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: 851 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 | """
Quickstart — see the triangle in action in under a minute.
Usage:
pip install torch transformers
python quickstart.py
Uses the three smallest open models that fit on ~4GB VRAM.
Swap any model name for your own.
"""
from nova_triangle.triangle import Triangle
print("Loading three models (first run downloads them)...\n")
tri = Triangle(
models=[
"HuggingFaceTB/SmolLM2-360M-Instruct",
"Qwen/Qwen2.5-0.5B-Instruct",
"HuggingFaceTB/SmolLM2-135M-Instruct",
],
max_rounds=2,
)
questions = [
"What is the oldest known written language?",
"Explain quantum superposition in one sentence.",
"What happens when three perspectives look at the same problem?",
]
for q in questions:
print(f"Q: {q}")
result = tri.process(q)
print(tri.report(result))
print("-" * 60)
print()
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