Transformers
English
triangulated-inference
edge-ai
ensemble
small-models
nova-triangle
gradient-ascent
self-correcting
Instructions to use Wayfinder6/nova-triangle with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Wayfinder6/nova-triangle with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Wayfinder6/nova-triangle", dtype="auto") - Notebooks
- Google Colab
- Kaggle
| """ | |
| TriangleResult — What comes back when three models deliberate. | |
| """ | |
| from dataclasses import dataclass, field | |
| from typing import Optional | |
| class TriangleResult: | |
| """The output of a triangulated inference.""" | |
| answer: str | |
| """The converged answer (or best candidate if no convergence).""" | |
| confidence: float | |
| """0.0 to 1.0. How much the three models agreed.""" | |
| converged: bool | |
| """True if all three models reached consensus.""" | |
| disagreement: dict = field(default_factory=dict) | |
| """Where the models diverged. Keys are model names, values are their raw answers.""" | |
| flag: Optional[str] = None | |
| """If disagreement was significant, this describes what they fought about. | |
| A flag is signal, not failure. It means the models found something worth examining.""" | |
| raw_responses: list = field(default_factory=list) | |
| """The unprocessed response from each model, in order.""" | |
| steering_model: Optional[str] = None | |
| """Which model steered this round (proposed the answer the others evaluated).""" | |
| rounds: int = 1 | |
| """How many deliberation rounds it took to converge (or max_rounds if it didn't).""" | |