Deep dives into parameter-efficient fine-tuning, hardware memory calibration, quantization mathematics, and local retrieval-augmented generation.
An architectural and economic comparison: why data sovereignty, predictable compute costs, and zero-telemetry hardware make on-device fine-tuning superior to hosted cloud APIs.
A comprehensive engineering deep dive into 4-bit NormalFloat quantization, double quantization memory savings, VRAM budgets, and when to pick LoRA vs. QLoRA on consumer hardware.
Parametric memory vs. non-parametric retrieval: understand the clear boundary between training for behavioral syntax and retrieving for dynamic knowledge, and how to combine both.