Building Real-World LLM Products with Fine-Tuning and More with Hamel Husain
Published:Jul 23, 2024 21:02
•1 min read
•Practical AI
Analysis
This podcast episode from Practical AI features Hamel Husain, founder of Parlance Labs, discussing the practical aspects of building LLM-based products. The conversation covers the journey from initial demos to functional applications, emphasizing the importance of fine-tuning LLMs. It delves into the fine-tuning process, including tools like Axolotl and LoRA adapters, and highlights common evaluation pitfalls. The episode also touches on model optimization, inference frameworks, systematic evaluation techniques, data generation, and the parallels to traditional software engineering. The focus is on providing actionable insights for developers working with LLMs.
Key Takeaways
- •Fine-tuning is a crucial technique for adapting LLMs to specific use cases.
- •Systematic evaluation and data curation are essential for improving LLM applications.
- •Model optimization and inference frameworks play a key role in deploying LLM-based products.
Reference
“We discuss the pros, cons, and role of fine-tuning LLMs and dig into when to use this technique.”