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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
🎉 Thanks to our Champion and Sponsor supporters:
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