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README.md

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 license: apache-2.0
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+
+<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
+[![Star History](https://api.star-history.com/svg?repos=PKU-YuanGroup/MoE-LLaVA&type=Date)](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>