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    LangSmith Fine-Tuning and smithtune CLI launch

    LangChain says agents’ existing traces can become training data without building a training pipeline by hand.

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    TLDR

    LangChain said on October 1 that it had launched LangSmith Fine-Tuning and the smithtune CLI the previous week. The company says the tools let developers turn an agent’s own trajectories into a fine-tuned model without building a training pipeline by hand. It also shared a quick demo.

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    6 Sources

    @LangChainLast week, we launched LangSmith Fine-Tuning and the smithtune CLI. Turn your agent’s own trajectories into a fine-tuned model without building a training pipeline by hand. Your traces already show your agent doing the job well. Now, that signal becomes training data. Quick demo from @jakebroekhuizen
    @ankush_gola11A huge part of post-training is picking and wrangling the right data. `smithtune` handles the entire lifecycle of agent traces -> fine-tuned model
    @Vtrivedy10we want every team to own this cycle: - log every agent action in Trajectories - use agents to help humans understand that data at scale - turn that data into Evals & Environments - do lots of Agent + Human review - Fine-tune much cheaper and faster open models that rock at those tasks
    @hwchase17RT @jakebroekhuizen: We spent a lot of time streamlining the process to go from your agent's trajectories to an SFT'd model on your trainin…

    6 Sources

    @LangChainLast week, we launched LangSmith Fine-Tuning and the smithtune CLI. Turn your agent’s own trajectories into a fine-tuned model without building a training pipeline by hand. Your traces already show your agent doing the job well. Now, that signal becomes training data. Quick demo from @jakebroekhuizen
    @ankush_gola11A huge part of post-training is picking and wrangling the right data. `smithtune` handles the entire lifecycle of agent traces -> fine-tuned model
    @Vtrivedy10we want every team to own this cycle: - log every agent action in Trajectories - use agents to help humans understand that data at scale - turn that data into Evals & Environments - do lots of Agent + Human review - Fine-tune much cheaper and faster open models that rock at those tasks
    @hwchase17RT @jakebroekhuizen: We spent a lot of time streamlining the process to go from your agent's trajectories to an SFT'd model on your trainin…