> ## Documentation Index
> Fetch the complete documentation index at: https://docs.coreweave.com/llms.txt
> Use this file to discover all available pages before exploring further.

# Datasets

> Build reproducible train and validation data from task traces or an existing W&B project.

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.

<Card title="Create your first dataset" href="/model-distillation/studio/datasets-quickstart" arrow="true">
  Follow the step-by-step guide from source selection through row inspection.
</Card>

## Choose a source

### Task traffic

Use the current task's traces when the proxy recorded the traffic. You can filter by:

* task version;
* resolved provider and model;
* scalar trace metadata;
* fixed start and end timestamps.

Task-source reads use task-attributed Agent Spans and reconstruct the exact provider-shaped Chat Completions request and response.

### Existing W\&B project

Choose an explicit entity and project to import compatible self-logged Weave calls that do not have Model Distillation task identity.

## Sampling and split

* **Maximum traces** caps the random sample after filtering.
* **Validation data** reserves a fraction for evaluations; the remaining rows are training data.
* Split assignment is deterministic and grouped by scenario, agent run, conversation, or trace. Related examples never leak across train and validation.

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

## 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:

* text in input or original output;
* train or validation split;
* relabel status;
* whether an evaluation was applied.

Delete clearly invalid entries before training. Deleting a row does not alter the source trace.

## Append new traffic

Use **Append** with a later fixed range to grow a dataset incrementally. 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 are not eligible for training.

<Accordion title="API: List datasets for a task (GET /tasks/{alias}/datasets)">
  Agents can discover ready dataset IDs before starting relabeling, training, or evaluation:

  ```bash theme={"system"}
  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](/model-distillation/studio/datasets-quickstart). See [List datasets](/model-distillation/reference/management) for this operation.
</Accordion>