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--- |
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license: apache-2.0 |
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inference: false |
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--- |
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**NOTE: This GGML conversion is primarily for use with llama.cpp.** |
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- 13B parameters |
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- 4-bit quantized |
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- Based on version 1.1 |
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- Used best available quantization for each format |
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- **Choosing between q4_0, q4_1, and q4_2:** |
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- 4_0 is the fastest. The quality is the poorest. |
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- 4_1 is slower. The quality is noticeably better. |
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- 4_2 generally offers the best speed to quality ratio. The drawback is that the format is WIP. |
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- 7B version of this can be found here: https://huggingface.co/eachadea/ggml-vicuna-7b-1.1 |
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<br> |
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<br> |
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# Vicuna Model Card |
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## Model details |
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**Model type:** |
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Vicuna is an open-source chatbot trained by fine-tuning LLaMA on user-shared conversations collected from ShareGPT. |
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It is an auto-regressive language model, based on the transformer architecture. |
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**Model date:** |
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Vicuna was trained between March 2023 and April 2023. |
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**Organizations developing the model:** |
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The Vicuna team with members from UC Berkeley, CMU, Stanford, and UC San Diego. |
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**Paper or resources for more information:** |
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https://vicuna.lmsys.org/ |
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**License:** |
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Apache License 2.0 |
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**Where to send questions or comments about the model:** |
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https://github.com/lm-sys/FastChat/issues |
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## Intended use |
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**Primary intended uses:** |
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The primary use of Vicuna is research on large language models and chatbots. |
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**Primary intended users:** |
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The primary intended users of the model are researchers and hobbyists in natural language processing, machine learning, and artificial intelligence. |
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## Training dataset |
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70K conversations collected from ShareGPT.com. |
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## Evaluation dataset |
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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. |
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## Major updates of weights v1.1 |
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- 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. |
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- Fix the supervised fine-tuning loss computation for better model quality. |
