vicuna-7b-v1.1 /
TheBloke's picture
license: other
# Vicuna 7B 1.1 HF
This is an HF version of the [Vicuna 7B 1.1 model](
It was created by merging the deltas provided in the above repo with the original Llama 7B model, [using the code provided on their Github page](
## My Vicuna 1.1 model repositories
I have the following Vicuna 1.1 repositories available:
**13B models:**
* [Unquantized 13B 1.1 model for GPU - HF format](
* [GPTQ quantized 4bit 13B 1.1 for GPU - `safetensors` and `pt` formats](
* [GPTQ quantized 4bit 13B 1.1 for CPU - GGML format for `llama.cpp`](
**7B models:**
* [Unquantized 7B 1.1 model for GPU - HF format](
* [GPTQ quantized 4bit 7B 1.1 for GPU - `safetensors` and `pt` formats](
* [GPTQ quantized 4bit 7B 1.1 for CPU - GGML format for `llama.cpp`](
# 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:**
Apache License 2.0
**Where to send questions or comments about the model:**
## 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
## 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 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.