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--- |
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license: apache-2.0 |
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library_name: sglang |
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tags: |
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- llava |
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inference: false |
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pipeline_tag: image-text-to-text |
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--- |
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## Inference Preparation |
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This is a fork of [liuhaotian/llava-v1.6-mistral-7b](https://huggingface.co/liuhaotian/llava-v1.6-mistral-7b) to be fully |
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compatible for inference with [SGLang](https://github.com/sgl-project/sglang/). |
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No other changes were made. |
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<br> |
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<br> |
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# LLaVA Model Card |
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## Model details |
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**Model type:** |
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LLaVA is an open-source chatbot trained by fine-tuning LLM on multimodal instruction-following data. |
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It is an auto-regressive language model, based on the transformer architecture. |
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Base LLM: [mistralai/Mistral-7B-Instruct-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2) |
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**Model date:** |
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LLaVA-v1.6-Mistral-7B was trained in December 2023. |
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**Paper or resources for more information:** |
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https://llava-vl.github.io/ |
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## License |
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[mistralai/Mistral-7B-Instruct-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2) license. |
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**Where to send questions or comments about the model:** |
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https://github.com/haotian-liu/LLaVA/issues |
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## Intended use |
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**Primary intended uses:** |
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The primary use of LLaVA is research on large multimodal models and chatbots. |
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**Primary intended users:** |
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The primary intended users of the model are researchers and hobbyists in computer vision, natural language processing, machine learning, and artificial intelligence. |
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## Training dataset |
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- 558K filtered image-text pairs from LAION/CC/SBU, captioned by BLIP. |
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- 158K GPT-generated multimodal instruction-following data. |
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- 500K academic-task-oriented VQA data mixture. |
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- 50K GPT-4V data mixture. |
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- 40K ShareGPT data. |
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## Evaluation dataset |
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A collection of 12 benchmarks, including 5 academic VQA benchmarks and 7 recent benchmarks specifically proposed for instruction-following LMMs. |
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