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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 Vicuna weights. |
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See https://github.com/lm-sys/FastChat#vicuna-weights for instructions. |
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# Vicuna Model Card |
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## Model details |
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**Model type:** |
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Vicuna is an open-source chatbot trained by fine-tuning LLaMA on user-shared conversations collected from ShareGPT. |
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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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Vicuna was trained between March 2023 and April 2023. |
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**Organizations developing the model:** |
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The Vicuna team with members from UC Berkeley, CMU, Stanford, and UC San Diego. |
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**Paper or resources for more information:** |
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https://vicuna.lmsys.org/ |
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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/lm-sys/FastChat/issues |
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## Intended use |
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**Primary intended uses:** |
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The primary use of Vicuna is research on large language 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 natural language processing, machine learning, and artificial intelligence. |
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## Training dataset |
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70K conversations collected from ShareGPT.com. |
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## Evaluation dataset |
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A preliminary evaluation of the model quality is conducted by creating a set of 80 diverse questions and utilizing GPT-4 to judge the model outputs. See https://vicuna.lmsys.org/ for more details. |
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