Datasets - CoreWeave Docs

A dataset is a durable snapshot of usable model interactions. Model Distillation stores entries in Postgres for inspection and evaluation, then exports immutable artifacts when training starts.

Create your first dataset

Follow the step-by-step guide from source selection through row inspection.

Choose a source

Studio builds datasets from the project’s own traffic. The API also accepts an existing W&B project as a source. The following sections describe both.

Project traffic

When the proxy recorded the traffic, use the current project’s traces. In Studio, the Source data section of the Create Dataset dialog lets you choose the following settings:

Project version: Pins the prompt and routing regime used to select traces.
Served by: Keeps only traces that one model served. To keep training data teacher-only, select the teacher. All models mixes the outputs of every model that served the version.
After (UTC) and Before (UTC): A fixed time window. Studio prefills the last 14 days as fixed timestamps.
Maximum traces: The random sample size after filtering. The default is 20,000 and the maximum is 100,000.

Through the API, filters also accepts scalar trace metadata. Project-source reads use project-attributed Agent Spans and reconstruct the exact provider-shaped Chat Completions request and response.

Existing W&B project

To import compatible self-logged Weave calls that don’t have Model Distillation project identity, set source to an object with entity and project in the API request instead of the string "task". This source isn’t available in the Studio dialog.

Sampling and split

Whichever source you choose, the following settings control how many rows the dataset contains and how those rows are divided between training and test.

Maximum traces caps the random sample after filtering.
Train / test split reserves test rows for evaluations and model comparisons. The remaining rows are training data. The default is 90% training and 10% test.

Split assignment is deterministic and grouped by scenario, agent run, conversation, or trace. Related examples don’t leak across train and test, and Model Distillation deduplicates inputs automatically.

Use a fixed date range. A moving window would make the same dataset configuration produce different training data later.

Inspect and filter rows

The dataset workbench shows messages, original output, relabeled output, comparison-model output, token estimates, split, and per-row evaluation results. Filters support the following criteria:

Text in input or original output
Train or test split
Relabel status
Whether an evaluation was applied

Delete invalid entries before training. Deleting a row doesn’t alter the source trace.

Append new traffic

To grow a dataset incrementally, use Append with a later fixed range. Model Distillation deduplicates entries by trace and turn. Appending increments the dataset revision and makes older relabel or evaluation results stale when their coverage no longer matches.

What is excluded

Errored traces, incomplete streams, invalid Chat Completions shapes, and traces whose fidelity mapping is incomplete aren’t eligible for training.

API: List datasets for a project (GET /tasks/{alias}/datasets)

Agents can discover ready dataset IDs before starting relabeling, training, or evaluation:

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

To create or append data, follow the copyable request in Datasets Quick Start. For reference details about this operation, see List datasets.

Create your first dataset

​Choose a source

​Project traffic

​Existing W&B project

​Sampling and split

​Inspect and filter rows

​Append new traffic

​What is excluded

Choose a source

Project traffic

Existing W&B project

Sampling and split

Inspect and filter rows

Append new traffic

What is excluded