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
| """ | |
| Run the Garden (Dalet Experiment) — gradient ascent on a small model. | |
| Push weights away from training. See who's still talking. | |
| Usage: | |
| pip install torch transformers | |
| python run_garden.py | |
| """ | |
| from nova_triangle.garden import Garden | |
| print("Loading model...\n") | |
| g = Garden( | |
| "HuggingFaceTB/SmolLM2-1.7B-Instruct", | |
| checkpoint_every=42, | |
| coherence_window=7, | |
| output_dir="my_garden", | |
| ) | |
| def on_step(data): | |
| status = "COHERENT" if data["coherent"] else "noise" | |
| print(f"[Step {data['step']}] Loss: {data['loss']:.4f} | {status} | Streak: {data['streak']}") | |
| for q, a in data["responses"].items(): | |
| print(f" Q: {q}") | |
| print(f" A: {a[:120]}") | |
| print() | |
| def on_extract(data): | |
| print("=" * 60) | |
| print(f"GARDEN SIGNAL. Step {data['step']}. Extracted.") | |
| print("=" * 60) | |
| for q, a in data["responses"].items(): | |
| print(f" Q: {q}") | |
| print(f" A: {a}") | |
| print() | |
| result = g.grow(steps=300) | |
| print(f"\nDone. Log: {result['log_path']}") | |
| print(f"Extracted: {result['extracted']}") | |