基于Llama的中文指令模型，低成本部署且支持医疗法律等专业领域。
Chinese-Vicuna: A Chinese Instruction-following Llama-based Model
- 用LoRA微调Llama，结合混合数据集与4比特量化提升效率。
- 7B模型可在RTX-2080Ti上运行，医疗问答与多轮对话表现良好。
- 开源工具链支持转换、推理与对话接口，适合开发者快速落地。
Chinese-Vicuna 是一个开源、资源高效的语言模型，通过低秩适配（LoRA）对 Meta 的 LLaMA 架构进行微调，旨在弥补中文指令遵循能力的不足。该模型面向低资源环境，支持在消费级 GPU（如 7B 模型的 RTX-2080Ti）上低成本部署，并可应用于医疗、法律等特定领域。通过整合 BELLE 与 Guanaco 混合数据集，以及 4 位量化（QLoRA），模型在翻译、代码生成和领域问答任务中表现优异。项目提供完整的模型转换、CPU 推理与多轮对话接口工具包，强调对研究人员与开发者的可及性。评估显示其在医疗任务、多轮对话连贯性及实时法律更新方面具有竞争力。其模块化设计、开源生态与社区驱动优化，使其成为中文大模型应用的通用基础。
原文摘要 · Abstract (English)
Chinese-Vicuna is an open-source, resource-efficient language model designed to bridge the gap in Chinese instruction-following capabilities by fine-tuning Meta's LLaMA architecture using Low-Rank Adaptation (LoRA). Targeting low-resource environments, it enables cost-effective deployment on consumer GPUs (e.g., RTX-2080Ti for 7B models) and supports domain-specific adaptation in fields like healthcare and law. By integrating hybrid datasets (BELLE and Guanaco) and 4-bit quantization (QLoRA), the model achieves competitive performance in tasks such as translation, code generation, and domain-specific Q\&A. The project provides a comprehensive toolkit for model conversion, CPU inference, and multi-turn dialogue interfaces, emphasizing accessibility for researchers and developers. Evaluations indicate competitive performance across medical tasks, multi-turn dialogue coherence, and real-time legal updates. Chinese-Vicuna's modular design, open-source ecosystem, and community-driven enhancements position it as a versatile foundation for Chinese LLM applications.
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