Fine-tuning Large Language Models with UNSLOth: A Comprehensive Guide
Learn how to fine-tune large language models using UNSLOth, a parameter-efficient method, and improve model performance with hybrid RAG techniques.
Course Timeline
🎥 Introduction: Fine-tuning vs. Retrieval Augmented Generation
Overview of fine-tuning and RAG, highlighting their strengths and weaknesses. Introduces the concept of model fine-tuning as an alternative to retraining from scratch.
🚀 What is Model Fine-tuning?
Detailed explanation of model fine-tuning, including its advantages (less data required) and challenges (finding the right balance of adjustment).
💡 Supervised Fine-tuning and its Limitations
Explores supervised fine-tuning, discussing its use of labeled datasets, and limitations when dealing with entirely new information or domains.
⚙️ Parameter-Efficient Fine-tuning Techniques: LoRA, QLoRA
Introduces parameter-efficient fine-tuning techniques like LoRA, QLoRA, and their advantages in terms of resource efficiency and reduced storage requirements. Uses analogies to illustrate the concepts.
🛠️ UNSLOth: A Poor Man's Fine-tuning Solution
Deep dive into UNSLOth, its capabilities, and why it's considered a 'poor man's' solution. Explains its advantages for users with limited resources.
👨💻 Fine-tuning with UNSLOth: A Step-by-Step Guide
Practical demonstration of fine-tuning a model using UNSLOth, including data preparation, parameter configuration, and training.
📊 Evaluating Model Performance: G-Eval and BLEURT Score
Explanation of model evaluation methods like G-Eval and BLEURT score, along with their application in assessing the fine-tuned model's performance.
🤝 Hybrid RAG: Combining Fine-tuning and Retrieval
Explores hybrid RAG systems, which combine fine-tuning and RAG for improved accuracy and reduced hallucinations. Details the corrective RAG algorithm.
🎯 Conclusion: Hybrid RAG with Fine-tuning for Enhanced Performance
Summary of findings and the advantages of using hybrid RAG systems with fine-tuning to improve overall model performance.