Numpy

A visualization of a Mixture-of-Experts architecture with sparse routing paths

Build a Sparse MoE Inference Engine from Scratch in NumPy

A hands-on guide to building a sparse MoE inference engine in pure NumPy, covering top-k gating, capacity-constrained token dispatch, and differentiable load balancing — a portfolio project that signals deep systems and ML engineering skill.

NumPy code on a laptop screen illustrating attention matrices

Hands‑On Build Guide: Page‑Transformer Inference with Greedy KV‑Cache Eviction and Paged Attention in Numpy

A step‑by‑step guide to implementing a lightweight page‑transformer with paged attention and KV‑cache eviction, ready to run and extend.

A visualization of a sparse Mixture-of-Experts routing architecture with multiple expert paths and a gating network.

Build a Sparse MoE Router from Scratch in NumPy

A hands-on guide to implementing a sparse MoE router from scratch in NumPy, covering noisy top-k gating, capacity-factor batching, and auxiliary load-balancing loss — a CV-worthy side project for ML systems engineers.

A neural network weight matrix visualized as a heatmap.

From Scratch: GPTQ‑Style Post‑Training Quantizer in NumPy

This guide walks you through implementing a GPTQ-style post-training quantizer entirely in NumPy. The resulting script compresses transformer weights and measures accuracy impact, showcasing your ability to ship low-level ML infrastructure.

A conceptual illustration of a token sampling distribution

Building a Production-Grade Top‑k/Top‑p Sampler with NumPy

This tutorial shows how to build a high‑performance top‑k/top‑p token sampler with temperature scaling and per‑token log‑probability tracking using NumPy. It demonstrates real systems skills relevant to ML inference roles.