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

飞桨多模态大模型套件PaddleMIX开发大赛

A intermediate llm competition requiring roughly 12–40 hours for an end-to-end practice run.

Difficulty
Intermediate
Estimated effort
12–40 hours
Registration deadline
To be confirmed
Compute
CPU only
OverviewPreparationSource

Overview

Who this competition fits

适合有飞桨/PaddleMIX 代码开发经验,且能处理图文多模态训练数据的开发者或团队。参赛者需要能围绕 llava_v1_5_mix665k 实现数据分析、过滤、配比或可视化算子,并在 LLaVA1.5 的 SFT 阶段验证效果;页面说明国内及全球开发者均可报名,参赛对象不设限。

Read the original official blurb
本次多模态数据提质大赛赛题名称为“为飞桨多模态大模型套件丰富数据分析和处理的能力”。旨在完善飞桨多模态大模型套件数据分析和处理能力,减少用户开发成本。 参赛项目将验证有效的数据处理方案合入PaddleMIX套件,包括数据分析和处理功能、单元测试适配、文档适配。技术路径采用开源数据llava_v1_5_mix665k,有效性验证试验在LLaVA1.5 sft阶段进行。技术方案可从单条数据和数据集多个角度进行数据质量的分析过滤,也可以从训练数据配比的角度分析训练数据的均衡性。 Official category: 实战竞技赛. Organizers: 百度飞桨. Participants: 199.

Preparation

From registration to a first submission

  1. 01

    PaddleMIX 开发

  2. 02

    多模态数据处理

  3. 03

    LLaVA1.5 SFT 训练

  4. 04

    Python 数据处理算子开发

  5. 05

    GitHub Fork 与 Pull Request

  6. 06

    单元测试与技术文档编写

Before you commit: 难点不只是提出数据清洗思路,而是将可验证的处理算子做成可被 PaddleMIX 接收的开源贡献:完成模型训练验证、评估结果和日志、单元测试、文档,并通过代码 review 合入主分支。若要在模型上验证数据策略,页面要求具备使用 4 张或 8 张 A100 卡进行 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
OpenAtom
Last checked
Sep 21, 2024

Entry assessment

est.
48

Prepare before joining

Overall fit estimate

Beginner fit48/100
Learning value60/100

Confirm eligibility and time commitment before moving into formal preparation.

Entry eligibility needs official confirmation

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Data and review state

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

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Source page verifiedMetric confirmedReviewed
Source published
Sep 21, 2024
Source created
Sep 21, 2024
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.