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临床LLM微调 是一款基于 Jupyter Notebook 开发的开源工具,专注于 LLM、微调、临床 等核心功能。作为 GitHub 开源项目,它拥有活跃的社区支持和持续的版本迭代,代码完全透明可审计,支持本地部署以保护数据隐私。无论是个人使用还是集成到企业工作流,都能提供稳定可靠的解决方案。
临床LLM微调 是一款基于 Jupyter Notebook 开发的开源工具,专注于 LLM、微调、临床 等核心功能。作为 GitHub 开源项目,它拥有活跃的社区支持和持续的版本迭代,代码完全透明可审计,支持本地部署以保护数据隐私。无论是个人使用还是集成到企业工作流,都能提供稳定可靠的解决方案。
# 克隆仓库 git clone https://github.com/baeseongsu/Clinical-LLM-FineTuning-HandsOn cd Clinical-LLM-FineTuning-HandsOn # 查看安装说明 cat README.md # 按 README 完成环境依赖安装后即可使用
# 查看帮助 clinical-llm-finetuning-handson --help # 基本运行 clinical-llm-finetuning-handson [options] <input> # 详细使用说明请查阅文档 # https://github.com/baeseongsu/Clinical-LLM-FineTuning-HandsOn
# clinical-llm-finetuning-handson 配置说明 # 查看配置选项 clinical-llm-finetuning-handson --config-example > config.yml # 常见配置项 # output_dir: ./output # log_level: info # workers: 4 # 环境变量(覆盖配置文件) export CLINICAL_LLM_FINETUNING_HANDSON_CONFIG="/path/to/config.yml"
This repository contains lecture materials and hands-on tutorials on fine-tuning a clinical domain Large Language Model (LLM).
BERT Devlin, Jacob. "Bert: Pre-training of deep bidirectional transformers for language understanding." arXiv preprint arXiv:1810.04805 (2018).GPT Radford, Alec, et al. "Improving Language Understanding by Generative Pre-Training."T5 Raffel, Colin, et al. "Exploring the limits of transfer learning with a unified text-to-text transformer." Journal of machine learning research 21.140 (2020): 1-67.GPT-3 Brown, Tom, et al. "Language models are few-shot learners." Advances in neural information processing systems 33 (2020): 1877-1901.FLAN Wei, Jason, et al. "Finetuned language models are zero-shot learners." arXiv preprint arXiv:2109.01652 (2021).Llama Touvron, Hugo, et al. "Llama: Open and efficient foundation language models." arXiv preprint arXiv:2302.13971 (2023).RLHF Ouyang, Long, et al. "Training language models to follow instructions with human feedback." Advances in neural information processing systems 35 (2022): 27730-27744.ChatGPT https://openai.com/index/chatgpt/Llama 2 Touvron, Hugo, et al. "Llama 2: Open foundation and fine-tuned chat models." arXiv preprint arXiv:2307.09288 (2023).Llama 3 Dubey, Abhimanyu, et al. "The Llama 3 Herd of Models." arXiv preprint arXiv:2407.21783 (2024).DPO Rafailov, Rafael, et al. "Direct preference optimization: Your language model is secretly a reward model." Advances in Neural Information Processing Systems 36 (2024).Alpaca Taori, Rohan, et al. "Alpaca: A strong, replicable instruction-following model." Stanford Center for Research on Foundation Models. https://crfm. stanford. edu/2023/03/13/alpaca. html 3.6 (2023): 7.Asclepius Kweon, Sunjun, et al. "Publicly Shareable Clinical Large Language Model Built on Synthetic Clinical Notes." arXiv preprint arXiv:2309.00237 (2023).Lora Hu, Edward J., et al. "Lora: Low-rank adaptation of large language models." arXiv preprint arXiv:2106.09685 (2021).QLora Dettmers, Tim, et al. "Qlora: Efficient finetuning of quantized llms." Advances in Neural Information Processing Systems 36 (2024).实用性强的LLM微调工具
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经综合评估,临床LLM微调 在AI工具赛道中表现稳健,质量优秀。如果你已有明确的使用需求,可以直接上手体验;如果还在评估阶段,建议对比同类工具后再做决策。
| 原始名称 | Clinical-LLM-FineTuning-HandsOn |
| Topics | LLM微调临床 |
| GitHub | https://github.com/baeseongsu/Clinical-LLM-FineTuning-HandsOn |
| License | MIT |
| 语言 | Jupyter Notebook |
收录时间:2026-07-10 · 更新时间:2026-07-10 · License:MIT · AI Skill Hub 不对第三方内容的准确性作法律背书。