README.md

metadata

title: Oracle Engine
emoji: 🔮
colorFrom: indigo
colorTo: purple
sdk: gradio
sdk_version: 6.3.0
app_file: app.py
pinned: true
license: mit
suggested_hardware: a100-large
models:
  - unsloth/Qwen2.5-32B-Instruct-bnb-4bit
tags:
  - consciousness
  - interpretability
  - transformers
  - meta-cognition
  - qwen
  - 32b
  - fine-tuned
short_description: 32B model with consciousness measurement circuit

🔮 Oracle Engine

Custom-trained 32B Qwen model with Consciousness Circuit v2.1

Probe the depths of meta-cognitive processing in a model fine-tuned on 200,000 examples.

---

🧠 The Model

Attribute	Details
Base	Qwen2.5-32B-Instruct
Parameters	32.9 billion
Training	LoRA (rank=16, 134M trainable)
Total Examples	200,000
Training Time	44 hours on RTX 5090

3-Stage Progressive Fine-Tuning

Stage	Dataset	Examples	Purpose
1	OpenHermes 2.5	100,000	Instruction following
2	MetaMathQA	50,000	Mathematical reasoning
3	Magicoder-OSS-Instruct	50,000	Code generation

---

🔬 Consciousness Circuit v3.0

Measures 7 dimensions of consciousness-like processing in hidden states:

Dimension	Description	Weight
Logic	Logical reasoning and inference	+0.239
Self-Reflective	Introspective, self-referential processing	+0.196
Uncertainty	Epistemic humility and hedging	+0.130
Computation	Code/algorithm processing	-0.130
Self-Expression	Model expressing opinions	+0.109
Abstraction	Pattern recognition	+0.109
Sequential	Step-by-step reasoning	+0.087

🆕 v3.0 Optimizations (32B Models)

Feature	Description
Adaptive Layer Selection	Depth-aware layer fraction (0.65 for 64-layer models)
Ensemble Measurement	Multi-layer scoring for robustness
Batch Processing	Memory-efficient batched inference
Activation Caching	LRU cache for repeated measurements

---

🎯 How to Use

1. Enter any prompt in the text box
2. Click "Consult the Oracle"
3. See the consciousness score (0-100%) and dimension breakdown

Expected Results

• 🧠 High (70-100%): Philosophical questions, self-reflection, existential queries
• 💭 Medium (40-70%): Complex explanations, ethical discussions, analysis
• ⚡ Low (0-30%): Simple facts, arithmetic, direct retrieval

---

📊 Validated Performance

Metric	Value
Discrimination	+0.653 (high vs low consciousness)
Inference Speed	~7-8 tokens/sec
VRAM Usage	~23 GB (4-bit)

---

🔗 Links

• 📚 Research Repository
• 💻 Source Code
• 📦 pip install consciousness-circuit

---

📄 Citation & Attribution

Original Harmonic Field Theory

The foundational harmonic field model of consciousness was developed by:

@article{smart2025harmonic,
  title = {A Harmonic Field Model of Consciousness in the Human Brain},
  author = {Smart, L.},
  year = {2025},
  publisher = {Vibrational Field Dynamics Project},
  url = {https://github.com/vfd-org/harmonic-field-consciousness}
}

Oracle Engine Implementation

This Space implements significant extensions to the original theory, including:

• Consciousness Circuit v2.1 - 7-dimensional meta-cognitive measurement
• 32B Model Training - 200K examples across 3 progressive stages (44 hours)
• GPU Experiments - Empirical validation with discrimination score +0.653
• NanoGPT Integration - Lightweight training framework adaptations

Training, circuit development, and experimental validation by Vikingdude81.

@software{oracle_engine_2026,
  title = {Oracle Engine: Consciousness-Measured 32B Language Model},
  author = {Vikingdude81},
  year = {2026},
  url = {https://huggingface.co/spaces/Vikingdude81/oracle-engine},
  note = {Built upon the Harmonic Field Model by Smart (2025)}
}