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Curated-Awesome-Lists/awesome-llms-fine-tuning

A curated awesome list of resources, tutorials, papers, tools and courses for fine-tuning large language models.

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Stars
532
Forks
84
Open issues
12
Last commit
2 months ago

What is it?

Awesome LLMs Fine-Tuning is a curated list of resources about adapting pre-trained large language models such as GPT, BERT and RoBERTa to specific tasks and domains. It groups entries into sections covering GitHub projects, articles and blogs, online courses, books, research papers, videos, tools, events and slide decks.

The GitHub projects section lists fine-tuning frameworks and toolkits, including LLaMA-Factory, lit-gpt, H2O LLM Studio, xtuner and QLoRA-based scripts, along with their short descriptions and star counts at the time of compilation. Papers include LoRA, QLoRA and LOMO, giving readers a path from practical tooling to the underlying research.

The list is aimed at ML practitioners and researchers who want an organized starting point for fine-tuning work rather than searching across scattered sources.

Key features

  • 01Groups fine-tuning resources into GitHub projects, papers, courses, books, videos and tools
  • 02Lists fine-tuning frameworks such as LLaMA-Factory, lit-gpt and H2O LLM Studio
  • 03Includes research papers on LoRA, QLoRA, and parameter-efficient fine-tuning
  • 04Covers tutorials and guides with step-by-step examples on consumer GPUs
  • 05Collects online courses, meetups and slide decks on LLM fine-tuning

Use cases

  • Finding an open-source framework to fine-tune a Llama or Qwen model on a custom dataset
  • Reviewing the foundational papers behind LoRA and QLoRA before implementing them
  • Learning fine-tuning concepts through beginner-friendly tutorials and courses
  • Scouting tools and datasets for domain-specific model adaptation

Things to know

  • No license is listed in the project metadata.

In collections

Natural language processing tools & libraries

Topics

#llm#fine-tuning#awesome-list#deep-learning#machine-learning#nlp#transformers#lora#ai#gpt

FAQ

What is awesome-llms-fine-tuning?
A curated awesome list of resources, tutorials, papers, tools and courses for fine-tuning large language models. Awesome LLMs Fine-Tuning is a curated list of resources about adapting pre-trained large language models such as GPT, BERT and RoBERTa to specific tasks and domains. It groups entries into sections covering GitHub projects, articles and blogs, online courses, books, research papers, videos, tools, events and slide decks.
Is awesome-llms-fine-tuning free to use? What license does it have?
awesome-llms-fine-tuning has no declared license. Its code is public, but without a license you have no explicit permission to reuse it; ask the author before relying on it.
Is awesome-llms-fine-tuning still maintained?
Updated within 6 months. The last commit to awesome-llms-fine-tuning was on Sep 4, 2026.
What can I use awesome-llms-fine-tuning for?
Finding an open-source framework to fine-tune a Llama or Qwen model on a custom dataset Reviewing the foundational papers behind LoRA and QLoRA before implementing them Learning fine-tuning concepts through beginner-friendly tutorials and courses Scouting tools and datasets for domain-specific model adaptation
Where can I get awesome-llms-fine-tuning?
The source code and installation instructions are on GitHub: https://github.com/Curated-Awesome-Lists/awesome-llms-fine-tuning

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