LLM Fine-Tuning from Scratch

By the end of this module you will be able to identify suitable datasets for fine-tuning, apply data cleaning and preprocessing techniques, and format datasets effectively for various LLM fine-tuning tasks using the Hugging Face `datasets` library.

By the end of this module you will be able to differentiate between various fine-tuning strategies, understand the concept and benefits of Parameter-Efficient Fine-Tuning (PEFT), and implement LoRA for fine-tuning a pre-trained LLM on a specific task.

By the end of this module you will be able to understand and apply QLoRA for highly memory-efficient fine-tuning, set up a complete training pipeline using Hugging Face `Trainer`, and manage training parameters effectively.

By the end of this module you will be able to select appropriate metrics for evaluating generative LLMs, implement evaluation pipelines, and interpret model performance against common benchmarks.

By the end of this module you will be able to save and load fine-tuned models, explore various deployment options for LLMs, and build a basic API to serve your custom LLM for inference.