---
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:

```bash
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.