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
| """ | |
| 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() | |