Latent-Space Fine-Tuning
Definition of LoRA, adapters, AWS SageMaker, and Bedrock as latent-space adaptation workflows.
Latent-Space Fine-Tuning #
Latent-space fine-tuning describes how adaptation methods such as LoRA, prefix tuning, and adapters change a pretrained model by learning small parameter sets that shift, rotate, or redirect internal representations.
Most of the base model stays frozen while the added parameters steer latent vectors toward a new domain, vocabulary, tone, or task. Cloud tools such as AWS SageMaker and Amazon Bedrock can support this workflow by training adapters and exposing embeddings for inspection.
Example: A legal-domain LoRA can teach an off-the-shelf LLM to arrange legal jargon more usefully in its latent space without fully retraining the model.
Dictionary: https://dictionary.platphormnews.com/en/define/latent-space-fine-tuning
Related Documentation
Latent Space Surgery
Definition of targeted model editing through concept directions in latent space.
Latent Operations
Definition of vector shifts, interpolation, slicing, masking, and sampling in latent space.
Latent Reasoning
Definition of latent reasoning in LLM hidden states and continuous representations.
Embedding Space
Definition of embedding space as a vector representation for semantic similarity and retrieval.
Latent Space
Definition of latent space in machine learning, LLMs, and embeddings.