lucasjinreal / lucasjinreal/Namo-R1
Namo-V2 release
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- Dominant language
- Python
- Stars
- 256
- Forks
- 26
- PR merge metrics
- No merged PRs in 30d
Description
Hello! We just finished training of Namo-500M-V2!
The v2 version boosted a lot in OCR ability, and it adopted SiglipV2 as vision encoder. Moreover, it can handle native resolution input as usual in the Namo series.
We are still conducting mDPO on the trained V2 model. We are exploring if mDPO could further enhance the tiny model's ability or not. Stay tuned for our final release.
Also, the new V2 version will have two versions, one with 578 tokens per / img, and one with 256 tokens per / img.
Contributor guide
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First steps
- Read the whole issue, then the project's contributing guide.
- Comment on the issue to say you are picking it up — it saves two people doing the same work.
- Fork the repository and make your change on a branch.
- Open a pull request that references the issue number.
Research direction
The issue announces the Namo-500M-V2 model, SiglipV2 vision encoder, mDPO work, and two token configurations, but names no files, tests, or implementation entry point. It does not define a concrete change or completion condition, so further project context and maintainer direction are needed before work can start.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python
- Domain
- ai, machine-learning
- Issue type
- Feature
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 15/100