Model Fine-Tuning User Guide

Prepare datasets, choose base models, create fine-tuning jobs, manage adapters, evaluate outputs, and promote tuned models.

Who This Guide Is For

Where To Go

Page Use It For
/model-fine-tuning Fine-tuning dashboard.
/model-fine-tuning/new Create a fine-tuning job.
/model-fine-tuning/base-models Select base models.
/model-fine-tuning/datasets Fine-tuning datasets.
/model-fine-tuning/data Data preparation.
/model-fine-tuning/jobs Fine-tuning jobs.
/model-fine-tuning/adapters Adapter artifacts.
/model-fine-tuning/evaluation Evaluate tuned outputs.
/model-fine-tuning/settings Fine-tuning settings.

Core Concepts

Concept Meaning
Base model The foundation model selected for tuning.
Dataset Instruction, preference, classification, or task-specific training data.
Adapter A lightweight fine-tuned artifact such as LoRA where supported.
Job The training execution for a fine-tuning configuration.
Evaluation The validation stage before registration or deployment.

Common Workflows

Fine-tune a model

  1. Select base model.
  2. Choose or upload dataset.
  3. Validate schema and examples.
  4. Configure training method and resources.
  5. Launch job.
  6. Review metrics and artifacts.
  7. Evaluate tuned output.
  8. Register the approved artifact in ModelOps.

Manage adapters

  1. Open Adapters.
  2. Review model, dataset, and training metadata.
  3. Compare evaluation results.
  4. Archive stale adapters.
  5. Promote selected adapters for deployment.

Best Practices