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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:** Llama 2 Community License Agreement |
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- **Finetuned from model:** [Llama 2](https://arxiv.org/abs/2307.09288) |
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- **Qunatized** AWQ |
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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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- Download and deploy weights on GPU, use one click template from Rinpod - https://github.com/TrelisResearch/one-click-llms |
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## Training Details |
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Vicuna v1.5 is fine-tuned from Llama 2 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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## Qunatized |
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Using AWQ method using AutoAWQ - https://github.com/casper-hansen/AutoAWQ |
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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) |
