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The release is a developer tool that turns LangSmith agent trajectories into SFT training data, then handles training on Fireworks or Baseten and evaluation in LangSmith. Its current scope is supervised fine-tuning only, so it doesn't yet cover preference-based or reinforcement methods. The vendor's claim that a fine-tuned model can match or beat frontier models on narrow tasks is plausible but unverified here, so readers should benchmark against their own tasks before adopting it.
Introducing LangSmith Fine-Tuning
LangChain introduces LangSmith Fine-Tuning and SmithTune, a CLI built for post-training models. Train specialized models without building data pipelines by hand.
Today we're launching LangSmith Fine-Tuning and smithtune, a CLI that helps teams turn LangSmith trajectories into custom fine-tuned models for their agents. It handles the entire fine-tuning process from one CLI: dataset creation and preparation from LangSmith trajectories, training with Fireworks or Baseten, and evaluation with LangSmith. You can run smithtune directly or work with your coding agent to run commands and inspect the results. smithtune is built for post-training models. It currently supports supervised fine-tuning (SFT) which trains a model using examples of good behavior. You give the model inputs/outputs and it learns by updating model weights to imitate that behavior. LangSmith trajectory data is designed to support SFT, and smithtune helps turn these trajectories into useful training data. This gives teams…
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