Wu and Lei Huang},
      year={2024},
      eprint={2402.14289},
      archivePrefix={arXiv},
      primaryClass={cs.LG}
}