Testing Fine Tuned Open Source Models in LangSmith

Ryan Brandt, CTO and co-founder of ChatOpenSource, discusses using LangSmith, a platform by LangChain, to bring Large Language Model (LLM) applications into production effectively. He highlights the increasing capability of open-source models like Mistral 7b and Llama2, emphasizing the future potential of easily swapping models in applications. Brandt outlines a process for fine-tuning and evaluating models, such as Llama2-7b and Llama2-13b, using LangSmith to automate evaluations and compare performance across different models using datasets. The LangSmith platform streamlines dataset evaluation with a user-friendly UI and API, enabling developers to assess model performance efficiently. Brandt's findings reveal a relationship between model parameters, training data volume, and performance, noting that llama2 models can perform comparably to GPT-3.5-turbo-base under certain conditions. This process underscores the potential of open-source models to compete with established models and the importance of efficient model evaluation tools like LangSmith.

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