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.
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