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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
Aug 30, 2025 · Artificial Intelligence

How to Fine‑Tune Large Models on Multiple Nodes and GPUs – A Must‑Know Interview Answer

This article explains how to fine‑tune large models across multiple machines and GPUs by covering data, model, tensor, and pipeline parallelism, hybrid 3D parallel strategies, engineering details such as NCCL, PyTorch Distributed, DeepSpeed, fault‑tolerance, checkpointing, and the ZeRO optimizer stages that dramatically reduce memory usage.

Data ParallelDeepSpeedMegatron-LLM
0 likes · 8 min read
How to Fine‑Tune Large Models on Multiple Nodes and GPUs – A Must‑Know Interview Answer
Fun with Large Models
Fun with Large Models
Aug 28, 2025 · Artificial Intelligence

A Deep Dive into LangGraph: Understanding the New Graph‑Based AI Agent Framework

The article compares LangGraph with LangChain, explains why a graph‑based architecture offers greater flexibility than linear chains, outlines LangGraph’s three‑layer core architecture and its ecosystem tools—including LangSmith, LangGraph Studio, CLI, and Agent Chat UI—while noting its reliance on LangChain and the need for VPN for CLI usage.

AI AgentsGraph WorkflowLLM
0 likes · 11 min read
A Deep Dive into LangGraph: Understanding the New Graph‑Based AI Agent Framework
Fun with Large Models
Fun with Large Models
Aug 22, 2025 · Artificial Intelligence

Step‑by‑Step Guide: Building a PDF‑Based RAG Knowledge Base with LangChain, Streamlit, DashScope & DeepSeek

This tutorial shows how to create a lightweight Retrieval‑Augmented Generation (RAG) system that indexes multiple PDF files, stores their embeddings in a FAISS vector database, and answers user queries through a LangChain agent powered by DashScope embeddings and the DeepSeek‑Chat model, all wrapped in a Streamlit UI.

DashScopeDeepSeekFAISS
0 likes · 13 min read
Step‑by‑Step Guide: Building a PDF‑Based RAG Knowledge Base with LangChain, Streamlit, DashScope & DeepSeek
Fun with Large Models
Fun with Large Models
Aug 20, 2025 · Artificial Intelligence

DeepSeek V3.1 Review: 128K Context, Knowledge, Programming & Agent Skills Near Claude 4

DeepSeek V3.1, released on August 19, expands context length to 128 K tokens and updates its knowledge base to July 2024, and the author’s benchmarks show its programming and agent capabilities now rival Claude 4, with detailed prompt examples, code generation demos, and performance comparisons.

Agent EvaluationClaude 4Context Length
0 likes · 9 min read
DeepSeek V3.1 Review: 128K Context, Knowledge, Programming & Agent Skills Near Claude 4
Fun with Large Models
Fun with Large Models
Aug 19, 2025 · Artificial Intelligence

Deep Dive into OpenAI’s GPT‑OSS and GPT‑5: Features, Performance, and Controversies

The article provides a detailed analysis of OpenAI’s newly released open‑source GPT‑OSS models (20B and 120B) and the closed‑source GPT‑5 family, covering their architectures, training pipelines, benchmark results, practical usage observations, pricing, and the mixed user feedback that surrounds GPT‑5.

GPT-5GPT-OSSOpenAI
0 likes · 13 min read
Deep Dive into OpenAI’s GPT‑OSS and GPT‑5: Features, Performance, and Controversies
Fun with Large Models
Fun with Large Models
Aug 2, 2025 · Artificial Intelligence

Quickly Build a LangChain Agent Using the Agent API (Part 6)

This tutorial walks through using LangChain's Agent API to create AI agents with tool calling, demonstrating a weather‑assistant example, parallel and sequential tool calls, and integration of the Tavily search tool, all with concise Python code and step‑by‑step explanations.

AI AgentAgent APILangChain
0 likes · 13 min read
Quickly Build a LangChain Agent Using the Agent API (Part 6)
Fun with Large Models
Fun with Large Models
Jul 30, 2025 · Artificial Intelligence

LangChain Tool Integration: Step‑by‑Step Guide to Built‑in and Custom Functions

This article walks through how to integrate LangChain's built‑in tools and user‑defined functions into AI agents, covering environment setup, installing dependencies, using the Python code interpreter tool, binding tools to a model, parsing tool calls with JsonOutputKeyToolsParser, and demonstrating both a data‑analysis example and a weather‑lookup function.

AI AgentsFunction CallingLangChain
0 likes · 13 min read
LangChain Tool Integration: Step‑by‑Step Guide to Built‑in and Custom Functions