library_name: transformers
tags: []
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This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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Learning Rate Optimization for Language Model Fine-tuning
This script implements an advanced learning rate optimization strategy for fine-tuning large language models, combining Bayesian optimization with Gaussian Process Regression (GPR) for precise learning rate selection.
Key Features
1. Bayesian Optimization
- Uses Optuna framework to perform systematic learning rate search
- Implements Tree-structured Parzen Estimators (TPE) for efficient hyperparameter optimization
- Automatically explores learning rates between 1e-6 and 1e-4 in log space
2. Advanced Loss Tracking
- Evaluates model performance using mean loss from the final 20% of training steps
- Handles training failures gracefully with proper memory management
3. Sophisticated Post-processing
- Applies Gaussian Process Regression to model the learning rate-loss relationship
- Calculates uncertainty estimates for each prediction
- Implements Expected Improvement (EI) acquisition function for optimal learning rate selection
4. Memory Optimization
- Implements gradient checkpointing for efficient memory usage
- Includes automatic memory clearing between trials
Technical Details
The optimization process consists of three main phases:
- Initial exploration using Bayesian optimization
- Refinement using Gaussian Process Regression
- Final selection using Expected Improvement criterion
The script was designed this way because:
- Bayesian optimization provides efficient exploration of the learning rate space
- GPR adds uncertainty quantification and smooth interpolation between observed points
- The combination allows for both exploration and exploitation of the learning rate space
Advantages
- More reliable than manual learning rate selection
- Provides uncertainty estimates for each prediction
- Automatically adapts to different model sizes and datasets
- Generates visualizations for analysis
- Saves comprehensive results for reproducibility
This approach is particularly valuable for fine-tuning large language models where training costs are high and optimal learning rate selection is crucial for model performance.
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Training Details
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Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
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