How to go from your agent's traces to a fine-tuned model in one workflow
This walkthrough introduces LangSmith Fine-Tuning and the SmithTune CLI, providing a single, end-to-end workflow to convert agent traces (trajectories) into custom, fine-tuned models. The process involves dataset creation and preparation from LangSmith trajectories, training using providers like Fireworks or Baseten, and subsequent evaluation, all managed through the SmithTune CLI. The overall workflow is: Create $\rightarrow$ Prepare $\rightarrow$ Plan $\rightarrow$ Train $\rightarrow$ Evaluate $\rightarrow$ Deploy.
Key takeaways
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Supervised Fine-Tuning (SFT) Purpose
SFT teaches a model desired behavior (e.g., tool selection, workflow adherence) using 'golden examples' derived from agent traces, improving task accuracy and tool reliability.
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Trajectory Format Importance
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The LangSmith trajectory format is critical because it provides a detailed reconstruction of the agent's context, including tools available at each turn, which is necessary for post-training jobs.
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SmithTune Workflow Overview
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The process starts by pulling trajectories from a LangSmith tracing project, defining a task-specific rubric, having a model council judge the traces, and then pushing the approved traces back to LangSmith to form a training dataset.
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Training and Evaluation
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After preparing the dataset (specifying provider, base model, and splits), the user runs `smithtune plan` to review settings, followed by `smithtune train` to execute the job. Evaluation compares the fine-tuned model against the base model on held-out test data.
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Deployment
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If results meet quality targets, `smithtune deploy` is used to serve the tuned model and connect it to the application. Otherwise, the process iterates by refining examples or training settings.
