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Better maintained files can be found here: https://huggingface.co/CRD716/ggml-vicuna-1.1-quantized/


The PR #1405 brought breaking changes - none of the old models work with the latest build of llama.cpp.

Pre-PR #1405 files have been marked as old but remain accessible for those who need them (oobabooga, gpt4all-chat haven't been updated to support the new format as of May 14).

Additionally, q4_3 and q4_2 have been completely axed in favor of their 5-bit counterparts (q5_1 and q5_0, respectively).

New files inference up to 10% faster without any quality reduction.



  • Main files are based on v1.1 release
    • See changelog below
    • Use prompt template: HUMAN: <prompt> ASSISTANT: <response>
  • Uncensored files are based on v0 release
    • Use prompt template: ### User: <prompt> ### Assistant: <response>
  • PR #896 was used for q4_0. Everything else is latest as of upload time.


Several quantization methods are supported. They differ in the resulting model disk size and inference speed.

Model F16 Q4_0 Q4_1 Q4_2 Q4_3 Q5_0 Q5_1 Q8_0
7B (ppl) 5.9565 6.2103 6.1286 6.1698 6.0617 6.0139 5.9934 5.9571
7B (size) 13.0G 4.0G 4.8G 4.0G 4.8G 4.4G 4.8G 7.1G
7B (ms/tok @ 4th) 128 56 61 84 91 91 95 75
7B (ms/tok @ 8th) 128 47 55 48 53 53 59 75
7B (bpw) 16.0 5.0 6.0 5.0 6.0 5.5 6.0 9.0
-- -- -- -- -- -- -- -- --
13B (ppl) 5.2455 5.3748 5.3471 5.3433 5.3234 5.2768 5.2582 5.2458
13B (size) 25.0G 7.6G 9.1G 7.6G 9.1G 8.4G 9.1G 14G
13B (ms/tok @ 4th) 239 104 113 160 175 176 185 141
13B (ms/tok @ 8th) 240 85 99 97 114 108 117 147
13B (bpw) 16.0 5.0 6.0 5.0 6.0 5.5 6.0 9.0

q5_1 or 5_0 are the latest and most performant implementations. The former is slightly more accurate at the cost of a bit of performance. Most users should use one of the two. If you encounter any kind of compatibility issues, you might want to try the older q4_x

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.
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