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| inference: false |
| pipeline_tag: image-text-to-text |
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| # LLaVA Model Card |
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| ## Model details |
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| **Model type:** |
| LLaVA is an open-source chatbot trained by fine-tuning LLaMA/Vicuna on GPT-generated multimodal instruction-following data. |
| It is an auto-regressive language model, based on the transformer architecture. |
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| **Model date:** |
| LLaVA-v1.5-7B was trained in September 2023. |
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| **Paper or resources for more information:** |
| https://llava-vl.github.io/ |
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| ## License |
| Llama 2 is licensed under the LLAMA 2 Community License, |
| Copyright (c) Meta Platforms, Inc. All Rights Reserved. |
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| **Where to send questions or comments about the model:** |
| https://github.com/haotian-liu/LLaVA/issues |
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| ## Intended use |
| **Primary intended uses:** |
| The primary use of LLaVA is research on large multimodal models and chatbots. |
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| **Primary intended users:** |
| 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 |
| - 558K filtered image-text pairs from LAION/CC/SBU, captioned by BLIP. |
| - 158K GPT-generated multimodal instruction-following data. |
| - 450K academic-task-oriented VQA data mixture. |
| - 40K ShareGPT data. |
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| ## Evaluation dataset |
| A collection of 12 benchmarks, including 5 academic VQA benchmarks and 7 recent benchmarks specifically proposed for instruction-following LMMs. |
