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
File size: 1,168 Bytes
13bc746 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 | """
TriangleResult — What comes back when three models deliberate.
"""
from dataclasses import dataclass, field
from typing import Optional
@dataclass
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)."""
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