Hands‑On Build Guide: LoRA‑Augmented Transformer Attention Block in Pure Python
A step‑by‑step guide to implementing a LoRA‑modulated attention block in pure Python, complete with runnable code, testing, and a roadmap to production‑grade extensions.
Transformers
Pure-Python INT8 Weight Quantizer for Transformer Inference: A Hands-On Build Guide
Learn to implement a production‑ready INT8 quantizer with per‑channel scaling, test it on transformer weights, and extend it for real‑world deployment.
Build a LoRA Adapter Trainer from Scratch in Pure Python
Learn to implement LoRA fine-tuning from scratch, creating a portfolio project that demonstrates real ML infrastructure expertise.
From Scratch Decoding: Building a Pure-Python Sampler for Transformer Logits
A hands-on guide to building a pure-Python sampler for transformer logits, covering temperature scaling, top-k, top-p, and typical-p decoding from scratch.
From Scratch: Building a LoRA Fine-Tuning Adapter in Pure PyTorch
Implement a Low-Rank Adaptation (LoRA) module from scratch in PyTorch, then extend it with persistence, observability, and distributed training for production readiness.