# Personal AI Infrastructure -- LoRA Module

**Version**: v1.0.0 | **Status**: Active | **Last Updated**: March 2026

## Overview

LoRA provides parameter-efficient fine-tuning simulation for PAI agents. It demonstrates the LoRA technique (Hu et al. 2021) using pure Python + NumPy, enabling agents to understand and work with low-rank adaptation without requiring PyTorch.

## PAI Algorithm Phase Mapping

| Phase | Activity | Key Functions/Tools |
|-------|----------|-------------------|
| **OBSERVE** | Analyze weight matrix dimensions and rank requirements | `LoRAConfig`, `lora_apply` |
| **THINK** | Evaluate parameter efficiency trade-offs (rank vs quality) | `LoRAConfig.scaling`, parameter reduction calculations |
| **PLAN** | Configure LoRA hyperparameters (rank, alpha) for target modules | `LoRAConfig(rank=r, alpha=a)` |
| **BUILD** | Apply LoRA adaptation to weight matrices | `apply_lora(W, rank, alpha)`, `LoRALayer` |
| **EXECUTE** | Run forward pass through adapted layers | `layer.forward(x)`, `layer(x)` |
| **VERIFY** | Validate effective rank, check merged output matches unmerged | `layer.effective_rank`, `merge_lora`, numerical comparison |
| **LEARN** | Record parameter efficiency metrics and rank analysis | `lora_apply` MCP tool results |

## MCP Tools Available

| Tool | Description | Trust Level |
|------|-------------|-------------|
| `lora_apply` | Apply LoRA and report parameter efficiency metrics | SAFE |

## Agent Capabilities

| Agent Type | Primary Use | Key Functions |
|-----------|-------------|--------------|
| **Engineer** | Apply LoRA to model weights, merge for deployment | `apply_lora`, `merge_lora`, `LoRALayer` |
| **Architect** | Evaluate rank/alpha trade-offs, parameter budgeting | `LoRAConfig`, `lora_apply` |
| **QATester** | Verify merge correctness, rank preservation | `effective_rank`, forward comparison |

## Navigation

- [README](README.md) | [AGENTS](AGENTS.md) | [SPEC](SPEC.md)
