LLM Fine-Tuning Course – From Supervised FT to RLHF, LoRA, and Multimodal
Learn how to tailor massive models to specific tasks with this comprehensive, deep dive into the modern LLM ecosystem. You will progress from the core foundations of supervised fine-tuning to advanced alignment techniques like RLHF and DPO, ensuring your models are both capable and helpful. Through hands-on practice with the Hugging Face ecosystem and high-performance tools like Unsloth and Axolotl, you’ll gain the technical edge needed to implement parameter-efficient strategies like LoRA and QLoRA.Code: https://github.com/sunnysavita10/Complete-LLM-Finetuning
Course developed by @sunnysavita10
❤️ Support for this channel comes from our friends at Scrimba – the coding platform that's reinvented interactive learning: https://scrimba.com/freecodecamp
⭐️ Chapters ⭐️
- 00:00:00 Introduction & Course Syllabus
- 00:03:42 LLM Training Pipeline Overview
- 00:05:01 Parameter Level Fine-Tuning: Full vs. Partial
- 00:07:22 Partial Fine-Tuning: Old School vs. Advanced Methods
- 00:10:07 Parameter Efficient Fine-Tuning (PEFT): LoRa & QLoRa
- 00:13:01 Advanced PEFT Techniques: DoRA, IA3, & BitFit
- 00:17:34 Data Level Fine-Tuning: Instructional vs. Non-Instructional
- 00:19:55 Preference Based Learning: RLHF & DPO
- 00:24:25 Deep Dive: Unsupervised Pre-training (Self-Supervised Learning)
- 00:30:45 Deep Dive: Non-Instructional Fine-Tuning & Domain Adaptation
- 00:40:48 Data Preparation for Non-Instructional Fine-Tuning
- 00:42:51 Deep Dive: Instructional Fine-Tuning & Chatbot Creation
- 00:47:57 Deep Dive: Preference Alignment with Human Feedback
- 00:50:38 Family-wise LLM Breakdown: Llama, GPT, Gemini, & DeepSeek
- 00:55:23 Practical Setup: Essential Libraries & GPU Connection
- 01:08:56 Working with Pre-built vs. Custom Custom Data Sets
- 01:21:02 Model Selection, Tokenization, & Padding Explained
- 01:26:11 Defining Training Arguments: Epochs, Learning Rate, & Batch Size
- 01:32:38 Executing Fine-Tuning with LoRa
- 01:42:35 Post-Training: Model Prediction & Inferencing
- 01:45:15 Part 2: Comprehensive Guide to Instructional Fine-Tuning
- 02:16:32 Loading & Unzipping Previous Training Checkpoints
- 02:30:13 Masking Labels for Improved Instructional Responses
- 02:40:02 Part 3: Preference Alignment & DPO Training
- 02:56:07 Preference Optimization Techniques: RLHF, RL AIF, & DPO
- 03:02:40 DPO Intuition: Understanding the Training Loss Formula
- 03:07:44 Practical DPO Implementation & Avoiding LoRa Stacking
- 03:37:30 Introduction to the Llama Factory Project
- 03:51:09 Setup & Setting up Llama Factory via GitHub
- 04:03:19 Using Llama Factory Web UI: Selecting Models & Data
- 04:29:44 Training via CLI: Configuration via YAML Files
- 04:37:55 Unsloth Framework: Achieving 2x Faster Training
- 04:57:33 Inside Unsloth: Custom Kernels & Memory Efficiency
- 05:14:14 Practical Walkthrough: Fine-Tuning with Unsloth
- 05:32:08 Enterprise Fine-Tuning via OpenAI API
- 05:48:06 Preparing & Validating JSONL Data for OpenAI
- 06:21:55 Creating and Monitoring OpenAI Fine-Tuning Jobs
- 06:52:20 Google Cloud Vertex AI: Fine-Tuning Gemini Models
- 07:22:41 Data Management in Google Cloud Storage Buckets
- 08:31:01 Embedding Fine-Tuning Masterclass
- 08:38:40 Multimodal AI: Image, Video, & Audio Modalities
- 09:13:48 Vision Transformer (ViT) Architecture Deep Dive
- 09:58:48 Keyword Search vs. Semantic Similarity
- 11:24:45 Step-by-Step: The Modern Text Embedding Process
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