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Fine-tuningFreeOpen Source
HYPEROPT
Distributed Bayesian hyperparameter optimization for ML training
BSD-3-Clause
ABOUT
Fine-tuning LLMs and deep learning models requires extensive hyperparameter tuning — learning rates, LoRA rank, batch sizes, optimizer settings — but brute-force grid search or random search wastes expensive GPU hours and rarely finds optimal configurations. Hyperopt provides intelligent Bayesian optimization algorithms (Tree-of-Parzen-Estimators, adaptive TPE) that converge to good hyperparameters in far fewer trials. Its distributed execution engine scales across clusters and supports early stopping to terminate unpromising trials automatically.
INSTALL
pip install hyperoptINTEGRATION GUIDE
1. Tune LoRA learning rate, rank, and alpha parameters for LLM fine-tuning in fewer GPU hours
2. Optimize training hyperparameters for distributed fine-tuning runs across multiple GPUs
3. Automate hyperparameter search for model quantization and compression configurations
4. Run parallel hyperparameter sweeps across a cluster with MongoDB-based trial coordination
5. Combine with early stopping algorithms to terminate underperforming trials and save compute
TAGS
hyperparameter-optimizationpythonbayesian-optimizationmachine-learningtrainingdistributed