Fine-tuning Large Language Models with Parameter-Efficient Methods and Hybrid RAG
A comprehensive guide to fine-tuning LLMs using parameter-efficient techniques like LoRA and QLoRA, and integrating them with Hybrid RAG for improved performance.
Course Timeline
🤔 Introduction to Fine-tuning and RAG
Explains the concepts of fine-tuning and Retrieval Augmented Generation (RAG), comparing their strengths and weaknesses.
🔬 Fine-tuning Techniques: Supervised Fine-tuning, LoRA, and QLoRA
Details different fine-tuning approaches: supervised fine-tuning, LoRA, and QLoRA, highlighting their advantages and disadvantages in terms of resource consumption and performance.
🛠️ UNSloth: A Practical Fine-tuning Tool
Introduces UNSloth, a user-friendly tool for fine-tuning LLMs on consumer-grade hardware, emphasizing its memory efficiency and ease of use.
👨💻 Fine-tuning Process with UNSloth: Data Preparation and Hyperparameter Tuning
A step-by-step walkthrough of the fine-tuning process using UNSloth, including data preparation, JSON formatting, hyperparameter selection (learning rate, batch size, etc.), and training configuration.
📊 Model Evaluation: G-Eval, BLEURT, and Human Feedback
Explores various evaluation methods for assessing the performance of fine-tuned LLMs, including G-Eval, BLEURT, and human feedback.
🚀 Hybrid RAG: Combining Fine-tuning and RAG for Enhanced Performance
Introduces Hybrid RAG, combining the strengths of fine-tuning and RAG to reduce hallucinations and improve accuracy. Details implementation and results.
✅ Conclusion and Next Steps
Summarizes the key takeaways from the video and suggests next steps for viewers interested in further exploring these techniques.