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  3. 飞桨多模态大模型套件PaddleMIX开发大赛
Baidu AI StudioLLMResearchRegistration unverified

飞桨多模态大模型套件PaddleMIX开发大赛

A research llm competition requiring roughly 30–90 hours for an end-to-end practice run.

Difficulty
Research
Estimated effort
30–90 hours
Registration deadline
To be confirmed
Compute
Free GPU OK
OverviewPreparationSource

Overview

Who this competition fits

适合能在 PaddleMIX 中开发 Python 数据处理组件的开发者或团队,尤其是熟悉图文多模态数据、LLaVA 训练数据清洗/配比,并能完成 LLaVA1.5 SFT 验证的人。参赛对象不限,国内及全球开发者均可报名;高校、企业和科研院所也在主要申报单位范围内。

Read the original official blurb
飞桨多模态大模型套件PaddleMIX整合了业界前沿的多模态大模型与飞桨框架底层高性能技术,全面兼顾高性能算法、便捷开发、高效训练和完备部署,其丰富的多模态模型库覆盖图像、文本、视频、音频模态模型。为完善百度飞桨多模态大模型套件PaddleMIX的数据分析和处理能力,参赛项目有机会将验证有效的数据处理方案合入企业实际产品中,包括数据分析和处理功能、单元测试适配、文档适配等,从而为千万用户降本增效。 Official tags: 1. Sign-ups: 16.

Preparation

From registration to a first submission

  1. 01

    PaddlePaddle/PaddleMIX 开发

  2. 02

    多模态数据质量分析

  3. 03

    LLaVA 1.5 SFT 微调

  4. 04

    A100 多卡训练

  5. 05

    GitHub Fork 与 Pull Request

  6. 06

    Python 单元测试与文档编写

Before you commit: 难点不只是提出数据过滤思路,而是要把可验证有效的细粒度处理算子做成可合入 PaddleMIX 的工程代码:完成数据集实验、可视化或模型训练验证、单测和文档,并通过代码 Review 后合入主分支。若要在模型上验证策略,页面要求具备使用 4 张或 8 张 A100 卡进行 LLaVA SFT 训练的环境。

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
Baidu AI Studio
Last checked
Not recorded

Entry assessment

est.
22

Prepare before joining

Overall fit estimate

Beginner fit22/100
Learning value58/100

Confirm eligibility and time commitment before moving into formal preparation.

Entry eligibility needs official confirmation

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Data and review state

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Source page verifiedMetric confirmedReviewed
Published on this site
Jul 21, 2026

Official metric is not published on the Baidu AI Studio 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.