Results for "fine-tuning"

Techniques that fine-tune small additional components rather than all weights to reduce compute and storage.

Updating a pretrained model’s weights on task-specific data to improve performance or adapt style/behavior.

Fine-tuning on (prompt, response) pairs to align a model with instruction-following behaviors.

PEFT method injecting trainable low-rank matrices into layers, enabling efficient fine-tuning.

Reusing knowledge from a source task/domain to improve learning on a target task/domain, typically via pretrained models.

Ensuring model behavior matches human goals, norms, and constraints, including reducing harmful or deceptive outputs.

A high-capacity language model trained on massive corpora, exhibiting broad generalization and emergent behaviors.

Task instruction without examples.

Removing weights or neurons to shrink models and improve efficiency; can be structured or unstructured.

Practices for operationalizing ML: versioning, CI/CD, monitoring, retraining, and reliable production management.

Automated testing and deployment processes for models and data workflows, extending DevOps to ML artifacts.

End-to-end process for model training.

Methods that learn training procedures or initializations so models can adapt quickly to new tasks with little data.

Models whose weights are publicly available.

Explicit output constraints (format, tone).

Loss of old knowledge when learning new tasks.

Train/test environment mismatch.