Mar 13
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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: 👾 @omerhattapoglu1158 👾 @goddardtan 👾 @akihayashi6629 👾 @kikilogsin 👾 @anthonycampbell2148 👾 @tobymiller7790 👾 @rajibdassharma497 👾 @CloudVirtualizationEnthusiast 👾 @adilsoncarlosvianacarlos 👾 @martinmacchia1564 👾 @ulisesmoralez4160 👾 @_Oscar_ 👾 @jedi-or-sith2728 👾 @justinhual1290 -- Learn to code for free and get a developer job: https://www.freecodecamp.org Read hundreds of articles on programming: https://freecodecamp.org/news