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Training produces an output artifact bundle containing the trained model, evaluation metrics, and result plots. These artifacts are written to the local output directory for the run. If object-store / huggingface upload is enabled, the same directory is packaged and uploaded as a zip artifact. Training also runs two evaluations:
  • Pre-training baseline: evaluates the base model before fine-tuning
  • Post-training: evaluates the fine-tuned model

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Artifacts on disk

Artifacts are written under: {training.output.local_path}/{training.output.model_name}/ This output directory contains the trained model artifacts, evaluation results, and supporting files needed to inspect or reuse the run. If object-store upload is enabled, this directory is packaged and uploaded as a zip artifact.

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Detailed artifact list

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Model artifacts

  • model weights / adapter files
  • Tokenizer files
  • model card or metadata files when generated

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Prompt and metrics

  • prompt_template.txt: Contains the prompt template to be used for registration / deployment
  • training_metrics.json: Contains the summarized metrics report (F1 scores, confusion matrices etc)

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Evaluation plots

  • stored under plots/ sub-directory
  • ROC curves
  • PR curves
  • confusion matrix and related multi-class plots when applicable
To run evaluation again later using a different dataset, see Evaluate.