ruvector-learning-wasm

What problem does it solve?

This Skill enables ultra-fast, low-rank adaptation (LoRA) of model weights directly in the browser or edge environments, overcoming the need for GPU hardware and enabling real-time model fine-tuning.

Core Features & Use Cases

• On-device Fine-tuning: Adapt models with sub-100 microsecond latency using WebAssembly.
• Real-time Agent Behavior: Modify agent behavior dynamically based on user interactions or environmental changes.
• Lightweight Training: Perform parameter-efficient training without relying on cloud GPUs.
• Use Case: Imagine an AI assistant in a web application that needs to learn user preferences in real-time. This Skill allows the assistant's underlying model to adapt its responses instantly as the user provides feedback, all within the browser.

Quick Start

Use the ruvector-learning-wasm skill to initialize a MicroLoRA adapter with input dimension 768 and adapt weights using provided input and target activation vectors.