PyTorch

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.

Abstract diagram of a mixture-of-experts router routing tokens to multiple expert networks.

Build a Mixture-of-Experts Router From Scratch in PyTorch

A portfolio-grade project: a pure PyTorch mixture-of-experts router with top-k gating, expert load-balancing loss, and Switch-style capacity factors — with runnable code, tests, and a roadmap to senior-level extensions.

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.

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.

Abstract illustration of GPU scheduling slots filled and freed across inference steps.

Continuous Batching: How vLLM and Friends Keep GPUs Fed

A working engineer’s guide to continuous batching for LLM inference: why static batching wastes GPU cycles, how iteration-level scheduling works, and what tools like vLLM and TGI actually do differently.