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the model is underrated , are you trying to fly under the radar ?

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#33 2 comments 0 reactions 0 assignees View on GitHub

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Assessment

Difficulty
5/5
Estimated time
Over a week
Newbie friendliness
15/100
Issue type
Feature
Clarity
Needs clarification
Activity status
Stale
Tech stack
python

Research direction

Start by reviewing the paper, the current demos, and the reported training setup of 1024 GPUs over 42 days. The issue does not identify files, entry points, a specific optimization, or acceptance criteria, so the intended change and definition of done need clarification before implementation.

Written by the indexing model from the issue text.

Description

MOVA training 1024 GPUs for 42 days (~43 000 GPU‑days). and you don't bother to optimize and get your model out there on reddit and forums .
i'm reading the paper and this model should give more than even the demos you have don't reflect it's true power

"Computational Resources. All three phases run on 1024 GPUs (128 nodes, 8 GPUs per node). For 360p
training (Phases 1–2), we use CP=8, yielding effective batch size 128. For 720p fine-tuning (Phase 3), increased
sequence length requires CP=16, reducing effective batch size to 64. The complete training spans 42 days,
totaling approximately 43,000 GPU-days. "

Dominant language
Python
Stars
1.1k
Forks
95
PR merge metrics
No merged PRs in 30d

Getting set up

This project ships no dev container, Dockerfile or contributing guide, so setting up is up to you: start from its README, and see our first-contribution guide for the general steps.

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.

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