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  3. ModelEngine AI 创新应用学习赛
OpenAtomLLMAdvancedRegistration unverified

ModelEngine AI 创新应用学习赛

A advanced llm competition requiring roughly 24–80 hours for an end-to-end practice run.

Difficulty
Advanced
Estimated effort
24–80 hours
Registration deadline
To be confirmed
Compute
CPU only
OverviewPreparationSource

Overview

Who this competition fits

适合想把大模型做成可运行行业智能体的开发者或小团队,尤其是愿意学习 ModelEngine 的 App Platform 或 Nexent,并能围绕教育、医疗、法律、金融、政务或工业场景设计应用的人。也适合具备 RAG、模型微调、低代码编排、Agent 或自定义插件开发经验的参与者;赛事面向个人和组织开放。

Read the original official blurb
ModelEngine提供从数据处理、知识生成,到模型微调和部署,以及RAG应用开发的AI训推全流程工具链。本次竞赛鼓励参赛者基于ModelEngine中的一站式可视化应用编排平台App Platform及多模态智能体平台Nexent,结合教育、医疗、法律、金融、政务、工业等垂直行业需求,开发具有创新性、实用性的大模型应用。 Official category: 训练学习赛. Organizers: 华为技术有限公司. Competition code: 2025OAC030. Participants: 5.

Preparation

From registration to a first submission

  1. 01

    ModelEngine App Platform

  2. 02

    Nexent 平台

  3. 03

    大模型应用开发

  4. 04

    RAG 知识库构建

  5. 05

    提示词配置

  6. 06

    源码与技术文档编写

Before you commit: 难点不只是做出问答原型,而是将应用深度适配 App Platform 或 Nexent,交付可部署、可复现的完整配置与源码,并以视频演示和线上答辩证明全链路功能、应用价值及用户体验。若想争取附加分,还需要为平台开发或优化特性,并在社区提交代码。

Source

How this page was assembled

Competition information is structured from the official page. Scores are platform estimates for decision support; official rules take precedence.

Official competition page
OpenAtom
Last checked
Aug 28, 2025

Entry assessment

est.
30

Prepare before joining

Overall fit estimate

Beginner fit30/100
Learning value60/100

Confirm eligibility and time commitment before moving into formal preparation.

Entry eligibility needs official confirmation

Open official pageSign in to saveOpen guide

Data and review state

Separate what is confirmed from what still needs review. Official rules and deadlines win.

View data stateClose
Source page verifiedMetric confirmedReviewed
Source published
Aug 28, 2025
Source created
Aug 28, 2025
Published on this site
Jul 21, 2026

Official metric is not published on the OpenAtom competition listing; verify scoring on the competition page.

reward_value_usd is a rough CNY→USD estimate (×0.14) for ranking only; use reward_summary for the official prize text.