Learn about and implement techniques for training and optimizing large AI models.
From the README
- 大模型参数高效微调技术原理综述（一）-背景、参数高效微调简介
- 大模型参数高效微调技术原理综述（二）-BitFit、Prefix Tuning、Prompt Tuning
- 大模型参数高效微调技术原理综述（三）-P-Tuning、P-Tuning v2
- 大模型参数高效微调技术原理综述（四）-Adapter Tuning及其变体
- 大模型参数高效微调技术原理综述（五）-LoRA、AdaLoRA、QLoRA
- 大模型参数高效微调技术原理综述（六）-MAM Adapter、UniPELT
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Free · no card · unsubscribe anytime本项目旨在分享大模型相关技术原理以及实战经验（大模型工程化、大模型应用落地）
llm-action has 25k stars on GitHub. It has been forked 2.8k times. llm-action is written mainly in HTML. It has been in active development since 2023. llm-action is available under the Apache-2.0 license. Its main topics are llm, llm-inference, llm-serving, llm-training.
What is llm-action?
本项目旨在分享大模型相关技术原理以及实战经验（大模型工程化、大模型应用落地）
Is llm-action open source?
llm-action is an open-source project. It is released under the Apache-2.0 license.
Is llm-action free?
Yes. llm-action is free and open source — you can use, modify and self-host it.
What license does llm-action use?
llm-action is available under the Apache-2.0 license.
What language is llm-action written in?
llm-action is written mainly in HTML.
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