---
license: other
inference: false
---
# Vicuna 7B GPTQ 4-bit 128g

This repository contains the [Vicuna 7B model](https://huggingface.co./lmsys/vicuna-7b-delta-v0) quantised using [GPTQ-for-LLaMa](https://github.com/qwopqwop200/GPTQ-for-LLaMa).

The original Vicuna 7B repository contains deltas rather than weights. Rather than merging the deltas myself, I used the model files from https://huggingface.co./helloollel/vicuna-7b.

## Provided files

Two model files are provided. You don't need both, choose the one you prefer.

Details of the files provided:
* `vicuna-7B-GPTQ-4bit-128g.pt`
  * pt format file, created with the latest [GPTQ-for-LLaMa](https://github.com/qwopqwop200/GPTQ-for-LLaMa) code.
  * Command to create:
    * `python3 llama.py vicuna-7B c4 --wbits 4 --true-sequential --act-order --groupsize 128 --save vicuna-7B-GPTQ-4bit-128g.pt`
* `vicuna-7B-GPTQ-4bit-128g.safetensors`
  * newer `safetensors` format, with improved file security, created with the latest [GPTQ-for-LLaMa](https://github.com/qwopqwop200/GPTQ-for-LLaMa) code.
  * Command to create:
    * `python3 llama.py vicuna-7B c4 --wbits 4 --true-sequential --act-order --groupsize 128 --save_safetensors vicuna-7B-GPTQ-4bit-128g.safetensors`

## How to run these GPTQ models in `text-generation-webui`

These model files were created with the latest GPTQ code, and require that the latest GPTQ-for-LLaMa is used inside the UI.

Here are the commands I used to clone the Triton branch of GPTQ-for-LLaMa, clone text-generation-webui, and install GPTQ into the UI:
```
git clone https://github.com/qwopqwop200/GPTQ-for-LLaMa
git clone https://github.com/oobabooga/text-generation-webui
mkdir -p text-generation-webui/repositories
ln -s GPTQ-for-LLaMa text-generation-webui/repositories/GPTQ-for-LLaMa
```

Then install this model into `text-generation-webui/models` and launch the UI as follows:
```
cd text-generation-webui
python server.py --model vicuna-7B-GPTQ-4bit-128g --wbits 4 --groupsize 128  # add any other command line args you want
```

The above commands assume you have installed all dependencies for GPTQ-for-LLaMa and text-generation-webui. Please see their respective repositories for further information.

If you are on Windows, or cannot use the Triton branch of GPTQ for any other reason, you can instead use the CUDA branch:
```
git clone https://github.com/qwopqwop200/GPTQ-for-LLaMa -b cuda
cd GPTQ-for-LLaMa
python setup_cuda.py install
```
Then link that into `text-generation-webui/repositories` as described above.

# 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.