LoRA Infrastructure — Training Pipeline & Adapter Management

I will build a LoRA training infrastructure — covering an automated training pipeline, adapter version management, dynamic adapter loading for multi-task inference (loading the appropriate adapter per request without full model reload), and a management interface for your team to track and compare adapter performance. LoRA's adapter architecture enables a deployment pattern unavailable with full fine-tuning — a single base model loaded once in memory with multiple task-specific adapters swapped dynamically per request, enabling multi-task serving at the memory cost of a single model plus lightweight adapters, which is the key infrastructure pattern that makes LoRA economically superior to full fine-tuning for multi-task deployments.

The infrastructure covers training pipeline automation, adapter registry with version tracking, dynamic adapter loading implementation (PEFT's load_adapter for hot-swapping), multi-adapter serving performance benchmarking, and a management dashboard.