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 -
safetensorsandptformats - 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 -
safetensorsandptformats - 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: 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.