ruvector-learning-wasm

You can fine-tune models in the browser without a GPU by using WebAssembly to perform low-rank adaptation. This Skill handles MicroLoRA weight adaptation directly on the edge, enabling on-device training without cloud hardware.

Real-time LoRA adaptation in WebAssembly operates with sub-100 microsecond latency for rank-2 LoRA. This ultra-fast processing allows model weights to update instantly during user interactions.

Yes, you can adapt agent behavior dynamically by modifying model weights in real-time. As users provide feedback, the underlying model adjusts its responses instantly within the browser environment.

To initialize a MicroLoRA adapter, specify the input dimension, such as 768, then adapt the weights using provided input and target activation vectors. This enables parameter-efficient training at the edge.

WebAssembly supports parameter-efficient training for edge computing by running lightweight LoRA adaptation. It bypasses the need for GPU hardware, allowing fine-tuning directly in browser or edge environments.

Low-rank adaptation is used at the edge because it achieves ultra-fast weight updates with minimal computational overhead. Full fine-tuning requires heavy GPU resources, while MicroLoRA runs efficiently in WebAssembly.