Quantizing Model - 8-bit
Vicuna Model Card
Model Details
Vicuna is a chat assistant trained by fine-tuning Llama 2 on user-shared conversations collected from ShareGPT.
•	Developed by: Vigneshwaran D
•	Model type: An auto-regressive language model based on the transformer architecture
•	License: Llama 2 Community License Agreement
•	Finetuned from model: Llama 2
•	Quantized to: 8-bit
Base Model Sources
•	Repository: https://github.com/lm-sys/FastChat
•	Blog: https://lmsys.org/blog/2023-03-30-vicuna/
•	Paper: https://arxiv.org/abs/2306.05685
•	Demo: https://chat.lmsys.org/
Uses
The primary use of Vicuna is research on large language models and chatbots. The primary intended users of the model are researchers and hobbyists in natural language processing, machine learning, and artificial intelligence.
How to Get Started with the Model
•	Command line interface: https://github.com/lm-sys/FastChat#vicuna-weights
•	APIs (OpenAI API, Huggingface API): https://github.com/lm-sys/FastChat/tree/main#api
Training Details
Vicuna v1.5 is fine-tuned from Llama 2 with supervised instruction fine-tuning. The training data is around 125K conversations collected from ShareGPT.com. See more details in the "Training Details of Vicuna Models" section in the appendix of this paper.
Quantization Details
This model has been quantized to 8-bit precision to reduce memory usage and improve inference speed, making it more efficient for deployment on devices with limited resources. The quantization process involves converting the model weights from 32-bit floating point to 8-bit integer representation.
Evaluation
 
Vicuna is evaluated with standard benchmarks, human preference, and LLM-as-a-judge. See more details in this paper and leaderboard.
Difference between different versions of Vicuna
See vicuna_weights_version.md