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ModelScopeLLMAdvancedRegistration closed

AI PC Agent Skills 征文活动

A advanced llm competition requiring roughly 25–70 hours for an end-to-end practice run.

Difficulty
Advanced
Estimated effort
25–70 hours
Registration deadline
Jun 30, 2026
Compute
Free GPU OK
OverviewPreparationSource

Overview

Who this competition fits

Advanced practitioners with experience in machine learning. Requires strong technical foundations and comfort with complex ML pipelines.

Read the original official blurb
本次活动重点鼓励开发者探索如何利用 Intel 酷睿 Ultra 处理器(如最新的 Panther Lake 架构的 GPU、NPU 加速能力),配合 OpenVINO 推理框架,将模型能力转化为可编程、可复用的Skill技能包。这是 AI PC 真正从“硬件概念”走向“生产力工具”的核心跨越。 Official category: 研习社征文赛. Participants: 83. Organizers: Intel, OpenVINO™中文社区, 魔搭社区.

Preparation

From registration to a first submission

  1. 01

    Python

  2. 02

    scikit-learn or similar ML library

  3. 03

    data analysis with pandas

Before you commit: Requires solid machine learning fundamentals and good experimental methodology. The competitive landscape is strong, with many experienced teams.

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
ModelScope
Last checked
Apr 30, 2026

Entry assessment

est.
22

Prepare before joining

Overall fit estimate

Beginner fit22/100
Learning value69/100

Confirm eligibility and time commitment before moving into formal preparation.

Entry eligibility needs official confirmation

Open official pageSign in to saveOpen guide

Timeline

Registration opens
Apr 30, 2026
Registration deadline
Jun 30, 2026

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
Apr 30, 2026
Published on this site
Jul 21, 2026

Official metric is not published on the ModelScope 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.