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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

49 recent articles
AI2ML AI to Machine Learning
AI2ML AI to Machine Learning
Oct 15, 2025 · Artificial Intelligence

NanoChat Source Code Deep Dive: Karpathy’s Full‑Stack LLM Pipeline Explained

This article dissects NanoChat’s end‑to‑end LLM pipeline—from a lightweight 561M‑parameter transformer and custom Rust BPE tokenizer to Chinchilla‑scaled training, multi‑task fine‑tuning, optional RL on GSM8K, KV‑cache inference optimizations, and benchmark results that slightly surpass GPT‑2 Large.

CORE benchmarkChinchilla scalingFastAPI
0 likes · 10 min read
NanoChat Source Code Deep Dive: Karpathy’s Full‑Stack LLM Pipeline Explained
AI2ML AI to Machine Learning
AI2ML AI to Machine Learning
Oct 13, 2025 · Artificial Intelligence

How Large‑and‑Small Language Model Collaboration Is Shaping the Future

The article argues that combining large, high‑capacity models with lightweight, fine‑tuned small models can cut costs, lower latency, enable specialized vertical tasks, and shift development from chasing ever‑bigger models toward optimal system architectures, outlining key techniques such as state‑space models, knowledge distillation, and staged fine‑tuning.

AI architectureEfficiencyKnowledge Distillation
0 likes · 3 min read
How Large‑and‑Small Language Model Collaboration Is Shaping the Future
AI2ML AI to Machine Learning
AI2ML AI to Machine Learning
Oct 1, 2025 · Artificial Intelligence

2025 Large Model Engineering Breakthroughs: Cutting Costs, Boosting Performance, and Extending Context

The 2025 open‑source reports reveal major advances in large‑model engineering, including drastic cost cuts such as DeepSeek‑V3 training for $5.57 M, performance gains where Gemma 3 4B matches Gemma 2 27B, memory efficiencies like 85 % KV‑cache reduction, and a suite of new techniques—from loss‑free MoE balancing to multi‑token prediction—that together push context lengths to one million tokens and enable multimodal, aligned, and industry‑specific models.

Memory Efficiencyattention mechanismscost reduction
0 likes · 13 min read
2025 Large Model Engineering Breakthroughs: Cutting Costs, Boosting Performance, and Extending Context
AI2ML AI to Machine Learning
AI2ML AI to Machine Learning
Sep 30, 2025 · Artificial Intelligence

Dynamic Multimodal Video Generation: Prioritizing Stability and High Quality

The article surveys the evolution of video generation models—from early GANs and DCGAN to diffusion‑based approaches like Stable Diffusion and DiT—highlighting how stability, high quality, massive compute, and multimodal data pipelines are shaping the current and future paths of dynamic multimodal video generation.

Stable DiffusionTransformerdiffusion models
0 likes · 7 min read
Dynamic Multimodal Video Generation: Prioritizing Stability and High Quality
AI2ML AI to Machine Learning
AI2ML AI to Machine Learning
Sep 28, 2025 · Artificial Intelligence

Core Metrics for Enterprise Large‑Model Engineering

The article outlines the five essential engineering domains—application, model, compute, knowledge, and data—in the era of large models, and details concrete scale, efficiency, service, value, quality, and security metrics that enterprises should track to drive intelligent outcomes.

AI engineeringbusiness valuecompute metrics
0 likes · 7 min read
Core Metrics for Enterprise Large‑Model Engineering
AI2ML AI to Machine Learning
AI2ML AI to Machine Learning
Sep 24, 2025 · Artificial Intelligence

Key Points for Evaluating AI Agents

The article explains how Coze's Compass introduces a flexible evaluation system for AI agents, outlines a four‑dimensional submodule assessment (planning, tool use, self‑reflection, memory), and details specific testing criteria and challenges for web, scientific, dialogue, and programming agents.

AI agentsCozeEvaluation Framework
0 likes · 6 min read
Key Points for Evaluating AI Agents
AI2ML AI to Machine Learning
AI2ML AI to Machine Learning
Sep 22, 2025 · Big Data

Why AI‑Native Big Data Platforms Are About to Explode

The article examines how large‑model limitations in accuracy, explainability, and stability have stalled decision‑support use, prompting industry leaders to champion AI‑Ready data infrastructures, Data 4.0 concepts, and AI‑generated service code as the next wave of AI‑native big data platforms.

AIAI NativeData 4.0
0 likes · 6 min read
Why AI‑Native Big Data Platforms Are About to Explode
AI2ML AI to Machine Learning
AI2ML AI to Machine Learning
Sep 11, 2025 · Industry Insights

Key Takeaways from Asset Management Leaders on Large‑Model AI at the Bund Conference

The article compiles senior asset‑management executives' perspectives on applying large‑model AI—covering vertical versus generic models, integration strategies, talent and cost considerations, innovative C2C development, AI‑native platforms, and the practical challenges of using LLMs in investment research.

AI applicationsAsset ManagementC2C development
0 likes · 5 min read
Key Takeaways from Asset Management Leaders on Large‑Model AI at the Bund Conference
AI2ML AI to Machine Learning
AI2ML AI to Machine Learning
Sep 2, 2025 · Artificial Intelligence

Why Enterprise Large‑Model Digitalization Is So Hard: Key Challenges and Capabilities

The article analyzes why enterprise‑wide large‑model AI projects face steep hurdles, outlining required human capabilities, historical labor shifts, current hot technologies such as RAG, Agent, CoT and multimodal, their limits, a three‑stage implementation roadmap, typical case pitfalls, and the key success factors for sustainable digital transformation.

AgentCoTDigital transformation
0 likes · 15 min read
Why Enterprise Large‑Model Digitalization Is So Hard: Key Challenges and Capabilities