Instructions to use throsturx/bihmoe-poc with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use throsturx/bihmoe-poc with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("throsturx/bihmoe-poc", dtype="auto") - Notebooks
- Google Colab
- Kaggle
Experiment Protocol
Runs
- Run ID format: YYYYMMDD-HHMMSS__seed
Training
- Train structured S and dense D_a side-by-side on the same batch stream.
- Update order per step:
- forward/backward/step S
- forward/backward/step D_a
- Eval cadence: every 500–1000 steps (fixed).
Splits
- Fixed IID eval set
- Fixed OOD eval set
- Fixed structure-break eval set (e.g., bind-shuffle)
Metrics
Primary:
- IID accuracy/loss
- OOD accuracy/loss
- structure-break accuracy/loss
Secondary (structured-only):
- expert usage entropy
- hemisphere disagreement vs error (simple AUROC)
Early-kill criteria
PoC success if ANY:
- S > D_a on OOD by clear margin at same budget.
- S degrades less under structure-break perturbation.
- Disagreement is a useful error signal.
PoC failure:
- No meaningful separation on OOD/robustness and no disagreement signal within a small budget.
Artifacts
All outputs must go under: /store/speedy/dualbrain/out/bihmoe-poc//