Advanced Fine-Tuning in Rust

Overview

Master the complete fine-tuning pipeline—from transformer internals to production deployment—using memory-efficient techniques that run on consumer hardware. This course transforms you from someone who uses large language models into someone who customizes them. You'll learn to fine-tune 7-billion parameter models on a laptop GPU using QLoRA, which reduces memory requirements from 56GB to just 4GB through intelligent quantization and low-rank adaptation. What sets this course apart is its rigorous, scientific approach. You'll apply Popperian falsification methodology throughout: instead of asking "does my model work?", you'll systematically try to break it. This skeptical mindset—testing tokenization edge cases, running rank ablation studies, and validating corpus quality through six falsification categories—builds the critical thinking skills that separate production-ready engineers from those who ship fragile systems. By course end, you'll confidently: calculate VRAM requirements and select appropriate hardware; trace inference through the six-step transformer pipeline; configure LoRA rank to match task complexity; build quality training corpora using AST extraction; and publish datasets to HuggingFace with proper splits and documentation. Built entirely on a sovereign AI stack, everything runs locally with no external dependencies—true ML independence.

Syllabus

ML Foundations & Compute

This module establishes the foundational knowledge required for understanding fine-tuning at a deep level. Learners will explore core ML concepts including parameters, VRAM constraints, and gradients, then progress to understanding data shapes and their mapping to hardware. The module concludes with transformer architecture fundamentals and the inference pipeline, preparing learners for the technical depth required in subsequent weeks.

Transformer Internals & LoRA Introduction

This module dives deep into the internal mechanisms of transformers, covering tokenization implementation, the attention mechanism with QKV projections, and feed-forward networks where two-thirds of model parameters reside. The module bridges into fine-tuning by introducing LoRA fundamentals, showing how to train only 0.1% of parameters while achieving full fine-tuning results.

QLoRA & Corpus Engineering

This module covers the complete production fine-tuning workflow from quantization techniques through corpus creation and publication. Learners will understand how QLoRA combines 4-bit quantization with LoRA adapters for 7× memory reduction, then build quality training datasets using AST parsing, falsification testing, and proper train/validation/test splits. The module concludes with Hugging Face publishing workflows.