LLM Fine-Tuning for Beginners: HuggingFace & Unsloth

LLM Fine-Tuning for Beginners: HuggingFace & Unsloth is a beginner-friendly, hands-on course that takes you from understanding how AI models work all the way to fine-tuning state-of-the-art LLMs using the latest techniques — LoRA, QLoRA, DPO, and GRPO — on both CUDA GPUs and Apple Silicon.

No prior machine learning experience required. You will start from the very basics and progressively build up to advanced fine-tuning techniques used in production AI systems today.

What You’ll Learn

1. Introduction to Machine Learning & Natural Language Processing (NLP) Libraries

Discover how to process, analyze, and derive insights from textual data using popular NLP tools.

2. In-Depth Understanding of the Transformers Library

Dive deep into HuggingFace’s Transformers, the gold standard for building state-of-the-art NLP and LLM solutions.

3. Evaluating AI Models

Measure performance using robust metrics and refine your models for optimal results.

4. Fine-Tuning BERT for Text Classification

Customize pre-trained models or build your own from scratch with Full training of a model

5. Fine-Tuning DistilBERT for Q&A

Understand how to fine-tune a DistilBERT model for Q&A classification with SQuAD format dataset with HuggingFace library

6.BERT + LoRA and QLoRA for Text Classification

Understanding LoRA (Low Rank Adaptation) and QLoRA (Quantized Low Rank Adaptation) for efficient training of LLMs on consumer grade GPUs

7. Fine-Tuning Qwen with LoRA and QLoRA on both CUDA and MLX on Apple Silicon

Understand how to fine-tune Qwen models on CUDA and MLX frameworks

8. DPO and GRPO — Alignment and Reinforcement Learning

Understand DPO and GRPO instead of relaying on SFT alone and how its going to help building and fine-tuning your custom AI Model

Tools and Frameworks You Will Master

• HuggingFace Transformers — model loading, tokenization, training

• TRL — SFTTrainer, DPOTrainer, GRPOTrainer

• PEFT — LoRA and QLoRA configuration

• Unsloth — 2x faster training, 30-40% less VRAM on CUDA

• Unsloth MLX — native Apple Silicon training via Metal

• BitsAndBytes — 4-bit NF4 quantization for QLoRA

• RunPod — cloud GPU setup, SSH via VSCode, per-second billing

• HuggingFace Hub — push merged models for deployment

Real World Project — QA360 Framework

Throughout the course you will build the QA360 Framework — a fine-tuned LLM that thinks like a senior QA engineer:

• Generate custom QA360 dataset using Claude API

• Build SFT, DPO, and GRPO datasets progressively

• Train the same model across all techniques

• Compare output quality across SFT, DPO, and GRPO

• See the model produce structured test analysis for any software requirement

By the end of this course, you’ll be equipped with the knowledge and practical experience to confidently develop, test, and optimize your own Transformer-based models and LLMs, setting you on an exciting path in the rapidly evolving world of AI.