A comprehensive guide to parameter-efficient fine-tuning of Large Language Models. Learn when to fine-tune vs prompt engineer, dataset preparation best practices, and deployment with vLLM and TensorRT-LLM.
Introduction
In this article, we explore the key concepts and practical applications of fine-tuning llms for enterprise: lora, qlora, and beyond. Whether you're a seasoned developer or just getting started, this guide will provide valuable insights.
Key Takeaways
- Understanding the fundamentals and core principles
- Best practices for production environments
- Performance optimisation techniques
- Common pitfalls and how to avoid them
- Real-world implementation examples
Conclusion
We hope this article has provided you with a solid foundation for understanding and implementing these concepts in your own projects. Stay tuned for more technical deep-dives from the Datapin team.
