Udemy – LLM Fine-Tuning with Hugging Face: LoRA, QLoRA, PEFT 2026-6

Updated August 10, 2026 8.64 GB

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Descriptions

LLM Fine-Tuning with Hugging Face: LoRA, QLoRA, PEFT, Welcome to the ultimate practical and project-based course designed to help you master fine-tuning modern Large Language Models and Hugging Face Transformers for real-world natural language processing (NLP), computer vision, and audio applications. This comprehensive guide starts from the basics of the Hugging Face library, covering pipeline workflows, checkpoints, datasets, and Auto Classes, before diving deep into the core architecture of Transformers including attention mechanisms, encoder-decoder stacks, self-attention, and positional encoding. You’ll gain a thorough understanding of BERT, learning how masked language modeling and next sentence prediction work, and how it is fine-tuned for downstream tasks.

Through step-by-step hands-on projects, you will fine-tune Transformer models for key use cases like sentiment classification, fake news detection, named entity recognition, text summarization, and image classification using Vision Transformers. You will explore advanced model optimization and efficiency techniques like knowledge distillation using DistilBERT, MobileBERT, and TinyBERT, as well as parameter-efficient fine-tuning (PEFT), LoRA, QLoRA, and 4-bit quantization on custom datasets using models like Phi-2 and LLaMA style models. Additionally, the course features cutting-edge Audio LLM integration using Qwen3-TTS for voice cloning, emotion control, Whisper-based transcription, and supervised fine-tuning, equipping you with a complete skillset to deploy state-of-the-art AI models.

What you’ll learn

Understand Hugging Face Transformers and how Transformer models power modern NLP and Generative AI applications.
Use Hugging Face pipelines, checkpoints, datasets, tokenizers, Auto Classes, and Spaces for practical AI projects.
Learn Transformer architecture including attention, QKV vectors, encoder-decoder blocks, and positional encoding.
Fine-tune transformers for text classification, question answering, natural language inference, text summarization, and machine translation.
Understand BERT architecture, masked language modeling, next sentence prediction, and BERT fine-tuning.
Fine-tune BERT for multi-class sentiment classification and build a Streamlit app for real-time prediction.
Fine-tune DistilBERT, MobileBERT, and TinyBERT for fake news detection and performance benchmarking.
Fine-tune Transformer models for NER, text summarization, image classification, and custom NLP tasks.
Learn PEFT, LoRA, QLoRA, 4-bit quantization, and fine-tune LLMs on custom datasets.
Fine-tune LLaMA-style chat models and Qwen3-TTS audio models for voice cloning and custom speech generation.

Who this course is for

Python developers who want to learn Hugging Face Transformers, NLP, and LLM fine-tuning through hands-on projects.
Data scientists and machine learning engineers who want to fine-tune BERT, T5, ViT, LLaMA, and other models.
NLP engineers who want to build real-world Transformer projects for classification, NER, summarization, and generation.
AI engineers who want to learn PEFT, LoRA, QLoRA, custom LLM fine-tuning, and instruction tuning workflows.
Students and researchers who want to understand Transformer architecture, BERT, knowledge distillation, and LLM training.
Generative AI learners who want to explore text, vision, and audio model fine-tuning using Hugging Face.

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

Publisher : Udemy
Teacher : KGP Talkie | Laxmi Kant
Language : English
Level : Intermediate
Number of Course : 183
Duration : 20 hours and 25 minutes

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

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

Requirements

Basic Python programming knowledge is required to follow the coding projects and fine-tuning notebooks.
Basic understanding of machine learning or deep learning will be helpful but not strictly required.
Basic NLP knowledge is useful, but important concepts are explained step by step in the course.
A computer with internet access is required. Google Colab or a GPU machine is recommended for training.
No prior Hugging Face experience is needed. You will learn Transformers and fine-tuning from the basics.