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
| # Architecture notes (high level) | |
| This repo compares a dense baseline vs a structured "dual-stream" model on synthetic compositional tasks. | |
| Key stabilized regime (what produced the replicated ood_long gains): | |
| - chiasm_mode = bias (no information starvation) | |
| - curriculum mixing that includes long+gap samples during training | |
| - Schedule-2 with ramp; braid rehearsal prevents late-phase regression | |
| See `docs/preliminary_results.md` for the replication panel and ablations. | |