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--- |
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license: apache-2.0 |
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tags: |
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- llava |
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pipeline_tag: image-text-to-text |
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--- |
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<p align="center"> |
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<img src="https://s11.ax1x.com/2023/12/28/piqvDMV.png" width="250" style="margin-bottom: 0.2;"/> |
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<p> |
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<h2 align="center"> <a href="https://arxiv.org/abs/2401.15947">MoE-LLaVA: Mixture of Experts for Large Vision-Language Models</a></h2> |
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<h5 align="center"> If you like our project, please give us a star ⭐ on GitHub for latest update. </h2> |
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<h5 align="center"> |
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</h5> |
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## 📰 News |
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* **[2024.01.30]** The [paper](https://arxiv.org/abs/2401.15947) is released. |
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* **[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. |
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## 😮 Highlights |
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MoE-LLaVA shows excellent performance in multi-modal learning. |
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### 🔥 High performance, but with fewer parameters |
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- 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. |
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### 🚀 Simple baseline, learning multi-modal interactions with sparse pathways. |
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- With the addition of **a simple MoE tuning stage**, we can complete the training of MoE-LLaVA on **8 V100 GPUs** within 2 days. |
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## 🤗 Demo |
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### Gradio Web UI |
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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. |
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```bash |
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# use phi2 |
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deepspeed --include localhost:0 moellava/serve/gradio_web_server.py --model-path "LanguageBind/MoE-LLaVA-Phi2-2.7B-4e" |
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# use qwen |
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deepspeed --include localhost:0 moellava/serve/gradio_web_server.py --model-path "LanguageBind/MoE-LLaVA-Qwen-1.8B-4e" |
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# use stablelm |
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deepspeed --include localhost:0 moellava/serve/gradio_web_server.py --model-path "LanguageBind/MoE-LLaVA-StableLM-1.6B-4e" |
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``` |
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### CLI Inference |
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```bash |
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# use phi2 |
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deepspeed --include localhost:0 moellava/serve/cli.py --model-path "LanguageBind/MoE-LLaVA-Phi2-2.7B-4e" --image-file "image.jpg" |
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# use qwen |
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deepspeed --include localhost:0 moellava/serve/cli.py --model-path "LanguageBind/MoE-LLaVA-Qwen-1.8B-4e" --image-file "image.jpg" |
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# use stablelm |
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deepspeed --include localhost:0 moellava/serve/cli.py --model-path "LanguageBind/MoE-LLaVA-StableLM-1.6B-4e" --image-file "image.jpg" |
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``` |
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## 🐳 Model Zoo |
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| Model | LLM | Checkpoint | Avg | VQAv2 | GQA | VizWiz | SQA | T-VQA | POPE | MM-Bench| LLaVA-Bench-Wild | MM-Vet | |
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|----------|-----------|-----------|---|---|---|---|---|---|---|---|---|---| |
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| 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 | |
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| 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 | |
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| 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 | |
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<!-- |
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| 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 | |
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| 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 | |
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--> |
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## ⚙️ Requirements and Installation |
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* Python >= 3.10 |
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* Pytorch == 2.0.1 |
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* CUDA Version >= 11.7 |
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* **Transformers == 4.36.2** |
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* **Tokenizers==0.15.1** |
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* Install required packages: |
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```bash |
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git clone https://github.com/PKU-YuanGroup/MoE-LLaVA |
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cd MoE-LLaVA |
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conda create -n moellava python=3.10 -y |
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conda activate moellava |
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pip install --upgrade pip # enable PEP 660 support |
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pip install -e . |
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pip install -e ".[train]" |
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pip install flash-attn --no-build-isolation |
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# Below are optional. For Qwen model. |
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git clone https://github.com/Dao-AILab/flash-attention |
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cd flash-attention && pip install . |
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# Below are optional. Installing them might be slow. |
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# pip install csrc/layer_norm |
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# If the version of flash-attn is higher than 2.1.1, the following is not needed. |
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# pip install csrc/rotary |
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``` |
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## 🗝️ Training & Validating |
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The training & validating instruction is in [TRAIN.md](docs/TRAIN.md) & [EVAL.md](docs/EVAL.md). |
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## 💡 Customizing your MoE-LLaVA |
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The instruction is in [CUSTOM.md](docs/CUSTOM.md). |
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## 😍 Visualization |
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The instruction is in [VISUALIZATION.md](docs/VISUALIZATION.md). |
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## 🤖 API |
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**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. |
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**Using the following command to run the code.** |
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```bash |
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deepspeed predict.py |
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``` |
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```python |
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import torch |
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from moellava.constants import IMAGE_TOKEN_INDEX, DEFAULT_IMAGE_TOKEN |
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from moellava.conversation import conv_templates, SeparatorStyle |
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from moellava.model.builder import load_pretrained_model |
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from moellava.utils import disable_torch_init |
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from moellava.mm_utils import tokenizer_image_token, get_model_name_from_path, KeywordsStoppingCriteria |
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def main(): |
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disable_torch_init() |
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image = 'moellava/serve/examples/extreme_ironing.jpg' |
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inp = 'What is unusual about this image?' |
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model_path = 'LanguageBind/MoE-LLaVA-Phi2-2.7B-4e' # LanguageBind/MoE-LLaVA-Qwen-1.8B-4e or LanguageBind/MoE-LLaVA-StableLM-1.6B-4e |
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device = 'cuda' |
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load_4bit, load_8bit = False, False # FIXME: Deepspeed support 4bit or 8bit? |
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model_name = get_model_name_from_path(model_path) |
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tokenizer, model, processor, context_len = load_pretrained_model(model_path, None, model_name, load_8bit, load_4bit, device=device) |
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image_processor = processor['image'] |
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conv_mode = "phi" # qwen or stablelm |
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conv = conv_templates[conv_mode].copy() |
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roles = conv.roles |
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image_tensor = image_processor.preprocess(image, return_tensors='pt')['pixel_values'].to(model.device, dtype=torch.float16) |
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print(f"{roles[1]}: {inp}") |
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inp = DEFAULT_IMAGE_TOKEN + '\n' + inp |
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conv.append_message(conv.roles[0], inp) |
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conv.append_message(conv.roles[1], None) |
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prompt = conv.get_prompt() |
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input_ids = tokenizer_image_token(prompt, tokenizer, IMAGE_TOKEN_INDEX, return_tensors='pt').unsqueeze(0).cuda() |
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stop_str = conv.sep if conv.sep_style != SeparatorStyle.TWO else conv.sep2 |
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keywords = [stop_str] |
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stopping_criteria = KeywordsStoppingCriteria(keywords, tokenizer, input_ids) |
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with torch.inference_mode(): |
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output_ids = model.generate( |
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input_ids, |
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images=image_tensor, |
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do_sample=True, |
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temperature=0.2, |
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max_new_tokens=1024, |
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use_cache=True, |
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stopping_criteria=[stopping_criteria]) |
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outputs = tokenizer.decode(output_ids[0, input_ids.shape[1]:], skip_special_tokens=True).strip() |
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print(outputs) |
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if __name__ == '__main__': |
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main() |
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``` |
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## 🙌 Related Projects |
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* [Video-LLaVA](https://github.com/PKU-YuanGroup/Video-LLaVA) This framework empowers the model to efficiently utilize the united visual tokens. |
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* [LanguageBind](https://github.com/PKU-YuanGroup/LanguageBind) An open source five modalities language-based retrieval framework. |
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## 👍 Acknowledgement |
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* [LLaVA](https://github.com/haotian-liu/LLaVA) The codebase we built upon and it is an efficient large language and vision assistant. |
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## 🔒 License |
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* 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. |
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* 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. |
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## ✏️ Citation |
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If you find our paper and code useful in your research, please consider giving a star :star: and citation :pencil:. |
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```BibTeX |
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@misc{lin2024moellava, |
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title={MoE-LLaVA: Mixture of Experts for Large Vision-Language Models}, |
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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}, |
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year={2024}, |
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eprint={2401.15947}, |
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archivePrefix={arXiv}, |
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primaryClass={cs.CV} |
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} |
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``` |
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```BibTeX |
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@article{lin2023video, |
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title={Video-LLaVA: Learning United Visual Representation by Alignment Before Projection}, |
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author={Lin, Bin and Zhu, Bin and Ye, Yang and Ning, Munan and Jin, Peng and Yuan, Li}, |
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journal={arXiv preprint arXiv:2311.10122}, |
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year={2023} |
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} |
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``` |
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## ✨ Star History |
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[](https://star-history.com/#PKU-YuanGroup/MoE-LLaVA&Date) |
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## 🤝 Contributors |
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<a href="https://github.com/PKU-YuanGroup/MoE-LLaVA/graphs/contributors"> |
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<img src="https://contrib.rocks/image?repo=PKU-YuanGroup/MoE-LLaVA" /> |
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</a> |
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