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metadata
license: apache-2.0
inference: false

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

  • PR #896 was used for q4_0. Everything else is latest as of upload time.
  • A warning for q4_2 and q4_3: These are WIP. Do not expect any kind of backwards compatibility until they are finalized.
  • 7B can be found here: https://huggingface.co/eachadea/ggml-vicuna-7b-1.1
  • Choosing the right model:

    • ggml-vicuna-13b-1.1-q4_0 - Fast, lacks in accuracy.

    • ggml-vicuna-13b-1.1-q4_1 - More accurate, lacks in speed.

    • ggml-vicuna-13b-1.1-q4_2 - Pretty much a better q4_0. Similarly fast, but more accurate.

    • ggml-vicuna-13b-1.1-q4_3 - Pretty much a better q4_1. More accurate, still pretty slow.

    • ggml-vicuna-13b-1.0-uncensored - Available in q4_2 and q4_3, is an uncensored/unfiltered variant of the model. It is based on the previous release and still uses the ### Human: syntax. Avoid unless you need it.

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.