Machine-Learning

Diagram of a mini GPT training pipeline showing token embeddings, attention, MLP blocks, and an optimizer loop.

Build a Mini GPT From Scratch in Pure NumPy: A Hiring-Manager-Worthy Project

Build a tiny GPT end-to-end in NumPy to demonstrate ownership of the ML stack: mixed precision, AdamW, cosine LR, and gradient accumulation. Code-first, with a roadmap to production-grade upgrades.

Abstract visualization of a tensor grid being mapped from float32 values into discrete quantization bins.

Why Quantization Works: The Mathematics That Makes LLMs 4x Smaller

Quantization shrinks 70B-parameter models from 140GB to 35GB without retraining. Here is the math, the failure modes, and why it works in production.

Diagram of an LLM serving as the final ranking stage in a recommender pipeline.

GenRec: An LLM-Backed Recommendation Ranker

GenRec replaces a stack of hand-engineered ranking models with a single fine-tuned LLM that scores items from natural-language context. Here is the architecture, the training loop, and what production teams should know before shipping it.

Terminal screenshot of a fine-tuning training run showing loss curves.

Fine-Tuning a Small LLM: A Hands-On Experiment

A working engineer’s journal of fine-tuning a small open-source LLM with LoRA on a custom dataset — the full pipeline, the surprises, and the measurable results.

Diagram of a speculative decoder: a small draft model proposing tokens that a larger target model verifies in parallel.

Build a Speculative Decoder From Scratch: A Portfolio Project That Signals Real Systems Skill

A working engineer’s guide to implementing a speculative decoder from scratch: draft with n-gram proposals, verify with a target model, ship it as a portfolio piece. Real code, real benchmarks, real extensions.