A hands-on build guide for a blockwise INT4 inference engine with packed-nibble weights, per-group scales/zero-points, and fused dequantizing GEMM — a portfolio project that demonstrates real systems and ML engineering depth.
A hands-on guide to implementing temperature, top-k, and top-p sampling in pure Python, with runnable code that showcases systems engineering skills.
This tutorial walks you through implementing a LoRA weight merger in PyTorch, giving you a concrete portfolio piece that highlights systems thinking and ML engineering prowess.
A hands-on guide to building a pure-Python transformer model with a custom decoding loop implementing temperature, top-k, and top-p sampling — a CV-worthy project that demonstrates deep ML systems knowledge.
Learn how to build a high-performance speculative decoding engine from scratch, mastering KV-cache management and token-tree verification to optimize LLM inference.