Datasets

BiOS supports multiple dataset formats and provides built-in validation, preview, and column mapping so your data is ready for training.

Supported Formats

Each training method expects specific columns:

Uploading Datasets

Uploading a dataset is the first step in any training workflow. The platform handles validation, format detection, and preview generation automatically.

1. Navigate to Datasets in the sidebar. You will see your existing datasets listed with their name, format, row count, size, source, and upload date.
2. Click Upload Dataset in the top right corner. A drop zone appears where you can drag and drop your file or click to browse your filesystem.
3. Select your file. The platform accepts files up to 200MB. A progress bar shows the upload status in real time.
4. Once uploaded, the platform automatically:
   • Detects the file format (JSONL, Parquet, or CSV)
   • Counts the total number of rows
   • Identifies all columns and their data types
   • Validates the structure for common issues (missing fields, malformed JSON, encoding errors)
5. A green Dataset is valid and ready for training banner confirms the file passed all validation checks.
6. If your columns do not match the expected names, adjust column mapping (see below) before using the dataset in a training job.

You can also import datasets from HuggingFace Hub using the Import from HF button. This opens a search interface where you can browse public datasets or access your private repos (after connecting your HuggingFace account under Integrations).

Format Validation

Before upload completes, the platform validates your dataset for common issues: missing required fields, inconsistent schemas across rows, encoding errors, and empty records. You can also use the standalone validation endpoint to check a file before uploading.

Column Mapping

If your dataset columns do not match the expected names, you can configure column mapping to tell the platform which of your columns corresponds to which expected field. This is common when importing datasets from external sources that use different naming conventions.

For example, if your SFT dataset has columns named "question" and "answer", map question → instruction andanswer → output. The mapping is saved with the dataset and applied automatically whenever it is used in a training job.

HuggingFace Import

You can import datasets directly from the HuggingFace Hub:

1. Public datasets: use the Hub Search to find and preview any public dataset, then import it with one click.
2. Private datasets: first connect your HuggingFace account under Integrations by adding your API token. Then browse your private repos and import from them.

Preview and Display

Each dataset has two preview modes: a paginated raw preview showing all columns and rows (supports pagination), and a display previewthat renders formatted instruction/response pairs for quick inspection.