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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 LLaMA/Vicuna on GPT-generated multimodal instruction-following data. |
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It is an auto-regressive language model, based on the transformer architecture. |
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**Model date:** |
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LLaVA-v1-0719-336px-LoRA-Vicuna-13B-v1.3 was trained in July 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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Non-commerical Use. |
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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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- 80K GPT-generated multimodal instruction-following data. |
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## Evaluation dataset |
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A preliminary evaluation of the model quality is conducted by creating a set of 90 visual reasoning questions from 30 unique images randomly sampled from COCO val 2014 and each is associated with three types of questions: conversational, detailed description, and complex reasoning. We utilize GPT-4 to judge the model outputs. |
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We also evaluate our model on the ScienceQA dataset. Our synergy with GPT-4 sets a new state-of-the-art on the dataset. |
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See https://llava-vl.github.io/ for more details. |
