14 Free Large Language Models Fine-Tuning Notebooks

Getting Started with LLM Fune-Tunning through These Free Colab Notebooks

Fine-tuning large language models (LLMs) has become a crucial skill for NLP practitioners, enabling customization and improved performance across various tasks. This article introduces 14 free Colab notebooks that provide hands-on experience in fine-tuning LLMs. 

From efficient training methodologies like LoRA and Hugging Face to specialized models such as Llama, Guanaco, and Falcon, each notebook explores unique aspects of the fine-tuning process. Advanced techniques like PEFT Finetune, Bloom-560m-tagger, and Meta_OPT-6–1b_Model offer insights into state-of-the-art approaches. 

Whether you’re interested in GPT-Neo-X, MPT-Instruct-30B, or Microsoft Phi 15B, these notebooks cover a diverse range of LLMs, making them suitable for both beginners and experienced practitioners. Delve into custom dataset training, self-supervised methods, and RLHF techniques, gaining a comprehensive understanding of fine-tuning. 

This article provides a roadmap to navigate these notebooks, making it an essential read for anyone keen on mastering the art of fine-tuning large language models.

This article summarizes this awsome GitHub repo by Ashish Patel. He has done great effort in collecting and building this notebooks and I thought it will be more helpful to provide a detailed description to each notebook.