Fine-tuning - CoreWeave Docs

Fine-tuning creates a task-specific model through W&B Serverless Training and records the resulting hosted artifact in Studio.

Train your first model

Choose a dataset, base model, and training configuration in Studio.

Supported base models

See Supported training models for available models and selection guidance. Studio can display the base model for an existing fine-tune even when the model is no longer available for new training runs.

Create a fine-tune

Choose:

A ready dataset with training rows
Original outputs or a completed relabel run
A base model
An optional fine-tune name
Optional training parameters

Names are normalized to lowercase hyphenated identifiers. When omitted, Studio generates a readable name.

Training parameters

Open the advanced configuration only when you need control over:

Field	Meaning
Maximum examples	Deterministic cap on training rows. Validation is not capped.
Batch size	Positive integer or `auto`
Epochs	Number of complete passes over the training set. Defaults to 1.
Peak learning rate	Positive scalar used to build the default schedule
Learning-rate schedule	Explicit non-negative value for every optimizer step
Assistant turns	Train on all assistant turns or only the last turn
Experimental configuration	Serverless Training options not yet modeled as first-class fields

With a scalar peak learning rate, Model Distillation creates the same schedule used by the ART SFT helper: 10% linear warmup followed by linear decay to zero. Supplying a full list preserves it exactly.

Status and artifacts

Fine-tunes move through Queued, Training, Deployed, or Failed. Training progress is reported from 0 to 100%. A deployed model includes an exact W&B artifact reference and is immediately selectable through wandb-inference for evaluations and routing. Deleting the Studio record does not delete the W&B Run or hosted artifact.

API: List training jobs and artifacts (GET /tasks/{alias}/finetunes)

Use this request to check progress, terminal status, and the artifact reference used in evaluations and routing:

curl --url "https://distillation.training.wandb.ai/v1/tasks/ticket-classifier/finetunes" \
  --header "Authorization: Bearer $WANDB_API_KEY" \
  --header "Wandb-Entity: your-team"

To start a job, use the request in Fine-tuning Quick Start. See List fine-tunes for this operation.

Last modified on September 2, 2026

Train your first model

​Supported base models

​Create a fine-tune

​Training parameters

​Status and artifacts

Supported base models

Create a fine-tune

Training parameters

Status and artifacts