Ml-Systems

Diagram of a mixed-precision training loop showing forward, autocast, loss scaling, and a custom CUDA bf16 kernel.

Building a Mixed-Precision GPT Trainer From Scratch: A CV-Grade ML Systems Project

A working engineer’s guide to building a from-scratch mixed-precision training loop — gradient scaling, dynamic loss scaling, and a custom bf16 tensor kernel — applied to a mini GPT. Includes architecture, runnable code, and a roadmap for turning the toy into a portfolio-grade ML systems project.

Building a GGUF Weight Loader From Scratch: A CV-Grade ML Systems Project

Build a runnable GGUF parser and dequantizer for Q4_K and Q6_K, map the result into a PyTorch Llama model, and end up with a project that signals real ML systems skill on your CV.

Tiled matrix multiplication diagram with softmax rows

Building Flash Attention from Scratch in NumPy: A CV-Worthy Side Project

A working engineer’s guide to implementing Flash Attention from the ground up using tiling and the online softmax trick — runnable NumPy, end-to-end tests, and a roadmap to senior-level extensions.

Diagram of a mini-GPT training loop with mixed precision, gradient accumulation, and optimizer components.

Building a Mini-GPT From Scratch: A Portfolio Project That Signals Real ML Systems Skill

A working engineer’s guide to building a mini-GPT training loop from scratch in PyTorch — featuring AMP, gradient accumulation, cosine warmup, weight tying, and AdamW — designed as a CV-grade portfolio piece.

Architecting Retrieval-Augmented Generation Systems: A Deep Dive into Production Pipelines

How to design Retrieval-Augmented Generation pipelines that hold up in production — covering indexing, hybrid retrieval, reranking, evaluation harnesses, and the operational realities of freshness, latency, and cost.