README.md · bczhou/TinyLLaVA-3.1B at 0ceedacb4d2168160f28110eb632d8980e1225f9

metadata

license: apache-2.0
datasets:
  - Lin-Chen/ShareGPT4V
  - liuhaotian/LLaVA-Pretrain
  - liuhaotian/LLaVA-Instruct-150K
language:
  - en
  - zh
tags:
  - llava
  - vision-language
  - llm
  - lmm

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

Model Details

Name	LLM	Checkpoint	LLaVA-Bench-Wild	MME	MMBench	MM-Vet	SQA-image	VQA-v2	GQA	TextVQA
TinyLLaVA-3.1B	Phi-2	TinyLLaVA-3.1B	75.8	1464.9	66.9	32.0	69.1	79.9	62.0	59.1
TinyLLaVA-2.0B	StableLM-2-1.6B	TinyLLaVA-2.0B	66.4	1433.8	63.3	32.6	64.7	78.9	61.9	56.4
TinyLLaVA-1.5B	TinyLlama	TinyLLaVA-1.5B	60.8	1276.5	55.2	25.8	60.3	76.9	60.3	51.7

🔧 Requirements and Installation

We recommend the requirements as follows.

Clone this repository and navigate to LLaVA folder

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

Install Package

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

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

Load model

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

Run Inference

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}
}