metadata
license: mit
datasets:
- antonypamo/savantorganized
language:
- en
🧬 ProSavantEngine Φ9.3 — Icosahedral Resonance Language Model
Author: [Antony Padilla Morales](https://huggingface.co/antonypamo
🧠 Overview
ProSavantEngine Φ9.3 extends the Resonance of Reality Framework (RRF) by coupling language semantics and icosahedral geometry through node-conditioned tokens [NODE_1]–[NODE_12].
Each text sample during training was enriched with its geometric node context, allowing the model to align meaning with spatial-frequency symmetry.
This version fine-tunes from Φ9.2-Lite on the full RRF corpus corpus_unificado_total.jsonl, augmented with icosahedron_nodes.json.
🚀 Quick Start
Install dependencies:
pip install torch transformers datasets scipy plotly gradio
Run inference:
from transformers import AutoTokenizer, AutoModelForMaskedLM
tok = AutoTokenizer.from_pretrained("antonypamo/ProSavantEngine_Phi9_3")
model = AutoModelForMaskedLM.from_pretrained("antonypamo/ProSavantEngine_Phi9_3")
text = "Quantum resonance aligns with [NODE_5]"
inputs = tok(text, return_tensors="pt")
outputs = model(**inputs, labels=inputs["input_ids"])
print(outputs.loss)
🧩 Fine-Tuning from the Hub
You can continue training directly from the Hub:
from transformers import Trainer, TrainingArguments
args = TrainingArguments.from_pretrained("antonypamo/ProSavantEngine_Phi9_3")
trainer = Trainer.from_pretrained(
"antonypamo/ProSavantEngine_Phi9_3",
args=args,
train_dataset=my_dataset,
eval_dataset=my_eval
)
trainer.train()
📦 Requirements
torch
transformers
datasets
scipy
plotly
gradio
📚 Dataset
The model was trained on the unified corpus
antonypamo/savantorganized
and linked with icosahedron_nodes.json providing the 12-node geometric structure of the icosahedral lattice.
🔮 Applications
Resonant rewriting and coherence scoring
Prompt optimization and semantic filtration
Geometric–linguistic embeddings for RRF AI models
Integration into AGORA / SavantEngine resonance simulations
Cognitive field modeling and symbolic AI research
🧭 Related Resources
antonypamo/ProSavantEngine_Phi9_2_Lite
— prior iteration
antonypamo/savantorganized
— training corpus
ProSavantEngine Resonance Space
— live interactive demo
📜 Citation
@software{padilla2025prosavantengine,
author = {Padilla Morales, Antony},
title = {ProSavantEngine Φ9.3 — Icosahedral Resonant Language Model},
year = {2025},
url = {https://huggingface.co/antonypamo/ProSavantEngine_Phi9_3}
}
⚙️ Developer Notes
Add your dataset card or a link to any .jsonl corpus used.
Include training_args.json for reproducibility.
The model supports multi-node resonance learning via [NODE_X] tokens.
Compatible with both CPU and GPU environments.