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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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Latest from Fun with Large Models

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Fun with Large Models

Apr 22, 2026 · Artificial Intelligence

How to Quickly Integrate Agent Skills in LangChain DeepAgents

This article provides a step‑by‑step guide to using Agent Skills in LangChain DeepAgents, covering the Skills directory structure, the four engineering steps (discovery, system‑prompt injection, progressive loading, execution), and two practical examples—a simple skill lookup and a complex docx‑processing skill—complete with code snippets and troubleshooting tips.

Agent SkillDeepAgentsFileSystemMiddleware

0 likes · 15 min read

How to Quickly Integrate Agent Skills in LangChain DeepAgents

Fun with Large Models

Apr 17, 2026 · Artificial Intelligence

Mastering Large Model Training: Practical Parameter Tuning from Beginner to Pro

This guide walks you through interpreting training logs and loss curves, diagnosing common issues such as oscillation, under‑fitting, and over‑fitting, and applying concrete adjustments to learning rate, LoRA settings, batch size, and epochs, with scenario‑specific strategies to turn a novice into a tuning expert.

AI trainingLoRAParameter Tuning

0 likes · 23 min read

Mastering Large Model Training: Practical Parameter Tuning from Beginner to Pro

Fun with Large Models

Apr 9, 2026 · Artificial Intelligence

Harness Engineering: The Critical Factor That Determines AI Agent Performance

The article explains Harness Engineering, the emerging concept that moves AI agents from simple question answering to reliable task execution by adding constraints, orchestration, observation, and recovery mechanisms, and shows how it builds on Prompt and Context Engineering through layered architecture and real‑world examples from OpenAI and Anthropic.

AI agentsAgent architectureAnthropic

0 likes · 16 min read

Harness Engineering: The Critical Factor That Determines AI Agent Performance

Fun with Large Models

Apr 3, 2026 · Artificial Intelligence

Fast Guide to LangChain DeepAgents: How SubAgents Work

This article explains DeepAgents SubAgent mechanisms, showing how context isolation and task division improve complex agent workflows, details two creation methods (dictionary‑based and compiled), demonstrates a search‑and‑report demo, and outlines suitable and unsuitable scenarios with practical code examples.

AI agentsDeepAgentsLangChain

0 likes · 15 min read

Fast Guide to LangChain DeepAgents: How SubAgents Work

Fun with Large Models

Apr 1, 2026 · Artificial Intelligence

A Beginner's Deep Dive into Large‑Model Training Parameters with LLaMAFactory

This article walks readers through the three major training methods—full‑parameter, LoRA, and QLoRA—explaining their memory costs, data requirements, and trade‑offs, then provides a line‑by‑line breakdown of LLaMAFactory configuration files, hyper‑parameter tuning guidelines, and the process for merging LoRA adapters into a deployable model.

LLaMAFactoryLoRAQLoRA

0 likes · 27 min read

A Beginner's Deep Dive into Large‑Model Training Parameters with LLaMAFactory

Fun with Large Models

Mar 25, 2026 · Artificial Intelligence

Quick Guide to LangChain DeepAgents: Core Features and Fast Onboarding

This article introduces the background and key advantages of the DeepAgents framework, explains its four core capabilities—task planning, context management, sub‑agent generation, and long‑term memory—and provides a step‑by‑step code example that builds a complex AI agent with just a few lines of Python.

AI agentsDeepAgentsLangChain

0 likes · 11 min read

Quick Guide to LangChain DeepAgents: Core Features and Fast Onboarding

Fun with Large Models

Mar 20, 2026 · Artificial Intelligence

Step‑by‑Step Guide to Using LLaMAFactory for Full‑Cycle Large‑Model Training (Part 9)

This article walks through the complete workflow of fine‑tuning a Qwen2.5‑0.5B model with LLaMAFactory, covering environment setup, model download, dataset preparation, configuration editing, training execution, LoRA weight merging, and deployment via vLLM, while highlighting the framework’s minimal‑code and broad model support.

AI model trainingLLaMAFactoryLoRA

0 likes · 12 min read

Step‑by‑Step Guide to Using LLaMAFactory for Full‑Cycle Large‑Model Training (Part 9)

Fun with Large Models

Mar 15, 2026 · Artificial Intelligence

A Complete Guide to 2026’s Hottest Tech Concept: Agent Engineering

The article explains Agent Engineering—a systematic approach that turns nondeterministic large‑language‑model agents into reliable production‑grade applications through an iterative build‑test‑deploy‑observe‑improve loop, combining product, engineering, and data‑science thinking to address unpredictability and achieve continuous growth.

AI AgentData‑Driven OptimizationIterative Development

0 likes · 12 min read

A Complete Guide to 2026’s Hottest Tech Concept: Agent Engineering

Fun with Large Models

Mar 11, 2026 · Artificial Intelligence

LangChain DeepAgents Quick Guide – FileSystem Middleware Gives AI Agents System‑Level Memory Management

This article explains why AI agents need a memory‑management solution, introduces LangChain DeepAgents' FileSystem middleware, details its four backend options for short‑term, long‑term, disk‑based, and hybrid storage, and provides step‑by‑step Python examples for installing, configuring, and using the middleware in real‑world scenarios.

AI AgentDeepAgentsFileSystemMiddleware

0 likes · 16 min read

LangChain DeepAgents Quick Guide – FileSystem Middleware Gives AI Agents System‑Level Memory Management

Fun with Large Models

Mar 8, 2026 · Artificial Intelligence

EasyDataset: End-to-End Guide for Generating QA Datasets for LLM Fine‑Tuning

This article walks through the complete workflow of using EasyDataset to create high‑quality question‑answer pairs for supervised fine‑tuning, covering question generation (single and batch), three generation algorithms, answer generation (including chain‑of‑thought and multi‑turn dialogue), a hybrid quality‑assessment pipeline, and export to Alpaca or ShareGPT formats.

Alpaca formatData qualityEasyDataset

0 likes · 18 min read

EasyDataset: End-to-End Guide for Generating QA Datasets for LLM Fine‑Tuning