OpenBMB / OpenBMB/MiniCPM-V

Verify evals on Papers with Code

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Description

Hi,

Niels here from the open-source team at Hugging Face. Congratulations on your work!

I've made the paper and 25 verified paper-native evaluations available on Papers with Code.

The paper has results on Image Understanding, Video Understanding, Omni models, and 8 additional task pages.

The MiniCPM-o 4.5-Instruct results currently rank first on OmniDocBench v1.0 and VideoHolmes.

The MiniCPM-o 4.5-Instruct results currently rank third on DailyOmni and MMT-Bench.

Would it be possible to verify these results and let me know if any score, model name, benchmark protocol, or openness metadata should be corrected? The imported rows are tied to the paper or its official release artifacts; comparison-table baselines were not added.

You can also edit the task, methods, project page, and GitHub URL directly from the paper page using your Hugging Face account.

If you'd like to showcase the results in your repository README, you can copy these live leaderboard badges (or use the “Copy PwC badge” button in the Results section):

Papers with Code: SOTA on FutureOmni
Papers with Code: SOTA on OmniDocBench v1.0
Papers with Code: SOTA on VideoHolmes
Papers with Code: #3 on DailyOmni
Papers with Code: #3 on MMT-Bench
Papers with Code: #3 on WorldSense

Kind regards,

Niels

Contributor guide

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First steps

  1. Read the whole issue, then the project's contributing guide.
  2. Comment on the issue to say you are picking it up — it saves two people doing the same work.
  3. Fork the repository and make your change on a branch.
  4. Open a pull request that references the issue number.

Research direction

Start with the linked paper on Papers with Code and review the OmniDocBench, VideoHolmes, DailyOmni, MMT-Bench, WorldSense, and other linked task results. Verify each score, model name, benchmark protocol, and openness metadata against the paper or official release artifacts; done means reporting any corrections needed.

Written by the indexing model from the issue text.

Assessment

Tech stack
huggingface
Domain
machine-learning
Issue type
Documentation
Difficulty
4/5
Estimated time
3-5 days
Activity status
Quiet
Clarity
Mostly clear
Newbie friendliness
48/100

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