This repository is a curated collection of resources for fine-tuning Large Language Models (LLMs), targeting ML practitioners and researchers. It aims to provide a comprehensive overview of tutorials, papers, tools, and best practices to facilitate the adaptation of pre-trained LLMs for specific tasks and domains.

How It Works

The list categorizes resources into GitHub projects, articles, courses, books, research papers, videos, tools, conferences, slides, and podcasts. It highlights popular GitHub projects like AutoTrain, LlamaIndex, Petals, and LLaMA-Factory, showcasing their features and community adoption (star counts). The content covers various fine-tuning techniques, including Parameter-Efficient Fine-Tuning (PEFT), LoRA, QLoRA, and Reinforcement Learning from Human Feedback (RLHF).

Quick Start & Requirements

This is a curated list, not a runnable project. Specific tools mentioned within the list will have their own installation and dependency requirements, often involving Python, PyTorch, and potentially CUDA for GPU acceleration.

Highlighted Details

• Extensive coverage of Parameter-Efficient Fine-Tuning (PEFT) methods like LoRA and QLoRA.
• Inclusion of no-code and low-code GUI tools like H2O LLM Studio and Ludwig AI.
• Links to academic papers detailing novel fine-tuning techniques and evaluations.
• Resources for fine-tuning specific popular LLMs such as Llama 2, Falcon, and BLOOM.

Maintenance & Community

The list is community-driven, with many projects featuring active development and significant community engagement (indicated by GitHub stars). Links to relevant communities (e.g., Discord/Slack for specific tools) are often provided within the project descriptions.

Licensing & Compatibility

The licenses of individual tools and projects vary. Many listed projects, such as lit-gpt, are Apache 2.0 licensed, promoting broad compatibility. However, users must check the specific license of each tool for commercial use or closed-source integration.

Limitations & Caveats

As a curated list, it does not provide a unified interface or guarantee compatibility between the various tools and resources mentioned. Users must evaluate each component individually for their specific needs and technical environment.