Sieve-9B
Sthānika AI's open decision-model family. A LoRA adapter and answer head on a Qwen3.5 backbone read a text or JSON state once and return a calibrated probability for every option of choice, yes/no and score questions, without generating text.
This entry is Sieve-9B: a rank-32 LoRA (two rank-16 runs averaged) and a Kev-style pointer head on the post-trained Qwen3.5-9B, trained mostly on Kev's decision-v7 data. The repo holds the adapter and head, not the base weights; it loads through the sieve package from GitHub and needs about 20 GB of GPU memory. Each question runs from the cached state; up to 255 options. On the maker's development partitions: 87.2% in-domain (ECE 0.028), 82.9% on an out-of-domain transfer set. Sthānika's own run of the third-party Decision Index 0.2.1 kit gives 41.71; the board's 0.3 release shows Sieve 9B at 41.88 and Sieve 2B at 22.16. Siblings, built differently: Sieve-4B (a 255-way option-code head on Qwen3.5-4B; 67.7% on typed-decisions, self-run Index 43.82, not on the board) and sieve-2b (scores each option separately on 16 blocks of Qwen3.5-2B-Base, so option order cannot change the answer). Weights are Apache-2.0, but part of the training data keeps its own terms, some non-commercial (27% of Sieve-4B's records); the maker asks users to check them before commercial use. The cards say the lab is not affiliated with TypeSafe AI.
What it decides
- choice — picks one option from a set
- score — places the input on an ordered scale
- noul — answers a yes/no question with one probability
At a glance
| Parameters | 9B |
| Base model | qwen/qwen3.5-9b |
| Maker | Sthānika AI |
| Released | 2026-09-29 |
| License | apache-2.0 |
| Reported accuracy | 70.4% |
Get the weights
pip install systemonemodels
systemone pull sthanika-ai/sieve
The files are served from the maker's Hugging Face repository, sthanika-ai/Sieve-9B, and verified against the checksums recorded here.
Read more
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