AI BriefingTesting Fine-Tuned Open Source Models on LangSmith
Key point
This introduces a process for systematically evaluating and comparing the performance of fine-tuned open source models using LangSmith.
Details
As the performance of open source models like Mistral 7b and Llama2 improves, the ability to quickly swap in and test the model best optimized for an application is becoming increasingly important. Developers need an efficient evaluation process to compare different versions of models.
LangSmith provides a UI and API that reduce the complexity of Python scripts, helping you easily create evaluation datasets and directly compare the performance of multiple models across various axes. Data can be conveniently uploaded via Python code or the UI.
In this case study, model evaluation proceeded through the following steps:
- Initialization: Set the goal of fine-tuning Llama2-7b and Llama2-13b models using Hugging Face's sql-create-context dataset
- Data conversion: Converted the JSON-format dataset into .jsonl format suitable for chat fine-tuning
- Data sampling: Used GPT-4's Code Interpreter to select 10,000 rows from the dataset
- Validation set creation: Selected 1,000 unique SQL rows that did not overlap with the training data as a validation set, then uploaded them to LangSmith and performed automated evaluation
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