--- title: MoE-LLaVA-StableLM-1.6B-4e-384 canonical_url: "https://www.modelscope.cn/models/PKU-YuanLab/MoE-LLaVA-StableLM-1.6B-4e-384" md_url: "https://www.modelscope.cn/models/PKU-YuanLab/MoE-LLaVA-StableLM-1.6B-4e-384.md" repository: PKU-YuanLab/MoE-LLaVA-StableLM-1.6B-4e-384 last_updated: 2025-06-04 license: apache-2.0 pipeline_tag: visual-question-answering tasks: - visual-question-answering model_type: - moe_llava_stablelm architectures: - MoELLaVAStablelmForCausalLM parameters: 3.3B tensor_type: - BF16 library_name: - pytorch - safetensors frameworks: - Pytorch downloads: 431 stars: 2 --- # MoE-LLaVA-StableLM-1.6B-4e-384 > MoE-LLaVA-StableLM-1.6B-4e-384 - PKU-YuanLab 在 ModelScope 开源的模型。MoE-LLaVA: Mixture of Experts for Large Vision-Language Models If you like our project, please give us a star ⭐ on GitHub for latest update. PKU-YuanLab/MoE-LLaVA-StableLM-1.6B-4e-384 是 ModelScope 魔搭社区上的 3.3B 参数visual-question-answering模型，采用 apache-2.0 许可。 - **Repository**: PKU-YuanLab/MoE-LLaVA-StableLM-1.6B-4e-384 - **License**: apache-2.0 - **Tasks**: visual-question-answering - **Parameters**: 3.3B - **Downloads**: 431 - **Stars**: 2 - **Last updated**: 2025-06-04 Source: https://www.modelscope.cn/models/PKU-YuanLab/MoE-LLaVA-StableLM-1.6B-4e-384 ---

MoE-LLaVA: Mixture of Experts for Large Vision-Language Models

If you like our project, please give us a star ⭐ on GitHub for latest update.
## 📰 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 | ## ⚙️ 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