Hyperparameters
Configuration settings that control the training process, such as learning rate, batch size, and number of epochs. Set before training begins.
In-Depth Explanation
Hyperparameters are configuration values that control how a machine learning model learns, as opposed to parameters which are learned during training. They must be set before training begins and significantly impact model performance.
Common hyperparameters:
- Learning rate: How much to adjust weights per update
- Batch size: Samples processed before updating weights
- Epochs: Complete passes through training data
- Hidden layers/units: Network architecture
- Dropout rate: Regularization strength
- Optimizer: Algorithm for updating weights (Adam, SGD)
For LLM fine-tuning:
- Learning rate (often 1e-5 to 5e-5)
- Batch size (often small, 4-32)
- Epochs (often 1-5 for fine-tuning)
- LoRA rank (for efficient fine-tuning)
- Warmup steps
Hyperparameter tuning approaches:
- Grid search: Try all combinations
- Random search: Sample randomly
- Bayesian optimization: Smart exploration
- Manual tuning: Based on experience
Business Context
Hyperparameter tuning can significantly impact model performance and training costs. For US companies running AI workloads on AWS, Azure, or Google Cloud, proper tuning directly affects your monthly cloud bill.
How Clever Ops Uses This
We handle hyperparameter optimization for American businesses doing custom model training, finding configurations that balance performance and compute cost on US-region cloud infrastructure.
Example Use Case
"Adjusting learning rate to balance training speed and model quality - too high causes instability, too low wastes time and resources."