Permission request: non-commercial research mirror of tokenized UltraData sets (SHADOW-125M)
Nobody has claimed this yet.
Assessment
- Difficulty
- 5/5
- Estimated time
- Over a week
- Newbie friendliness
- 15/100
- Issue type
- Feature
- Clarity
- Needs clarification
- Activity status
- Active
- Tech stack
- huggingface
- Domain
- data
Research direction
No repository file, test, or implementation entry point is identified. Review the cited UltraData dataset cards and MiniCPM5 tokenizer terms, then determine whether OpenBMB can grant the requested redistribution permission. Done means recording a clear approval, limitation, or refusal for the proposed public tokenized mirror.
Written by the indexing model from the issue text.
Description
Hi OpenBMB team,
I'm Sai Kiran Bathula (Hugging Face / GitHub: QLNI), an independent researcher building SHADOW, a family of small ternary (1.58-bit) language models designed to run offline on a laptop CPU.
Already released:
- SHADOW-250M-Instruct: 250M parameters, 60 MB deployment, ~400 tokens/s on a laptop CPU, 100M-token offline context.
https://huggingface.co/QLNI/SHADOW-250M · https://github.com/QLNI/SHADOW-250M-Instruct - SHADOW-50M-Instruct: 44M ternary parameters, 19.8 MB, memory on disk, runs offline on a CPU.
https://huggingface.co/QLNI/shadow-50m-instruct · https://github.com/QLNI/SHADOW-50M-Instruct - SHADOW-50M-Vision data (the public multimodal corpus for our vision work): https://huggingface.co/datasets/QLNI/shadow-50m-vision
Next: SHADOW-125M. This is a ternary, looped transformer over a frozen 512-bit word table, with ternary Engram memory and a separately trained retrieval index. It uses your MiniCPM5 tokenizer, and we plan to train on about 2T tokens from your UltraData family: Ultra-FineWeb / -L3, UltraX, UltraData-Math, UltraData-Code, SFT-2605, SFT-Agent-2609 and RL-2609. Your data recipe and the MiniCPM5-2B results are the reason we chose it.
Your cards say "no unauthorized unchanged redistribution", so we're asking before posting anything. We would like written permission to host a tokenized copy (MiniCPM5 token ids, in the mix and order we train on) in a public Hugging Face dataset repo, for non-commercial research use only.
Why public:
- Cost. Tokenizing 2T tokens takes about a week of CPU time, and private storage for ~5 TB is a monthly cost a small independent project can't carry. A public repo removes both.
- Reproducibility. Researchers can rerun our exact training without re-tokenizing, and our paper can point to the exact data.
What we would do:
- Release it strictly as non-commercial, research-only, stated at the top of the card.
- Label it clearly as derived from OpenBMB UltraData, not our data, with links to every source dataset and citations of your papers.
- Keep your redistribution notice and the terms of each upstream source on the card.
- Include a per-shard manifest (source file, revision) and honour any takedown request from you or rights holders within 48 hours.
If a full mirror isn't possible, we would welcome permission for part of it (for example only Ultra-FineWeb-L3 and UltraData-Math). Otherwise we'll keep the tokens private and publish only the recipe and the file list.
Thank you for releasing this data.
Sai Kiran Bathula (QLNI)
- Dominant language
- Jupyter Notebook
- Stars
- 11.3k
- Forks
- 778
- Avg merge
- 5h 3m
- Merged PRs (30d)
- 6
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
- 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.
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