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AI2ML AI to Machine Learning
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AI2ML AI to Machine Learning

Original articles on artificial intelligence and machine learning, deep optimization. Less is more, life is simple! Shi Chunqi

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Latest from AI2ML AI to Machine Learning

50 recent articles
AI2ML AI to Machine Learning
AI2ML AI to Machine Learning
Dec 19, 2025 · Artificial Intelligence

The 9 Key Ideas Behind FlashAttention

FlashAttention accelerates transformer inference by combining nine techniques—including loss‑less attention, GPU memory‑pyramid optimization, SRAM‑reusing tiling, safe softmax scaling, online buffering, tile‑size constraints, parallel multiplication, reduced KV slicing, and integrated backward‑pass caching—to achieve efficient, high‑throughput computation on modern GPUs.

Attention MechanismFlashAttentionGPU Optimization
0 likes · 8 min read
The 9 Key Ideas Behind FlashAttention
AI2ML AI to Machine Learning
AI2ML AI to Machine Learning
Dec 16, 2025 · Industry Insights

Why Computer Science Majors Must Embrace a Massive Paradigm Shift

The article argues that traditional storage‑centric computer science curricula are becoming obsolete as AI‑driven, compute‑centric paradigms dominate hardware, data‑center operations, and software ecosystems, urging universities and students to rapidly adopt new teaching focus and skills.

AI hardwareCUDAHeterogeneous Computing
0 likes · 10 min read
Why Computer Science Majors Must Embrace a Massive Paradigm Shift
AI2ML AI to Machine Learning
AI2ML AI to Machine Learning
Dec 3, 2025 · Artificial Intelligence

2026 Forecast: How Large‑Model AI Will Evolve After 2025 Breakthroughs

The article reviews the major 2025 breakthroughs in multimodal, open‑source, and deployment technologies for large models and outlines four 2026 trends—including ToC vs. ToB service split, dual‑hand data generation, MoE routing advances, and AI4Science breakthroughs—that will shape the next wave of AI development.

AI DeploymentAI4ScienceMixture of Experts
0 likes · 6 min read
2026 Forecast: How Large‑Model AI Will Evolve After 2025 Breakthroughs
AI2ML AI to Machine Learning
AI2ML AI to Machine Learning
Nov 4, 2025 · Artificial Intelligence

Common Debugging Signals for Large Language Models

This article outlines the end‑to‑end workflow for large‑model training, highlights typical debugging challenges such as memory OOM, performance bottlenecks, and gradient issues, and provides concrete strategies, tools (DeepSpeed, Megatron, Torchtitan, veScale) and best‑practice checklists to help engineers diagnose and resolve problems efficiently.

DebuggingDeepSpeedLLM
0 likes · 12 min read
Common Debugging Signals for Large Language Models
AI2ML AI to Machine Learning
AI2ML AI to Machine Learning
Nov 3, 2025 · Artificial Intelligence

Smol Training Playbook: Secrets to Building World-Class LLMs

The article details the SmolLM3 3B‑parameter model, its architecture, dual‑mode inference, a three‑stage data‑curation strategy, rigorous ablation methods, preference optimisation (APO/DPO), model merging, and practical training‑stability tricks, offering a comprehensive guide for building high‑performing large language models.

APOLLM trainingModel Merging
0 likes · 13 min read
Smol Training Playbook: Secrets to Building World-Class LLMs
AI2ML AI to Machine Learning
AI2ML AI to Machine Learning
Oct 24, 2025 · Industry Insights

How Generative AI Is Fueling a New Wave of Insurance Fraud

Generative AI tools like DALL·E, Midjourney and deep‑fake platforms are enabling criminals to create highly realistic images, videos and documents, leading to a surge in sophisticated insurance fraud across auto, property, health and life lines, and forcing insurers to overhaul detection and regulatory practices.

AI detectionGenerative AIIndustry Insights
0 likes · 13 min read
How Generative AI Is Fueling a New Wave of Insurance Fraud
AI2ML AI to Machine Learning
AI2ML AI to Machine Learning
Oct 24, 2025 · Artificial Intelligence

Beyond RAG: Three Emerging Knowledge‑Engineering Strategies (ICL, Online Learning, SLM)

The article outlines three post‑RAG knowledge‑engineering approaches—In‑Context Learning with dynamic few‑shot selection, Online Learning encompassing Meta‑Learning and Lifelong Learning to quickly adapt to new tasks, and the Small Language Model path that combines fine‑tuned task‑specific experts with LLM‑SLM collaboration for efficient, privacy‑preserving inference.

LLMLifelong LearningRAG
0 likes · 4 min read
Beyond RAG: Three Emerging Knowledge‑Engineering Strategies (ICL, Online Learning, SLM)
AI2ML AI to Machine Learning
AI2ML AI to Machine Learning
Oct 23, 2025 · Artificial Intelligence

Why Visually‑Rich Document Understanding Looks Like High‑End Docs: A Static Multimodal Overview

The article surveys the evolution of Visually‑Rich Document Understanding (VRDU), highlighting pioneering Chinese OCR research, the LayoutLM family, recent multimodal model breakthroughs, open‑source toolkits, and practical recommendations for handling diverse document types and tasks.

DeepSeek-OCRLayoutLMMultimodal OCR
0 likes · 11 min read
Why Visually‑Rich Document Understanding Looks Like High‑End Docs: A Static Multimodal Overview
AI2ML AI to Machine Learning
AI2ML AI to Machine Learning
Oct 20, 2025 · Artificial Intelligence

nanochat Source Code Deep Dive: Data Prep, Model Design, Training & Evaluation

This article revisits nanochat's core components, detailing the preparation of diverse training datasets, the scaling calculations for tokens and parameters, the model's MQA and KV‑cache design, the full training pipeline with gradient accumulation and mixed‑precision, cost breakdown, inference optimizations, evaluation tasks, and identified limitations with suggested improvements.

EvaluationKV cacheLLM
0 likes · 9 min read
nanochat Source Code Deep Dive: Data Prep, Model Design, Training & Evaluation