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license: apache-2.0 |
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inference: false |
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**NOTE: This "delta model" cannot be used directly.** |
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Users have to apply it on top of the original LLaMA weights to get actual LLaVA weights. |
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See https://github.com/haotian-liu/LLaVA#llava-weights for instructions. |
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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 was trained in May 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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Apache License 2.0 |
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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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595K filtered image-text pairs from CC3M. |
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150K 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. |
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