LLM Fine-Tuning with Hugging Face: LoRA, QLoRA, PEFT

About this course

Welcome to Fine Tuning LLM with Hugging Face Transformers for NLP, a practical and project-based course designed to help you understand and fine-tune modern Transformer models for real-world AI applications.

This course starts from the basics of Hugging Face Transformers and gradually takes you into advanced fine-tuning workflows. You will learn how pipelines work, how checkpoints and models are used, how Hugging Face datasets are loaded, and how Auto Classes simplify model loading, tokenization, training, and inference.

After building a strong foundation, you will go deeper into Transformer architecture. You will understand Seq2Seq models, attention mechanism, Q, K, V vectors, scaled dot-product attention, encoder-decoder stacks, positional encoding, self-attention, masked self-attention, cross-attention, and multi-head attention.

The course also covers BERT architecture in detail. You will learn how BERT processes input, how masked language modeling and next sentence prediction work, and how BERT is fine-tuned for downstream NLP tasks.

Then you will move into hands-on projects where you will fine-tune Transformer models for practical use cases such as sentiment classification, fake news detection, named entity recognition, text summarization, and image classification using Vision Transformers.

You will also learn knowledge distillation concepts using DistilBERT, MobileBERT, and TinyBERT. This will help you understand how smaller and faster Transformer models are created for real-world production use cases.

In the advanced sections, you will learn how to fine-tune LLMs on custom datasets using PEFT, LoRA, QLoRA, and 4-bit quantization. You will fine-tune models like Phi and LLaMA-style models for custom text generation and instruction/chat-based tasks.

The course also includes modern Audio LLM content using Qwen3-TTS. You will learn Qwen3-TTS architec

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