Results for "accessible weights"
Closed Model
Intermediate
Models accessible only via service APIs.
Weight Initialization
Intermediate
Methods to set starting weights to preserve signal/gradient scales across layers.
Fine-Tuning
Intermediate
Updating a pretrained model’s weights on task-specific data to improve performance or adapt style/behavior.
Parameter-Efficient Fine-Tuning
Intermediate
Techniques that fine-tune small additional components rather than all weights to reduce compute and storage.
Quantization
Intermediate
Reducing numeric precision of weights/activations to speed inference and reduce memory with acceptable accuracy loss.
Pruning
Intermediate
Removing weights or neurons to shrink models and improve efficiency; can be structured or unstructured.
Open-Weight Model
Intermediate
Models whose weights are publicly available.