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Overview

Effective hyperparameter tuning is crucial for achieving optimal performance when fine-tuning language models. This guide covers key hyperparameters, their impact on training, and best practices for tuning them.

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Key Hyperparameters

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Learning Rate

The learning rate controls how much the model updates its weights in response to training data.
  • Too high: Training may become unstable or diverge
  • Too low: Training may be slow or get stuck in suboptimal solutions
  • Best practice: Start with 2e-4 for LoRA and gradually decrease if training is unstable

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Batch Size

The number of training examples processed in each training step.
  • Larger batches: More stable gradients but require more memory
  • Smaller batches: Less memory but potentially noisier training
  • Best practice: Use the largest batch size that fits in your available memory. For LoRA, use an effective_batch_size of 16 or 32.

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Number of Epochs

How many times the model sees the entire training dataset over the course of training.
  • Too few: Model may underfit and not learn the task well
  • Too many: Model may overfit or waste computational resources
  • Best practice: Start with 3 epochs and adjust based on validation performance

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LoRA Rank

For LoRA fine-tuning, the rank determines the complexity of adaptations.
  • Higher rank: More expressive but requires more memory
  • Lower rank: More efficient but may limit adaptation capability
  • Best practice: Start with rank 16 for most tasks, increase for complex tasks

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Systematic Tuning Process

  1. Start with Defaults
    • Begin with our recommended default values
    • These work well for most common use cases
    • See the Configuration Reference for defaults
  2. Monitor Key Metrics
    • Training loss
    • Validation loss
    • Task-specific metrics (accuracy, F1, etc.) through offline evaluation
  3. Adjust One at a Time
    • Change only one hyperparameter at a time
    • Document the impact of each change
    • Keep track of what works and what doesn’t
  4. Consider Resource Constraints
    • Balance performance gains against computational costs
    • Monitor memory usage and training time

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Common Pitfalls

  • Overfitting: Watch for validation metrics diverging from training metrics
  • Unstable Training: If loss spikes or becomes NaN, reduce learning rate
  • Premature Optimization: Don’t tune hyperparameters before having a good baseline

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Advanced Techniques

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Learning Rate Scheduling

  • Use learning rate warmup to stabilize early training
  • Consider learning rate decay to fine-tune final performance
  • Example configuration:
config = SFTConfig(
  lr_scheduler_type="cosine",
  warmup_ratio=0.1,
)

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Gradient Clipping

  • Helps prevent exploding gradients
  • Particularly useful with larger learning rates
  • Example configuration:
Support coming soon.

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Next Steps

  • Review the SFT Configuration Reference for detailed parameter descriptions. You can also find the parameters for continued pretraining, GRPO, and augmentation in their respective configuration files.
  • Review the Hyperparameter Tuning Guide for more detailed information on how to tune hyperparameters.
  • Experiment with different configurations on a small subset of your data