{"id":14743,"date":"2026-08-10T11:18:15","date_gmt":"2026-08-10T11:18:15","guid":{"rendered":"https:\/\/nokobox.com\/index.php\/item\/udemy-llm-fine-tuning-with-hugging-face-lora-qlora-peft-2026-6\/"},"modified":"2026-08-10T11:18:15","modified_gmt":"2026-08-10T11:18:15","slug":"udemy-llm-fine-tuning-with-hugging-face-lora-qlora-peft-2026-6","status":"publish","type":"digital_item","link":"https:\/\/nokobox.com\/index.php\/item\/udemy-llm-fine-tuning-with-hugging-face-lora-qlora-peft-2026-6\/","title":{"rendered":"Udemy \u2013 LLM Fine-Tuning with Hugging Face: LoRA, QLoRA, PEFT 2026-6"},"content":{"rendered":"
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Descriptions<\/h2>\n

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\u2019ll 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.<\/p>\n

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.<\/p>\n

What you\u2019ll learn<\/h3>\n