Blog | Jinn's Hub

about / blog / sources / projects / ZH /

2026

FlyDSL notes — BasisAttr, the layer beneath Layout

My FlyDSL source reading collapsed the layout algebra into five words. This is the patch — Fly_Basis, BasisAttr, what they are, why layouts need them, and where to start when your mentor hands you the 'complete the BasisAttr surface' task.
From Python to Silicon — A Compiler & Arch Primer for the Working ML Engineer

You can write production ML systems for years without knowing what IR, MLIR, LLVM, ISA, or FFI actually mean. This is the patch — a bilingual primer for the undergrad-CS-but-skipped-compilers crowd, with a full HTML deep dive carrying six hand-drawn SVG plates.
Attention Mechanisms — Full, Sparse, Linear, NSA & GLA

Breaking down Full, Sparse, and Linear Attention, all the way to DeepSeek NSA and Gated Linear Attention

2025

Benchmark: Qwen3-Coder-30B-A3B + EAGLE3 Speculative Decoding

EAGLE3 speculative decoding benchmarks on Qwen3-Coder — 1.87x speedup for code generation
NeMo-RL vs slime: RL Training Framework Comparison

Deep comparison of two RL training frameworks: algorithms, engineering quality, MoE support, and ROCm compatibility
TritonForge: Server-based Multi-turn RL for Triton Kernel Generation

End-to-end server-based RL training and evaluation system for Triton kernel generation across NVIDIA and AMD, built on slime + Megatron
SFT & RL Training Guide

Complete guide from SFT vs RL fundamentals, loss computation, dataset construction to RLHF in practice
KV Cache & Model Weights

Understanding KV Cache vs Model Weights — the first step to LLM inference optimization
Memory Calculation of LLM on GPU

Detailed GPU memory estimation for LLM training and inference, covering DP/TP/PP/EP parallelism strategies
Transformer Deep Dive (Math + Code)

Deconstructing Transformer's Self-Attention, LayerNorm, and MLP from math, code, and architecture perspectives

© 2026 • Jinn's Hub 🔬

Press Esc or click anywhere to close