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pipeline_tag: visual-question-answering |
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inference: false |
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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-v1.5-13B was trained in September 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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Llama 2 is licensed under the LLAMA 2 Community License, |
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Copyright (c) Meta Platforms, Inc. All Rights Reserved. |
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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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- 450K academic-task-oriented VQA 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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llava-v1.5-13b-GGUF |
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This repo contains GGUF files to inference llava-v1.5-13b with llama.cpp end-to-end without any extra dependency. |
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stirred by twobob |
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Note: The mmproj-model-f16.gguf file structure is experimental and may change. Always use the latest code in llama.cpp. |
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props to @mys |
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