Awesome LLMs Fine-Tuning

Welcome to the curated collection of resources for fine-tuning Large Language Models (LLMs) like GPT, BERT, RoBERTa, and their numerous variants! In this era of artificial intelligence, the ability to adapt pre-trained models to specific tasks and domains has become an indispensable skill for researchers, data scientists, and machine learning practitioners.

Large Language Models, trained on massive datasets, capture an extensive range of knowledge and linguistic nuances. However, to unleash their full potential in specific applications, fine-tuning them on targeted datasets is paramount. This process not only enhances the models’ performance but also ensures that they align with the particular context, terminology, and requirements of the task at hand.

In this awesome list, we have meticulously compiled a range of resources, including tutorials, papers, tools, frameworks, and best practices, to aid you in your fine-tuning journey. Whether you are a seasoned practitioner looking to expand your expertise or a beginner eager to step into the world of LLMs, this repository is designed to provide valuable insights and guidelines to streamline your endeavors.

Table of Contents

• GitHub projects
• Articles & Blogs
• Online Courses
• Books
• Research Papers
• Videos
• Tools & Software
• Conferences & Events
• Slides & Presentations
• Podcasts

GitHub projects

• AutoTrain - No code fine-tuning of LLMs and other machine learning tasks.
• LlamaIndex 🦙: A data framework for your LLM applications. (23010 stars)
• Petals 🌸: Run LLMs at home, BitTorrent-style. Fine-tuning and inference up to 10x faster than offloading. (7768 stars)
• LLaMA-Factory: An easy-to-use LLM fine-tuning framework (LLaMA-2, BLOOM, Falcon, Baichuan, Qwen, ChatGLM3). (5532 stars)
• lit-gpt: Hackable implementation of state-of-the-art open-source LLMs based on nanoGPT. Supports flash attention, 4-bit and 8-bit quantization, LoRA and LLaMA-Adapter fine-tuning, pre-training. Apache 2.0-licensed. (3469 stars)
• H2O LLM Studio: A framework and no-code GUI for fine-tuning LLMs. Documentation: https://h2oai.github.io/h2o-llmstudio/ (2880 stars)
• Phoenix: AI Observability & Evaluation - Evaluate, troubleshoot, and fine tune your LLM, CV, and NLP models in a notebook. (1596 stars)
• LLM-Adapters: Code for the EMNLP 2023 Paper: "LLM-Adapters: An Adapter Family for Parameter-Efficient Fine-Tuning of Large Language Models". (769 stars)
• Platypus: Code for fine-tuning Platypus fam LLMs using LoRA. (589 stars)
• xtuner: A toolkit for efficiently fine-tuning LLM (InternLM, Llama, Baichuan, QWen, ChatGLM2). (540 stars)
• DB-GPT-Hub: A repository that contains models, datasets, and fine-tuning techniques for DB-GPT, with the purpose of enhancing model performance, especially in Text-to-SQL, and achieved higher exec acc than GPT-4 in spider eval with 13B LLM used this project. (422 stars)
• LLM-Finetuning-Hub : Repository that contains LLM fine-tuning and deployment scripts along with our research findings. :star: 416
• Finetune_LLMs : Repo for fine-tuning Casual LLMs. :star: 391
• MFTCoder : High Accur