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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
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 OptimizationLocal DeploymentOllama
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.

InferenceModelScopePython
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 agentsPyTorchRAG
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.

LangGraphMulti-agentNetwork Architecture
0 likes · 13 min read
Designing Decentralized Multi‑Agent Networks with LangGraph: The Swarm Architecture
Fun with Large Models
Fun with Large Models
Dec 26, 2025 · Artificial Intelligence

LangGraph Agent Design Patterns Part 1: Prompt‑Chain, Router, and Parallel Modes

This article introduces three core LangGraph workflow patterns—Prompt‑Chain, Router, and Parallel—explaining their concepts, advantages, and concrete Python code examples that demonstrate how to decompose tasks, route requests, and run sub‑tasks concurrently for more reliable and efficient AI agents.

AI agentsLangGraphParallel mode
0 likes · 19 min read
LangGraph Agent Design Patterns Part 1: Prompt‑Chain, Router, and Parallel Modes
Fun with Large Models
Fun with Large Models
Dec 24, 2025 · Artificial Intelligence

Building an Automatic Email‑Processing Agent with LangGraph 1.0 – A Hands‑On Guide

This tutorial walks through the complete development of an automatic email‑processing agent using LangGraph 1.0, covering scenario analysis, state design, node implementation, graph assembly, and testing with both high‑priority bug reports and routine greeting emails, while demonstrating state management, conditional routing, and human‑in‑the‑loop controls.

LangChainLangGraphState Management
0 likes · 14 min read
Building an Automatic Email‑Processing Agent with LangGraph 1.0 – A Hands‑On Guide
Fun with Large Models
Fun with Large Models
Dec 21, 2025 · Artificial Intelligence

LangGraph 1.0 Quick Guide Part 2: Conditional Edges, Memory, and Human‑in‑the‑Loop

This article walks through three advanced LangGraph 1.0 features—using the Command object for conditional routing, checkpoint‑based memory for state persistence across invocations, and interrupt‑driven human‑in‑the‑loop control—providing concrete code examples, execution traces, and a comparison of design trade‑offs.

AI agentsCheckpointCommand
0 likes · 15 min read
LangGraph 1.0 Quick Guide Part 2: Conditional Edges, Memory, and Human‑in‑the‑Loop
Fun with Large Models
Fun with Large Models
Dec 17, 2025 · Artificial Intelligence

Quick Guide to LangGraph 1.0: Core Concepts, Nodes, and Edges

This article introduces LangGraph 1.0 as a programming‑language‑style framework for AI agents, explains its core abstractions—State, Node, Edge, Reducer, and Human‑in‑the‑Loop—shows how to define state and node functions, builds simple and parallel graphs with static, conditional, and MapReduce edges, and demonstrates conflict‑resolution using built‑in and custom reducers.

AI agentsGraph WorkflowLangChain
0 likes · 17 min read
Quick Guide to LangGraph 1.0: Core Concepts, Nodes, and Edges