The training set is the dataset that fine-tunes your base model. Generated training sets contain 2,000 labelled examples, and you can also add or reuse your own training data.Documentation Index
Fetch the complete documentation index at: https://docs.galileo.ai/llms.txt
Use this file to discover all available pages before exploring further.
The three training dataset sources
The step opens with a section heading Training data source and three selectable cards:Generate from test set
Uses 20% of your test set as seed examples to generate 2,000 labelled training examples. Recommended for first runs.
Add training logs
Upload or import your own raw production logs. If they are unlabelled, Luna Studio labels them before training.
Use existing training set
Reuse a previously generated, labelled, or uploaded training dataset from your workspace.
Generate from test set
This option creates a training set from your test set in three steps:- Configure generation and generate a sample dataset
- Review 50 sample rows and provide feedback to the generator
- Generate the final 2,000-example dataset once you are happy with the samples
Configure the generator
Configure generation with the following settings:
| Field | Notes |
|---|---|
| Test set (read-only) | Shows your selected test set with the caption “Uses 20% of test set as seed examples”. |
| Model | The LLM that generates the training samples. Options depend on the providers you have configured. Larger models usually produce better training data. |
| Output dataset name | Provide a name for the output dataset like project-ABC-metric-PQR-training-set-v1. Defaults to generated-training-set. |
| Metric (read-only) | Includes a View prompt popover so you can re-check the metric prompt. |
| Advanced settings | Optional generation settings. Keep the defaults for first runs. |
Review the sample data
Provide feedback and Regenerate samples
You can provide feedback by selecting the rows that look wrong and clicking the Regenerate button. Once you click the button, the Regenerate dataset modal opens with a radio group of reasons:| Reason | When to pick it |
|---|---|
| Samples are too repetitive | The generated rows look almost identical to each other. |
| Labels look incorrect | The labels don’t match what the inputs deserve. |
| Inputs are off-topic | The inputs don’t reflect the kind of data your application sees. |
| Provide own feedback | Free-form text area reveals — describe what’s wrong in your own words. |
Generate the final dataset
Once you’re happy with the samples, the footer button changes to Generate final dataset. Clicking it creates the full 2,000-example training set. When it completes, the drawer closes and Step 3 shows the Training set completed view (see below).Add training logs
The Add training logs path uploads or imports your own production logs. Clicking the card opens the Add training set modal — the same generic dataset source modal used elsewhere in the app, with three sources:Upload from local
Drag-and-drop a
.csv or .jsonl file.Fetch from URL
Paste an
http://, https://, s3://, or gs:// URL.Import from Galileo
Browse datasets in your connected Galileo workspace.
Use existing training set
The Use existing training set path lets you pick a previously generated, labelled, or uploaded training dataset from this workspace without regenerating data or importing a new file.Validation
Luna Studio runs validation on the training set to ensure it meets the required schema / format / content rules. If there are any validation errors, they will be highlighted (See example below). For more details, see Validation.Training set completed
After either flow finishes, the step replaces the picker with a Selected dataset card and (if available) a preview table.
Where to go next
Step 4 — Config and launch
Pick a base model and launch.
Training sets reference
Schema, validation rules, and sources.