Lessons From Fine-Tuning Llama-2

In recent developments, large open language models have achieved remarkable advancements, unlocking new possibilities for commercially-scalable enterprise applications. Among these models, Meta's Llama-2 series has set a new benchmark for open-source capabilities. While comprehensive language models like GPT-4 and Claude-2 offer versatile utility, they often exceed the needs of specialized applications. This presentation will explore our insights gained from fine-tuning open-source models for task-specific applications, demonstrating how tailored solutions can outperform even GPT-4 in specialized scenarios. We'll also discuss how leveraging Anyscale + Ray's suite of libraries has enabled efficient fine-tuning processes, particularly in an era where GPU availability presents a critical bottleneck for many organizations.

Takeaways:

• Where to apply fine-tuning and when would it shine?

• How to set up an LLM fine-tuning problem?

• How does Ray and its libraries help with building a fine-tuning infrastructure?

• What does it take to do parameter efficient fine-tuning?

• How does Anyscale platform help with LLM-based fine tuning?