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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Tasks: Minimal Diagnostic Suite
Principle
Tasks must expose compositional/generalization structure (OOD) and include at least one "structure-break" perturbation.
Baseline tasks (already in your stack)
- bind/count family with:
- IID split
- OOD split
- bind-shuffle / structure-break perturbation
Add ONE additional OOD axis (minimum)
Pick at least one:
- Length extrapolation:
- train max length L_train
- test at L_test > L_train
- Role swap:
- swap argument roles at test
- Compositional novelty:
- novel combinations of primitives at test
- Symbol remap:
- permute vocabulary at test (same underlying structure)
Dataset format (canonical record)
Each sample should produce:
record.json: canonical structured representation of the instanceanswer.json: canonical structured representation of the targetrender_q_en(record) -> strrender_a_en(answer) -> str
Reporting requirement
At eval time, dump a small fixed set of examples with:
- English question
- model prediction rendered to English
- gold answer rendered to English