---
inference: false
---

**NOTE: New version available**  
Please check out a newer version of the weights [here](https://huggingface.co/lmsys/vicuna-13b-v1.3).  
If you still want to use this old version, please see the compatibility and difference between different versions [here](https://github.com/lm-sys/FastChat/blob/main/docs/vicuna_weights_version.md).

**NOTE: This "delta model" cannot be used directly.**  
Users have to apply it on top of the original LLaMA weights to get actual Vicuna weights. See [instructions](https://github.com/lm-sys/FastChat/blob/main/docs/vicuna_weights_version.md#how-to-apply-delta-weights-for-weights-v11-and-v0).

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# Vicuna Model Card

## Model details

**Model type:**
Vicuna is an open-source chatbot trained by fine-tuning LLaMA on user-shared conversations collected from ShareGPT.
It is an auto-regressive language model, based on the transformer architecture.

**Model date:**
Vicuna was trained between March 2023 and April 2023.

**Organizations developing the model:**
The Vicuna team with members from UC Berkeley, CMU, Stanford, and UC San Diego.

**Paper or resources for more information:**
https://lmsys.org/blog/2023-03-30-vicuna/

**Where to send questions or comments about the model:**
https://github.com/lm-sys/FastChat/issues


## Intended use
**Primary intended uses:**
The primary use of Vicuna is research on large language models and chatbots.

**Primary intended users:**
The primary intended users of the model are researchers and hobbyists in natural language processing, machine learning, and artificial intelligence.

## Training dataset
70K conversations collected from ShareGPT.com.

## Evaluation dataset
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://lmsys.org/blog/2023-03-30-vicuna/ for more details.