---
license: apache-2.0
inference: false
---

**NOTE: This GGML conversion is primarily for use with llama.cpp.**
- PR #896 was used for q4_0. Everything else is latest as of upload time.
- A warning for q4_2 and q4_3: These are WIP. Do not expect any kind of backwards compatibility until they are finalized.
- 7B can be found here: https://huggingface.co/eachadea/ggml-vicuna-7b-1.1
- **Choosing the right model:**
  - `ggml-vicuna-13b-1.1-q4_0` - Fast, lacks in accuracy.
  - `ggml-vicuna-13b-1.1-q4_1` - More accurate, lacks in speed.

  - `ggml-vicuna-13b-1.1-q4_2` - Pretty much a better `q4_0`. Similarly fast, but more accurate.
  - `ggml-vicuna-13b-1.1-q4_3` - Pretty much a better `q4_1`. More accurate, still pretty slow.
 
  - `ggml-vicuna-13b-1.0-uncensored` - Available in `q4_2` and `q4_3`, is an uncensored/unfiltered variant of the model. It is based on the previous release and still uses the `### Human:` syntax. Avoid unless you need it.

---

# 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://vicuna.lmsys.org/

**License:**
Apache License 2.0

**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://vicuna.lmsys.org/ for more details.

## Major updates of weights v1.1
- Refactor the tokenization and separator. In Vicuna v1.1, the separator has been changed from `"###"` to the EOS token `"</s>"`. This change makes it easier to determine the generation stop criteria and enables better compatibility with other libraries.
- Fix the supervised fine-tuning loss computation for better model quality.