LLM Fine-Tuning Complete
Build Your Own Custom GPT
Course Code: FINETUNE-401
Duration: 10 Weeks
Level: Advanced
Platform: inskillflow Academy
Course Overview
Welcome to the most comprehensive course on LLM Fine-Tuning! This advanced program will take you from understanding the fundamentals of fine-tuning to deploying production-ready custom language models.
Whether you're an ML Engineer looking to specialize in LLMs, a researcher exploring state-of-the-art techniques, a developer building AI-powered applications, or an AI practitioner wanting to create domain-specific models, this course provides the complete knowledge and hands-on experience you need.
Learning Objectives
By the end of this course, you will be able to:
- Understand when to fine-tune vs use RAG or prompting
- Master Parameter-Efficient Fine-Tuning (PEFT) techniques
- Implement LoRA and QLoRA for memory-efficient training
- Prepare high-quality training datasets
- Train models using Hugging Face Transformers and TRL
- Apply advanced techniques like DPO and ORPO
- Evaluate and merge fine-tuned models
- Deploy fine-tuned models to production
Prerequisites
This is an advanced course. You should have:
- Strong Python programming skills
- Familiarity with PyTorch or TensorFlow
- Basic understanding of transformer architecture
- Experience with Hugging Face libraries (helpful but not required)
- Access to GPU resources (Google Colab free tier works for many exercises)
Teaching Methodology
80% Practical Application - 20% Theory
Each module combines:
- Clear theoretical explanations with mathematical foundations
- Production-ready code examples
- Google Colab compatible notebooks
- Interactive quizzes (30 questions per module)
- Real-world case studies and best practices
Course Structure - 10 Modules
Module 1: Introduction to Fine-Tuning
Duration: 1 Week
Content:
- What is Fine-Tuning?
- When to Fine-Tune vs Use RAG
- Fine-Tuning Landscape
Objectives:
- Understand transfer learning and fine-tuning concepts
- Make informed decisions about when to fine-tune
- Overview of available methods and tools
Module 2: Full Fine-Tuning
Duration: 1 Week
Content:
- Traditional Fine-Tuning Approach
- Computational Requirements
- Catastrophic Forgetting
Objectives:
- Understand full parameter updates
- Calculate GPU memory requirements
- Learn about forgetting and mitigation strategies
Module 3: Parameter-Efficient Fine-Tuning (PEFT)
Duration: 1 Week
Content:
- Introduction to PEFT
- PEFT vs Full Fine-Tuning
- PEFT Methods Overview
Objectives:
- Understand the motivation behind PEFT
- Compare different PEFT methods
- Know when to use each approach
Module 4: LoRA Deep Dive
Duration: 1 Week
Content:
- Understanding LoRA
- LoRA Mathematics
- Implementing LoRA with PEFT Library
Objectives:
- Master Low-Rank Adaptation concepts
- Understand the mathematics behind LoRA
- Implement LoRA training with Hugging Face
Module 5: QLoRA and Quantization
Duration: 1 Week
Content:
- What is QLoRA?
- 4-bit Quantization (bitsandbytes)
- Memory-Efficient Training
Objectives:
- Understand quantization for training
- Use bitsandbytes for 4-bit loading
- Optimize memory usage for large models
Module 6: Data Preparation
Duration: 1 Week
Content:
- Dataset Formats (Alpaca, ShareGPT)
- Data Quality and Curation
- Synthetic Data Generation
Objectives:
- Format data for instruction tuning
- Apply quality filtering strategies
- Generate synthetic training data
Module 7: Training with Hugging Face
Duration: 1 Week
Content:
- Transformers Library
- TRL (Transformer Reinforcement Learning)
- Training Arguments and Optimization
Objectives:
- Use SFTTrainer for fine-tuning
- Configure training hyperparameters
- Implement logging and monitoring
Module 8: Advanced Fine-Tuning Techniques
Duration: 1 Week
Content:
- Instruction Tuning
- DPO (Direct Preference Optimization)
- ORPO and Other Methods
Objectives:
- Implement instruction tuning pipelines
- Train models with preference optimization
- Compare DPO, ORPO, and RLHF
Module 9: Evaluation and Merging
Duration: 1 Week
Content:
- Evaluating Fine-Tuned Models
- Model Merging Techniques
- Ablation Studies
Objectives:
- Evaluate model quality comprehensively
- Merge multiple adapters and models
- Design and run ablation experiments
Module 10: Production Deployment
Duration: 1 Week
Content:
- Exporting and Serving Models
- Optimization for Inference
- Building Custom GPT Products
Objectives:
- Export models for deployment
- Optimize inference performance
- Build production-ready AI applications
Course Progress
| Status | Module | Link |
|---|---|---|
| ✅ Complete | Module 1 - Introduction to Fine-Tuning | Start |
| ✅ Complete | Module 2 - Full Fine-Tuning | Start |
| ✅ Complete | Module 3 - Parameter-Efficient Fine-Tuning | Start |
| ✅ Complete | Module 4 - LoRA Deep Dive | Start |
| ✅ Complete | Module 5 - QLoRA and Quantization | Start |
| ✅ Complete | Module 6 - Data Preparation | Start |
| ✅ Complete | Module 7 - Training with Hugging Face | Start |
| ✅ Complete | Module 8 - Advanced Fine-Tuning Techniques | Start |
| ✅ Complete | Module 9 - Evaluation and Merging | Start |
| ✅ Complete | Module 10 - Production Deployment | Start |
🎉 Course Complete!
All modules are ready with complete content and interactive quizzes (30 questions per module).
📖 Additional Resources
| Resource | Description |
|---|---|
| 📚 Glossary | 150+ fine-tuning terms with definitions |
| 🔗 Resources | Notebooks, datasets, papers, and tools |
| 🎯 Final Exam | 50 comprehensive questions |
Start Now!
Explore the modules from the sidebar and begin your journey to mastering LLM Fine-Tuning!
Good luck with your learning journey!
Last updated: December 2024
📊 Course Statistics
| Item | Count |
|---|---|
| Modules | 10 |
| Chapters | 30+ |
| Quiz Questions | 300 |
| Estimated Learning Hours | 50+ |