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.
This article details JD.com's challenges and solutions for deploying domestic NPU chips across heterogeneous GPU‑NPU clusters, covering architecture, scheduling, high‑performance training and inference engines, real‑world case studies, and future plans to scale AI workloads securely and efficiently.
The article analyzes large‑model interview expectations, explaining why superficial training experience often hurts, how top‑conference papers are viewed, and why interviewers prioritize solid fundamentals and curiosity over mere resume items, offering concrete high‑impact experience examples.
This article recounts how a JD 2023 campus recruit transformed from a confused student into a technically influential backend engineer by self‑learning, building a large‑model‑driven AI system, contributing to open‑source, winning awards, and reflecting on the challenges and opportunities of modern software development.
