AI BriefingGuide to LLM Fine-Tuning Using LangSmith
Key point
A guide has been released to support the fine-tuning and evaluation processes of open-source and OpenAI models using LangSmith.
Details
The LangChain team published a guide on how to fine-tune LLMs by leveraging LangSmith as a dataset management and evaluation tool. This guide covers both open-source LLM training using CoLab and HuggingFace, as well as OpenAI's new fine-tuning service.
For test cases, LLaMA2-7b-chat and gpt-3.5-turbo were used to perform knowledge graph triple extraction tasks. The training data was exported from LangSmith, and the results were also evaluated through LangSmith.
Recently, the open-source LLM ecosystem has grown rapidly, with models emerging that achieve performance close to SOTA even on consumer laptops. Additionally, as OpenAI supports fine-tuning for its latest models, the value of small, specialized models optimized for specific domains is increasing.
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