Using LangSmith to Support Fine-tuning

The text discusses the process of fine-tuning and evaluating large language models (LLMs) using LangSmith for dataset management and evaluation. It covers both open source LLMs on CoLab and HuggingFace, as well as OpenAI's new finetuning service. The guide demonstrates fine-tuning LLaMA2-7b-chat and gpt-3.5-turbo for an extraction task using training data exported from LangSmith. It also provides insights on when to fine-tune, how to do it efficiently, and the evaluation process. The results show that fine-tuning small open source models on well-defined tasks can outperform much larger generalist models.