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Dataset Preparation

When preparing your dataset for fine-tuning, keep these key principles in mind:

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Dataset Size and Quality

  • Aim for a minimum of 500-1000 representative examples
  • More high-quality examples may help increase performance
  • Examples in the training dataset should be similar to the requests at inference time
  • The dataset should contain a diverse set of examples

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Train-Test Split Best Practices

When creating train and evaluation splits for your dataset:
  • For larger datasets: Use 80% for training and 20% for evaluation
  • For smaller datasets: Use 90% for training and 10% for evaluation
  • Create a split column in your dataset with values:
    • train for training data
    • evaluation for evaluation data

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How Evaluation Sets are Used

The evaluation set serves several important purposes:
  • Helps track model performance on unseen data during training
  • Used to select the best checkpoint for your final model
  • If no evaluation set is provided:
    • All data will be used for training
    • Only training metrics will be reported
    • The final training checkpoint will be used for inference
  • When an evaluation set is provided:
    • The model is never trained on the evaluation set
    • Progress is tracked on both training and evaluation data
    • The checkpoint with the best evaluation performance (lowest loss) is selected
    • Helps prevent overfitting (lack of generalization) by monitoring the evaluation loss

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Instruction Formatting

Experimentation shows that using model-specific instruction (chat) templates significantly boosts performance. To learn more about how to use instruction templates with your data to improve performance, see our guide on Chat Templates.

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Large Dataset Handling

When working with large datasets (>100MB):
  • Use cloud storage solutions like Amazon S3, BigQuery, etc. instead of file uploads
  • Consider using pretokenized datasets for more efficient processing
  • Take advantage of external data source connections for streaming large datasets

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Common Pitfalls to Avoid

  1. Data Quality Issues
    • Inconsistent formatting across examples
    • Missing or incomplete responses
    • Poor quality or irrelevant examples
  2. Dataset Size Problems
    • Too few examples for effective learning
    • Dataset too large for file upload (use external data sources instead)
  3. Format Errors
    • Incorrect JSON/JSONL formatting
    • Missing required columns
    • Inconsistent data types