Axolotl helps fine-tune LLMs while MLflow tracks experiments, metrics, artifacts, and model versions.
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Configure Axolotl to log metrics and checkpoints to MLflow during fine-tuning. Register experiments with unique run IDs, store hyperparameters, and record outputs. MLflow then manages version control and enables rollback or promotion to production environments. This setup ensures reproducibility across LLM training workflows.
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Integrate Axolotl using MLflow callbacks so each training job automatically logs parameters and results. Upload best models to MLflow’s registry and connect deployment tools from there. This makes model lifecycle management easier for large language model development teams.
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