This guide walks you through everything you need to know about working with datasets in Facet, from uploading your data to configuring field mappings and processing options.

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Uploading Your Dataset

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Supported File Formats

Facet supports a wide range of data formats for maximum flexibility:

CSV Files

Best for: Structured data, spreadsheets, tabular data
  • Comma-separated values
  • Headers in first row
  • UTF-8 encoding recommended

JSON/JSONL

Best for: Conversational data, complex structures - JSON: Single object with array of examples - JSONL: One JSON object per line - Nested structures supported

Parquet

Best for: Large datasets, efficient storage - Columnar format - Fast loading and processing - Compressed storage

Excel Files

Best for: Business data, multiple sheets
  • .xlsx and .xls formats
  • Multiple sheets supported
  • First sheet used by default

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File Size Limits

  • Maximum file size: 100MB per upload
  • Recommended size: 10-50MB for optimal processing speed
  • Large datasets: Consider splitting into multiple files or using Hugging Face Hub
Very large files may take longer to process. Consider using Hugging Face Hub for datasets over 100MB.

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Using Hugging Face Datasets

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Public Datasets

Import datasets directly from Hugging Face Hub using their repository name: 
Examples:
- microsoft/DialoGPT-medium
- HuggingFaceH4/ultrachat_200k
- tatsu-lab/alpaca
1

Find a Dataset

Browse Hugging Face Datasets to find one that fits your needs.
Look for datasets with conversation or instruction-following formats for best results.
2

Copy the Repository Name

Use the format organization/dataset-name from the dataset page.
You don’t need to include the full URL, just the repository identifier.
3

Select Subset (Optional)

If the dataset has multiple subsets, choose the one you want to use.
Common subsets include “train”, “test”, “validation”, or specific task names.

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Private Datasets

For private repositories, you’ll need to provide your Hugging Face token:
  1. Go to Hugging Face Settings
  2. Create a new token with “Read” permissions
  3. Enter the token when prompted during dataset import
Keep your Hugging Face token secure and never share it publicly.

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Configuring Field Mappings

Field mappings tell Facet how to transform your data into the conversational format needed for training.

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Understanding Field Types

Use for: Static text, instructions, prompts
{
  "type": "template",
  "value": "You are a helpful assistant. Answer the following question:"
}
Template fields use fixed text that doesn’t change between examples.

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Processing Modes Explained

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Language Modeling Mode

Best for: General conversation, instruction following, text generation This mode creates conversations with three roles:
  • System: Instructions or context for the model
  • User: Questions, prompts, or input from the user
  • Assistant: Model’s responses

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Prompt-Only Mode

Best for: Reasoning tasks, math problems, structured thinking This mode creates prompts without assistant responses, perfect for GRPO training where the model learns to reason step-by-step.

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Preference Tuning Mode

Best for: Aligning models with human preferences, RLHF This mode creates data with chosen and rejected responses for preference-based training algorithms.

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Vision Dataset Configuration

When working with images, Facet automatically detects image fields and enables vision processing.

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Supported Image Formats

  • File paths: Direct references to image files in your dataset
  • Base64 encoded: Images embedded as base64 strings
  • Hugging Face format: Images with bytes field (from HF datasets)
Images are always added to user messages only, and you can include multiple images in a single message.

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Next Steps

Once your dataset is processed and ready:
  1. Start Training: Use your processed dataset to fine-tune a Gemma model
  2. Review Results: Check the training progress and evaluation metrics
  3. Export Model: Download your fine-tuned model in various formats
  4. Deploy: Set up your model for production use
Ready to start training? Head to the Fine-tuning guide to learn how to train your model.