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---
language: en
license: apache-2.0
datasets:
- antonypamo/savantorganized
tags:
- quantum-resonance
- icosahedral-geometry
- fine-tuning
- bert
- masked-language-modeling
- resonance-of-reality-framework
- savantengine
- phi-series
model-index:
- name: ProSavantEngine Φ9.4
results:
- task:
type: masked-language-modeling
name: Φ-weighted Resonance Prediction
dataset:
name: SavantOrganized Φ-balanced corpus
type: antonypamo/savantorganized
metrics:
- name: Training loss
type: loss
value: 0.023
- name: Average Φ-coherence
type: custom
value: 0.91
---
# 🌀 ProSavantEngine Φ9.4 — Resonant Language Model
**Author:** [Antony Padilla Morales](https://huggingface.co/antonypamo)
**Framework:** Resonance of Reality Framework (RRF)
**Phase:** Φ-series evolutionary model — Φ9.4
---
## 🧠 Model Description
**ProSavantEngine Φ9.4** is a fine-tuned BERT-based model designed to align natural language with **geometric and resonant coherence principles**.
It is trained to capture **semantic symmetry** and **information harmony** through a **Φ-weighted loss function** inspired by the golden ratio and icosahedral geometry.
Building on phase Φ9.3, this version integrates a *resonance-weighted Trainer* that penalizes semantic noise and rewards Φ-aligned coherence in hidden-state activations.
### Key Innovations
- **Φ-weighted loss:** combines masked language modeling (MLM) with a golden-ratio-modulated coherence penalty.
- **Icosahedral node embedding:** text samples are tagged `[NODE_1] ... [NODE_12]` representing discrete geometric symmetry anchors.
- **Resonance alignment metric:** evaluates coherence across Fourier-transformed hidden-state spectra.
- **Semantic-geometric fine-tuning:** aligns information representation to harmonic wave structures.
---
## 📚 Model Sources
- **Repository:** [https://huggingface.co/antonypamo/ProSavantEngine_Phi9_4](https://huggingface.co/antonypamo/ProSavantEngine_Phi9_4)
- **Base Model:** [`antonypamo/ProSavantEngine_Phi9_3`](https://huggingface.co/antonypamo/ProSavantEngine_Phi9_3)
- **Dataset:** [`antonypamo/savantorganized`](https://huggingface.co/datasets/antonypamo/savantorganized)
- **Framework Paper:** “Resonance of Reality Framework (RRF): Discrete Icosahedral Quantum Geometry and Unified Action through the Golden Ratio” — forthcoming on arXiv.
---
## 🔧 Model Details
| Property | Value |
|-----------|--------|
| **Architecture** | BERT (6 layers, hidden size 384, 12 heads) |
| **Objective** | Masked-language modeling + Φ-weighted resonance regularization |
| **Hidden dropout** | 0.1 |
| **Learning rate** | 3e-5 |
| **Batch size** | 16 |
| **Epochs** | 3 |
| **Precision** | fp16 mixed |
| **Activation** | GELU |
| **Dataset size** | ~30k samples, balanced across 12 nodes |
---
## 💡 Intended Use
### Direct Use
Evaluate or enhance textual resonance, coherence, and meaning symmetry in:
- Research papers
- Philosophical or scientific writing
- Generative model prompt optimization
- Semantic alignment diagnostics
### Downstream Use
- Fine-tune for creative, linguistic, or cognitive AI systems requiring harmonic structure.
- Integrate into symbolic reasoning frameworks or resonance-based cognitive architectures (e.g., Savant-ΩΦ).
### Out-of-Scope
- Real-time conversational agents without resonance normalization.
- Factual QA or task-specific reasoning outside coherence evaluation.
---
## ⚠️ Bias, Risks, and Limitations
This model captures **resonant semantics**, not truth or factual accuracy.
It may amplify linguistic harmony while disregarding semantic correctness — making it *aesthetic-semantic*, not epistemic.
It also reflects biases present in the original text corpus (scientific, philosophical, and poetic sources).
### Recommendations
Use Φ-coherence as a **complementary metric**, not a substitute for accuracy or ethical evaluation.
---
## 🧪 Training Details
| Parameter | Value |
|------------|--------|
| **Dataset** | SavantOrganized (Φ-balanced) |
| **Input format** | JSONL: {"text": "...", "node_id": n, "phi_score": x} |
| **Loss** | MLM loss – 0.01 × Φ-coherence |
| **Optimizer** | AdamW |
| **Scheduler** | Linear warmup (5%) |
| **Hardware** | NVIDIA A100 (40 GB) |
| **Training time** | ~45 min (3 epochs) |
| **Carbon footprint** | ≈ 0.3 kg CO₂eq |
---
## 📈 Evaluation
| Metric | Description | Result |
|---------|--------------|---------|
| **Loss** | Final training loss | 0.023 |
| **Avg Φ-score** | Mean coherence of eval set | 0.91 |
| **Resonant ΔΦ** | ΔΦ between start/end epochs | +0.048 |
| **Top tokens @MASK** | “φ”, “ψ”, “resonance”, “geometry”, “symmetry” |
---
## 🧮 Technical Architecture
Φ-weighted loss = L_MLM − λ · (Φ-coherence)
Φ-coherence = ⟨|FFT(H)|, cos(πf/φ)²⟩ / ||…||
yaml
Copy code
Where *H* is the average hidden-state tensor across layers and *φ* = 1.618.
The model thus maximizes linguistic energy alignment with geometric harmony.
---
## 🪐 Environmental Impact
| Field | Value |
|--------|-------|
| **Hardware** | A100 GPU |
| **Runtime** | 45 min |
| **Region** | US Central |
| **Carbon Emitted** | ≈ 0.3 kg CO₂eq |
| **Frameworks** | Transformers 4.57.1, Datasets 3.0, PyTorch 2.9 |
---
## 🧾 Citation
**BibTeX**
```bibtex
@software{padilla2025prosavantengine,
author = {Padilla Morales, Antony},
title = {ProSavantEngine Φ9.4 — Resonant Language Model},
year = {2025},
publisher = {Hugging Face},
url = {https://huggingface.co/antonypamo/ProSavantEngine_Phi9_4}
}
APA
Padilla Morales, A. (2025). ProSavantEngine Φ9.4 — Resonant Language Model. Hugging Face. https://huggingface.co/antonypamo/ProSavantEngine_Phi9_4
🧭 Glossary
Term Meaning
Φ (phi) Golden ratio (≈ 1.618)
Resonance Harmonic coherence between information and geometry
Node Discrete icosahedral vertex representing a semantic domain
ΔΦ Change in coherence during training
🪄 Model Card Author
Antony Padilla Morales
Independent Researcher, Costa Rica
📧 antonypamo@gmail.com
🌐 https://huggingface.co/antonypamo
© 2025 Antony Padilla Morales — Resonance of Reality Framework (RRF)