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inference: false |
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license: llama2 |
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# Vicuna Model Card |
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## Model Details |
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Vicuna is a chat assistant trained by fine-tuning Llama 2 on user-shared conversations collected from ShareGPT. |
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- **Developed by:** [LMSYS](https://lmsys.org/) |
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- **Model type:** An auto-regressive language model based on the transformer architecture |
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- **License:** Non-commercial license |
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- **Finetuned from model:** [Llama 2](https://arxiv.org/abs/2307.09288) |
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### Model Sources |
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- **Repository:** https://github.com/lm-sys/FastChat |
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- **Blog:** https://lmsys.org/blog/2023-03-30-vicuna/ |
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- **Paper:** https://arxiv.org/abs/2306.05685 |
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- **Demo:** https://chat.lmsys.org/ |
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## Uses |
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The primary use of Vicuna is research on large language models and chatbots. |
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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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## How to Get Started with the Model |
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- Command line interface: https://github.com/lm-sys/FastChat#vicuna-weights |
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- APIs (OpenAI API, Huggingface API): https://github.com/lm-sys/FastChat/tree/main#api |
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## Training Details |
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Vicuna v1.5 (16k) is fine-tuned from Llama 2 with supervised instruction fine-tuning and linear RoPE scaling. |
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The training data is around 125K conversations collected from ShareGPT.com. These conversations are packed into sequences that contain 16K tokens each. |
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See more details in the "Training Details of Vicuna Models" section in the appendix of this [paper](https://arxiv.org/pdf/2306.05685.pdf). |
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## Evaluation |
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Vicuna is evaluated with standard benchmarks, human preference, and LLM-as-a-judge. See more details in this [paper](https://arxiv.org/pdf/2306.05685.pdf) and [leaderboard](https://huggingface.co/spaces/lmsys/chatbot-arena-leaderboard). |
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## Difference between different versions of Vicuna |
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See [vicuna_weights_version.md](https://github.com/lm-sys/FastChat/blob/main/docs/vicuna_weights_version.md) |
