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metadata
license: apache-2.0
inference: false

NOTE: This GGML conversion is primarily for use with llama.cpp.

  • 13B parameters

  • 4-bit quantized

  • Based on version 1.1

  • Used best available quantization for each format

  • Choosing between q4_0, q4_1, and q4_2:

    • 4_0 is the fastest. The quality is the poorest.
    • 4_1 is slower. The quality is noticeably better.
    • 4_2 generally offers the best speed to quality ratio. The drawback is that the format is WIP.

  • 7B version of this can be found here: https://huggingface.co/eachadea/ggml-vicuna-7b-1.1



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.