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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

100 recent articles max

Fun with Large Models

Sep 24, 2025 · Artificial Intelligence

Interview Guide: Core Differences Between PPO and GRPO Algorithms for Large Model Fine‑Tuning

The article explains the fundamental principles of PPO and GRPO reinforcement‑learning algorithms, compares their architectures and training workflows, highlights why GRPO is gaining traction in large‑model fine‑tuning, discusses associated risks, and offers practical guidance on group size selection for engineers preparing for interviews.

GRPOPPORLHF

0 likes · 9 min read

Interview Guide: Core Differences Between PPO and GRPO Algorithms for Large Model Fine‑Tuning

Fun with Large Models

Sep 22, 2025 · Artificial Intelligence

Building Conditional Branch and Loop Graphs with LangGraph AI Agents

This tutorial demonstrates how to use LangGraph's low‑level API to create stateful conditional‑branch, loop, and combined graphs for AI agents, showing step‑by‑step definitions of Pydantic state models, node logic, edge configuration, compilation, and test execution with concrete code examples.

AI AgentsLangGraphPydantic

0 likes · 12 min read

Building Conditional Branch and Loop Graphs with LangGraph AI Agents

Fun with Large Models

Sep 20, 2025 · Artificial Intelligence

Getting Started with LangGraph Low‑Level API: Designing Nodes, Edges, and State

This tutorial explains the design principles of LangGraph's low‑level graph API—nodes, edges, and State—demonstrates a simple addition‑subtraction workflow, shows how to visualize and execute the graph, and introduces Pydantic‑based State validation for more robust agent development.

AI AgentsGraph WorkflowLangGraph

0 likes · 12 min read

Getting Started with LangGraph Low‑Level API: Designing Nodes, Edges, and State

Fun with Large Models

Sep 17, 2025 · Artificial Intelligence

Evaluating Fine-Tuned Large Model Performance: Methods and Interview Tips

The article explains how to assess fine‑tuned large models using both human judgment and dataset‑driven metrics, outlines common pitfalls, introduces benchmark datasets and evaluation frameworks, and provides concise answers to related interview questions.

EvalScopebenchmark datasetsevaluation

0 likes · 7 min read

Evaluating Fine-Tuned Large Model Performance: Methods and Interview Tips

Fun with Large Models

Sep 16, 2025 · Artificial Intelligence

LangGraph Data Analysis Assistant Agent: Step‑by‑Step Project Guide (Part 5)

This tutorial walks you through building a LangGraph-powered data analysis assistant that converts natural language into SQL, executes queries via NL2SQL and NL2Python tools, visualizes results with a Python interpreter, and deploys the agent using LangGraph CLI and Agent Chat UI for end‑to‑end interaction.

AI AgentAgent Chat UIData Analysis

0 likes · 21 min read

LangGraph Data Analysis Assistant Agent: Step‑by‑Step Project Guide (Part 5)

Fun with Large Models

Sep 12, 2025 · Artificial Intelligence

When to Choose Model Fine‑Tuning vs RAG for Large‑Model Engineering Interviews

The article explains the technical background and suitable scenarios for Retrieval‑Augmented Generation (RAG) and model fine‑tuning, compares their strengths, discusses how they can be combined, and provides interview‑style Q&A on their capabilities, risks, and differences from model distillation.

AI InterviewFine‑TuningRAG

0 likes · 7 min read

When to Choose Model Fine‑Tuning vs RAG for Large‑Model Engineering Interviews

Fun with Large Models

Sep 6, 2025 · Artificial Intelligence

How to Build a High-Quality Domain-Specific Fine-Tuning Dataset for Large Models

This article outlines a systematic engineering workflow for creating professional domain fine‑tuning datasets for large models, covering data processing, validation, optimal sample size, industrial‑environment practices, and special considerations for reinforcement‑learning based fine‑tuning.

Data Processingdata validationdataset construction

0 likes · 7 min read

How to Build a High-Quality Domain-Specific Fine-Tuning Dataset for Large Models

Fun with Large Models

Sep 3, 2025 · Artificial Intelligence

Mastering Multimodal Fine-Tuning of Large Models: Interview‑Ready Techniques

The article explains how to fine‑tune large multimodal models by focusing on the projection layer, optionally using LORA for language‑model adaptation, and highlights data alignment, common applications, and the added difficulty of modality alignment for interview preparation.

fine-tuninglarge modelsmultimodal

0 likes · 6 min read

Mastering Multimodal Fine-Tuning of Large Models: Interview‑Ready Techniques

Fun with Large Models

Sep 2, 2025 · Artificial Intelligence

How to Improve Agent Performance with Fine‑Tuning: Key Strategies for AI Interviews

This article explains how to boost large‑model agent performance for interview questions by using efficient fine‑tuning—building multi‑tool parallel and chain‑call datasets—and reinforcement‑learning fine‑tuning with reward functions that target tool accuracy, task completion, and call efficiency, illustrated with concrete JSON examples and open‑source references.

AgentFunction Callingdataset

0 likes · 9 min read

How to Improve Agent Performance with Fine‑Tuning: Key Strategies for AI Interviews

Fun with Large Models

Sep 1, 2025 · Artificial Intelligence

Build a LangGraph AI Agent in Two Lines Using the Prebuilt Graph API

This tutorial shows how to set up a Python environment, install LangGraph, and use its high‑level prebuilt graph API—specifically create_react_agent—to quickly create a weather‑assistant AI agent with just two lines of code, illustrating the full tool‑calling workflow and ReACT loop.

AI AgentsLangGraphPython

0 likes · 11 min read

Build a LangGraph AI Agent in Two Lines Using the Prebuilt Graph API