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Fun with Large Models
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Fun with Large Models

Master's graduate from Beijing Institute of Technology, published four top‑journal papers, previously worked as a developer at ByteDance and Alibaba. Currently researching large models at a major state‑owned enterprise. Committed to sharing concise, practical AI large‑model development experience, believing that AI large models will become as essential as PCs in the future. Let's start experimenting now!

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Recent Articles

Latest from Fun with Large Models

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Fun with Large Models
Fun with Large Models
Feb 10, 2026 · Artificial Intelligence

Building LangChain Agent Skills from Scratch to Cut Token Usage and Boost Tool Accuracy

The article presents a step‑by‑step design and implementation of a Claude‑style Skills mechanism for LangChain agents, using a double‑layer tool architecture, state‑driven dynamic filtering, and middleware interception to load only relevant tools, dramatically reducing token consumption and improving decision quality and response speed.

Agent SkillsDynamic LoadingLangChain
0 likes · 15 min read
Building LangChain Agent Skills from Scratch to Cut Token Usage and Boost Tool Accuracy
Fun with Large Models
Fun with Large Models
Feb 8, 2026 · Artificial Intelligence

How the US‑China LLM ‘War’ Plays Out: Deep Dive into Claude Opus 4.6 vs GPT‑5.3 CodeX

The article provides a detailed technical comparison of Anthropic's Claude Opus 4.6 and OpenAI's GPT‑5.3 CodeX, covering performance gains, context window size, agent teamwork, programming benchmarks, new features such as adaptive thinking and interactive development, and offers guidance on choosing the right model for specific workflows.

AI Model ComparisonClaude Opus 4.6Context Window
0 likes · 15 min read
How the US‑China LLM ‘War’ Plays Out: Deep Dive into Claude Opus 4.6 vs GPT‑5.3 CodeX
Fun with Large Models
Fun with Large Models
Feb 3, 2026 · Artificial Intelligence

OpenClaw Core Features and Architecture Explained (with LLM & Chat Integration)

OpenClaw, a 2026 open‑source AI agent, combines a protocol‑agnostic gateway, a four‑layer memory system, and an extensible Agent Skills framework to enable real‑time cross‑platform interaction, long‑term learning, and seamless integration with Chinese large language models and popular chat applications.

AI AgentAgent SkillsChat Tool Integration
0 likes · 11 min read
OpenClaw Core Features and Architecture Explained (with LLM & Chat Integration)
Fun with Large Models
Fun with Large Models
Jan 25, 2026 · Artificial Intelligence

Complete Guide to Agent Skills: Core Concepts, Design Patterns, and Hands‑On Code

This article explains the three‑layer Agent Skills architecture, demonstrates step‑by‑step creation and configuration of a Skill using Claude Code—including metadata, instruction, and resource layers, advanced scripting integration, and a detailed comparison with MCP, highlighting token savings and use‑case differences.

AI AgentAgent SkillsClaude Code
0 likes · 18 min read
Complete Guide to Agent Skills: Core Concepts, Design Patterns, and Hands‑On Code
Fun with Large Models
Fun with Large Models
Jan 18, 2026 · Artificial Intelligence

Step‑by‑Step Guide to Deploying Large Language Models Locally with VLLM and Ollama

This article walks through two mainstream local deployment solutions—high‑performance VLLM for production Linux servers and lightweight Ollama for personal Windows machines—covering environment setup, model download, server launch, API testing, key configuration parameters, and the quantization technique that makes Ollama models compact.

GPU optimizationModel QuantizationOllama
0 likes · 18 min read
Step‑by‑Step Guide to Deploying Large Language Models Locally with VLLM and Ollama
Fun with Large Models
Fun with Large Models
Jan 14, 2026 · Artificial Intelligence

Understanding Large Language Model Files: Structure, Tokens, and Inference with Qwen3

This article walks through the complete workflow of loading and running the open‑source Qwen3‑8B model, explaining each core file (weights, config, generation config, tokenizer), how the model tokenizes input, applies chat templates, generates responses, and decodes output, all illustrated with code and diagrams.

ModelScopePythonQwen3
0 likes · 16 min read
Understanding Large Language Model Files: Structure, Tokens, and Inference with Qwen3
Fun with Large Models
Fun with Large Models
Jan 12, 2026 · Artificial Intelligence

Why You Should Master Large‑Model Training: A Full‑Process Practical Guide

The article explains why mastering large‑model training is crucial for professionals, researchers, and enterprises, outlines the end‑to‑end pipeline—from data preparation and pre‑training to instruction fine‑tuning and RLHF alignment—compares training with RAG, and presents a structured learning roadmap.

AI AgentsData EngineeringPyTorch
0 likes · 14 min read
Why You Should Master Large‑Model Training: A Full‑Process Practical Guide
Fun with Large Models
Fun with Large Models
Jan 10, 2026 · Artificial Intelligence

Designing Decentralized Multi‑Agent Networks with LangGraph: The Swarm Architecture

This article explains LangGraph's network (decentralized) architecture for multi‑agent systems, compares it with supervisor and hierarchical designs, and provides a step‑by‑step Python example using the langgraph‑swarm library to build agents that can dynamically hand off control and preserve conversation continuity.

LangGraphPythonmulti-agent
0 likes · 13 min read
Designing Decentralized Multi‑Agent Networks with LangGraph: The Swarm Architecture