Instructions to use dhresearch/quality-router-v2-domain-v2shape with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Scikit-learn
How to use dhresearch/quality-router-v2-domain-v2shape with Scikit-learn:
from huggingface_hub import hf_hub_download import joblib model = joblib.load( hf_hub_download("dhresearch/quality-router-v2-domain-v2shape", "sklearn_model.joblib") ) # only load pickle files from sources you trust # read more about it here https://skops.readthedocs.io/en/stable/persistence.html - Notebooks
- Google Colab
- Kaggle
Domain policy, v2 interface shape
This file is a seed-0 refit made on 2026-10-03. The original training run wrote predictions and did not save weights. This upload is the refit.
quality_router_v2 version 8 sets learned_downrouting to false. The served card loads no fitted weights. This file is a learned model from the same study, and the served card leaves it unloaded.
Role
The same route-conditioned family with --code-fallback-interfaces function_call, so stdin_stdout is outside the code-tier fallback. The served flow does not call this object. Economy mode on the served card names a learned downrouter and is off.
Data
Same main export as the domain policy. Dataset: https://huggingface.co/datasets/dhresearch/outcome-router-v2-main.
Fit
The domain command above, plus --code-fallback-interfaces function_call.
Seed 0. Software at refit time: python 3.14.2, sklearn 1.9.1, numpy 2.5.3, joblib 1.6.0, lightgbm 4.7.0, scipy 1.18.1.
Agreement
Frozen file data/real_v2/preds_v2_domain_v2shape.jsonl: predicted_route matches on 250 of 250 shared test rows (rate 1.000000). Full-row matches, including rounded probabilities and labels: 250 of 250. Mismatched keys: {}. seed-0 refit. The original run wrote this prediction file and did not save weights. This upload is not the original weight file.
Load
Unpickle with joblib while outcome_router_data is importable.
Class: RouteConditionedSuccessRouter.
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