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!
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.
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.
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.
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.
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.
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.
The article explains how to estimate GPU memory and overall hardware requirements for fine-tuning and training large dense and MoE models, detailing calculations for full-parameter and LoRA approaches, scaling rules, and hidden costs relevant to interview assessments.
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.
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.
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.
