|
--- |
|
license: apache-2.0 |
|
--- |
|
|
|
<p align="center"> |
|
<img src="https://s11.ax1x.com/2023/12/28/piqvDMV.png" width="250" style="margin-bottom: 0.2;"/> |
|
<p> |
|
<h2 align="center"> <a href="https://arxiv.org/abs/2401.15947">MoE-LLaVA: Mixture of Experts for Large Vision-Language Models</a></h2> |
|
<h5 align="center"> If you like our project, please give us a star ⭐ on GitHub for latest update. </h2> |
|
|
|
<h5 align="center"> |
|
|
|
|
|
|
|
</h5> |
|
|
|
|
|
## 📰 News |
|
* **[2024.01.30]** The [paper](https://arxiv.org/abs/2401.15947) is released. |
|
* **[2024.01.27]** 🤗[Hugging Face demo](https://huggingface.co/spaces/LanguageBind/MoE-LLaVA) and **all codes & datasets** are available now! Welcome to **watch** 👀 this repository for the latest updates. |
|
|
|
## 😮 Highlights |
|
|
|
MoE-LLaVA shows excellent performance in multi-modal learning. |
|
|
|
### 🔥 High performance, but with fewer parameters |
|
- with just **3B sparsely activated parameters**, MoE-LLaVA demonstrates performance comparable to the LLaVA-1.5-7B on various visual understanding datasets and even surpasses the LLaVA-1.5-13B in object hallucination benchmarks. |
|
|
|
|
|
### 🚀 Simple baseline, learning multi-modal interactions with sparse pathways. |
|
- With the addition of **a simple MoE tuning stage**, we can complete the training of MoE-LLaVA on **8 V100 GPUs** within 2 days. |
|
|
|
|
|
|
|
## 🤗 Demo |
|
|
|
### Gradio Web UI |
|
|
|
Highly recommend trying out our web demo by the following command, which incorporates all features currently supported by MoE-LLaVA. We also provide [online demo](https://huggingface.co/spaces/LanguageBind/MoE-LLaVA) in Huggingface Spaces. |
|
```bash |
|
# use phi2 |
|
deepspeed --include localhost:0 moellava/serve/gradio_web_server.py --model-path "LanguageBind/MoE-LLaVA-Phi2-2.7B-4e" |
|
# use qwen |
|
deepspeed --include localhost:0 moellava/serve/gradio_web_server.py --model-path "LanguageBind/MoE-LLaVA-Qwen-1.8B-4e" |
|
# use stablelm |
|
deepspeed --include localhost:0 moellava/serve/gradio_web_server.py --model-path "LanguageBind/MoE-LLaVA-StableLM-1.6B-4e" |
|
``` |
|
|
|
|
|
|
|
### CLI Inference |
|
|
|
```bash |
|
# use phi2 |
|
deepspeed --include localhost:0 moellava/serve/cli.py --model-path "LanguageBind/MoE-LLaVA-Phi2-2.7B-4e" --image-file "image.jpg" |
|
# use qwen |
|
deepspeed --include localhost:0 moellava/serve/cli.py --model-path "LanguageBind/MoE-LLaVA-Qwen-1.8B-4e" --image-file "image.jpg" |
|
# use stablelm |
|
deepspeed --include localhost:0 moellava/serve/cli.py --model-path "LanguageBind/MoE-LLaVA-StableLM-1.6B-4e" --image-file "image.jpg" |
|
``` |
|
|
|
|
|
## 🐳 Model Zoo |
|
|
|
| Model | LLM | Checkpoint | Avg | VQAv2 | GQA | VizWiz | SQA | T-VQA | POPE | MM-Bench| LLaVA-Bench-Wild | MM-Vet | |
|
|----------|-----------|-----------|---|---|---|---|---|---|---|---|---|---| |
|
| MoE-LLaVA-1.6B×4-Top2 | 1.6B | [LanguageBind/MoE-LLaVA-StableLM-1.6B-4e](https://huggingface.co/LanguageBind/MoE-LLaVA-StableLM-1.6B-4e) | 60.0 | 76.0 | 60.4 | 37.2 | 62.6 | 47.8 | 84.3 | 59.4 | 85.9 | 26.1 | |
|
| MoE-LLaVA-1.8B×4-Top2 | 1.8B | [LanguageBind/MoE-LLaVA-Qwen-1.8B-4e](https://huggingface.co/LanguageBind/MoE-LLaVA-Qwen-1.8B-4e) | 60.2 | 76.2 | 61.5 | 32.6 | 63.1 | 48.0 | 87.0 | 59.6 | 88.7 | 25.3 | |
|
| MoE-LLaVA-2.7B×4-Top2 | 2.7B | [LanguageBind/MoE-LLaVA-Phi2-2.7B-4e](https://huggingface.co/LanguageBind/MoE-LLaVA-Phi2-2.7B-4e) | 63.9 | 77.1 | 61.1 | 43.4 | 68.7 | 50.2 | 85.0 | 65.5 | 93.2 | 31.1 | |
|
|
|
<!-- |
|
| LLaVA-1.5 | 7B | [liuhaotian/llava-v1.5-7b](https://huggingface.co/liuhaotian/llava-v1.5-7b) | 62.0 | 78.5 | 62.0 | 50.0 | 66.8 | 58.2 | 85.9 | 64.3 | 31.1 | |
|
| LLaVA-1.5 | 13B | [liuhaotian/llava-v1.5-13b](https://huggingface.co/liuhaotian/llava-v1.5-13b) | 64.9 | 80.0 | 63.3 | 53.6 | 71.6 | 61.3 | 85.9 | 67.7 | 36.1 | |
|
--> |
|
|
|
## ⚙️ Requirements and Installation |
|
* Python >= 3.10 |
|
* Pytorch == 2.0.1 |
|
* CUDA Version >= 11.7 |
|
* **Transformers == 4.36.2** |
|
* **Tokenizers==0.15.1** |
|
* Install required packages: |
|
```bash |
|
git clone https://github.com/PKU-YuanGroup/MoE-LLaVA |
|
cd MoE-LLaVA |
|
conda create -n moellava python=3.10 -y |
|
conda activate moellava |
|
pip install --upgrade pip # enable PEP 660 support |
|
pip install -e . |
|
pip install -e ".[train]" |
|
pip install flash-attn --no-build-isolation |
|
|
|
# Below are optional. For Qwen model. |
|
git clone https://github.com/Dao-AILab/flash-attention |
|
cd flash-attention && pip install . |
|
# Below are optional. Installing them might be slow. |
|
# pip install csrc/layer_norm |
|
# If the version of flash-attn is higher than 2.1.1, the following is not needed. |
|
# pip install csrc/rotary |
|
``` |
|
|
|
## 🗝️ Training & Validating |
|
The training & validating instruction is in [TRAIN.md](docs/TRAIN.md) & [EVAL.md](docs/EVAL.md). |
|
|
|
## 💡 Customizing your MoE-LLaVA |
|
The instruction is in [CUSTOM.md](docs/CUSTOM.md). |
|
|
|
## 😍 Visualization |
|
The instruction is in [VISUALIZATION.md](docs/VISUALIZATION.md). |
|
|
|
## 🤖 API |
|
**We open source all codes.** If you want to load the model (e.g. ```LanguageBind/MoE-LLaVA```) on local, you can use the following code snippets. |
|
|
|
**Using the following command to run the code.** |
|
|
|
```bash |
|
deepspeed predict.py |
|
``` |
|
|
|
```python |
|
import torch |
|
from moellava.constants import IMAGE_TOKEN_INDEX, DEFAULT_IMAGE_TOKEN |
|
from moellava.conversation import conv_templates, SeparatorStyle |
|
from moellava.model.builder import load_pretrained_model |
|
from moellava.utils import disable_torch_init |
|
from moellava.mm_utils import tokenizer_image_token, get_model_name_from_path, KeywordsStoppingCriteria |
|
|
|
def main(): |
|
disable_torch_init() |
|
image = 'moellava/serve/examples/extreme_ironing.jpg' |
|
inp = 'What is unusual about this image?' |
|
model_path = 'LanguageBind/MoE-LLaVA-Phi2-2.7B-4e' # LanguageBind/MoE-LLaVA-Qwen-1.8B-4e or LanguageBind/MoE-LLaVA-StableLM-1.6B-4e |
|
device = 'cuda' |
|
load_4bit, load_8bit = False, False # FIXME: Deepspeed support 4bit or 8bit? |
|
model_name = get_model_name_from_path(model_path) |
|
tokenizer, model, processor, context_len = load_pretrained_model(model_path, None, model_name, load_8bit, load_4bit, device=device) |
|
image_processor = processor['image'] |
|
conv_mode = "phi" # qwen or stablelm |
|
conv = conv_templates[conv_mode].copy() |
|
roles = conv.roles |
|
image_tensor = image_processor.preprocess(image, return_tensors='pt')['pixel_values'].to(model.device, dtype=torch.float16) |
|
|
|
print(f"{roles[1]}: {inp}") |
|
inp = DEFAULT_IMAGE_TOKEN + '\n' + inp |
|
conv.append_message(conv.roles[0], inp) |
|
conv.append_message(conv.roles[1], None) |
|
prompt = conv.get_prompt() |
|
input_ids = tokenizer_image_token(prompt, tokenizer, IMAGE_TOKEN_INDEX, return_tensors='pt').unsqueeze(0).cuda() |
|
stop_str = conv.sep if conv.sep_style != SeparatorStyle.TWO else conv.sep2 |
|
keywords = [stop_str] |
|
stopping_criteria = KeywordsStoppingCriteria(keywords, tokenizer, input_ids) |
|
|
|
with torch.inference_mode(): |
|
output_ids = model.generate( |
|
input_ids, |
|
images=image_tensor, |
|
do_sample=True, |
|
temperature=0.2, |
|
max_new_tokens=1024, |
|
use_cache=True, |
|
stopping_criteria=[stopping_criteria]) |
|
|
|
outputs = tokenizer.decode(output_ids[0, input_ids.shape[1]:], skip_special_tokens=True).strip() |
|
print(outputs) |
|
|
|
if __name__ == '__main__': |
|
main() |
|
``` |
|
|
|
## 🙌 Related Projects |
|
* [Video-LLaVA](https://github.com/PKU-YuanGroup/Video-LLaVA) This framework empowers the model to efficiently utilize the united visual tokens. |
|
* [LanguageBind](https://github.com/PKU-YuanGroup/LanguageBind) An open source five modalities language-based retrieval framework. |
|
|
|
## 👍 Acknowledgement |
|
* [LLaVA](https://github.com/haotian-liu/LLaVA) The codebase we built upon and it is an efficient large language and vision assistant. |
|
|
|
## 🔒 License |
|
* The majority of this project is released under the Apache 2.0 license as found in the [LICENSE](https://github.com/PKU-YuanGroup/MoE-LLaVA/blob/main/LICENSE) file. |
|
* The service is a research preview intended for non-commercial use only, subject to the model [License](https://github.com/facebookresearch/llama/blob/main/MODEL_CARD.md) of LLaMA, [Terms of Use](https://openai.com/policies/terms-of-use) of the data generated by OpenAI, and [Privacy Practices](https://chrome.google.com/webstore/detail/sharegpt-share-your-chatg/daiacboceoaocpibfodeljbdfacokfjb) of ShareGPT. Please contact us if you find any potential violation. |
|
|
|
|
|
|
|
## ✏️ Citation |
|
If you find our paper and code useful in your research, please consider giving a star :star: and citation :pencil:. |
|
|
|
```BibTeX |
|
@misc{lin2024moellava, |
|
title={MoE-LLaVA: Mixture of Experts for Large Vision-Language Models}, |
|
author={Bin Lin and Zhenyu Tang and Yang Ye and Jiaxi Cui and Bin Zhu and Peng Jin and Junwu Zhang and Munan Ning and Li Yuan}, |
|
year={2024}, |
|
eprint={2401.15947}, |
|
archivePrefix={arXiv}, |
|
primaryClass={cs.CV} |
|
} |
|
``` |
|
|
|
```BibTeX |
|
@article{lin2023video, |
|
title={Video-LLaVA: Learning United Visual Representation by Alignment Before Projection}, |
|
author={Lin, Bin and Zhu, Bin and Ye, Yang and Ning, Munan and Jin, Peng and Yuan, Li}, |
|
journal={arXiv preprint arXiv:2311.10122}, |
|
year={2023} |
|
} |
|
``` |
|
|
|
|
|
|
|
## ✨ Star History |
|
[](https://star-history.com/#PKU-YuanGroup/MoE-LLaVA&Date) |
|
|
|
|
|
## 🤝 Contributors |
|
|
|
<a href="https://github.com/PKU-YuanGroup/MoE-LLaVA/graphs/contributors"> |
|
<img src="https://contrib.rocks/image?repo=PKU-YuanGroup/MoE-LLaVA" /> |
|
</a> |
|
|
