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inference: false |
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**NOTE: New version available** |
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Please check out a newer version of the weights [here](https://github.com/lm-sys/FastChat/blob/main/docs/vicuna_weights_version.md). |
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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 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](https://arxiv.org/abs/2302.13971). |
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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 v1.3 is fine-tuned from LLaMA with supervised instruction fine-tuning. |
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The training data is around 125K conversations collected from ShareGPT.com. |
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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) |
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# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard) |
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Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_lmsys__vicuna-7b-v1.3) |
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| Metric | Value | |
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| Avg. | 45.22 | |
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| ARC (25-shot) | 50.43 | |
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| HellaSwag (10-shot) | 76.92 | |
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| MMLU (5-shot) | 48.14 | |
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| TruthfulQA (0-shot) | 47.01 | |
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| Winogrande (5-shot) | 70.48 | |
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| GSM8K (5-shot) | 5.69 | |
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| DROP (3-shot) | 17.89 | |
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