This article explores the critical hyperparameters that determine generative AI model performance, covering essential settings across training and architecture design—along with practical instructions on where to configure each parameter in your workflow.
The next article will capture the necessary hyperparameters during inference, fine-tuning, and diffusion models and how to configure those.
During Training which Hyperparameters
- Learning rate: Controls step size during optimization in training script arguments, configuration file, or optimizer setup in PyTorch/TensorFlow (learning_rate=1e-5 or in a config.json file)
- Batch size: Affects gradient quality, memory usage, and training speed in training script arguments, data loader configuration (batch_size=32 or DataLoader(batch_size=32))
- Number of training steps/epochs: Determines exposure to training data in training script arguments or early stopping callback ( num_epochs=3)
- Optimizer choice: Adam, AdamW or Lion in model training setup code or configuration file (optimizer=torch.optim.AdamW( model.parameters(), lr=lr))
- Weight decay: Controls regularization strength in optimizer initialization parameters (AdamW(params, weight_decay=0.01))
- Gradient clipping threshold: Prevents exploding gradients in training loop or in framework-specific utilities ( torch.nn.utils.clip_grad_norm_( model.parameters(),max_norm=1.0))
- Warmup steps: Gradual learning rate increase in learning rate scheduler configuration (warmup_steps=500 or in a scheduler object)
Architecture and Design Hyperparameters
- Model size: Number of parameters, layers, dimensions in model initialization parameters or configuration file (model_config.json or num_layers=24 hidden_size=1024)
- Context length: Maximum sequence length in model configuration and tokenizer settings (config.max_position_embeddings=4096 or max_seq_length=2048)
- Attention heads: Number and size of attention heads in model configuration ( config.num_attention_heads=16)
- Feed-forward network dimension: Size of FFN layers in model configuration (config.intermediate_size=4096)
- Embedding dimension: Size of token embeddings in model configuration ( config.hidden_size=768)
- Dropout rates: Various types of dropout in model configuration ( config.attention_dropout=0.1, config.hidden_dropout=0.1)
This article explores the critical hyperparameters that determine generative AI model performance, covering essential settings across training and architecture design.
Let me know if any parameter is missed out. Let me know your experience about configuring hyperparameters during training and design of Generative AI models.
Related Articles: The next article will capture the necessary hyperparameters during inference, fine-tuning, and diffusion Generative AI models and how to configure those:
