license: apache-2.0
inference: true
NOTE: This GGML conversion is primarily for use with llama.cpp.
- 7B parameters
- 4-bit quantized
- Based on version 1.1
- Used PR "More accurate Q4_0 and Q4_1 quantizations #896" (should be closer in quality to unquantized)
- Uncensored variant is available, but it's based on version 1.0
- 13B version of this can be found here: https://huggingface.co./eachadea/ggml-vicuna-13b-1.1
- Choosing between q4_0 and q4_1, the logic of higher number = better does not apply. If you are confused, stick with q4_0.
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. (48k for the uncensored variant. 22k worth of garbage removed – see https://huggingface.co./datasets/anon8231489123/ShareGPT_Vicuna_unfiltered)
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.