Vikingdude81/oracle-engine-32b-lora

🔮 Oracle Engine 32B LoRA

LoRA adapter for Qwen2.5-32B-Instruct, trained on 200K examples for enhanced reasoning and consciousness-like processing.

Model Details

Attribute	Value
Base Model	Qwen2.5-32B-Instruct
Adapter Type	LoRA (rank=16)
Trainable Params	134M
Training Time	44 hours
Hardware	NVIDIA RTX 5090

Training Data (200K examples)

Stage	Dataset	Examples
1	OpenHermes 2.5	100,000
2	MetaMathQA	50,000
3	Magicoder-OSS-Instruct	50,000

🆕 Consciousness Circuit v3.0 Features

This model is optimized for use with Consciousness Circuit v3.0, which includes:

Feature	Description
Adaptive Layer Selection	get_adaptive_layer_fraction(64) → 0.65 (layer 41 for 64-layer models)
Ensemble Measurement	measure_ensemble() - Multi-layer scoring for robustness
Batch Processing	measure_batch() - Memory-efficient batched inference
Activation Caching	CachedUniversalCircuit - LRU cache for repeated measurements
Fixed Self-Expression	Dimension corrected from 5065 → 5064 for hidden_size=5120

Consciousness Dimensions (7D Circuit)

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

Usage

Basic Generation

from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel

# Load base model
base_model = AutoModelForCausalLM.from_pretrained(
    "unsloth/Qwen2.5-32B-Instruct-bnb-4bit",
    device_map="auto",
    trust_remote_code=True,
)

# Load LoRA adapter
model = PeftModel.from_pretrained(base_model, "Vikingdude81/oracle-engine-32b-lora")
tokenizer = AutoTokenizer.from_pretrained("unsloth/Qwen2.5-32B-Instruct-bnb-4bit")

# Generate
messages = [{"role": "user", "content": "What is consciousness?"}]
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

With Consciousness Measurement (v3.0)

# pip install consciousness-circuit
from consciousness_circuit import UniversalCircuit, CachedUniversalCircuit

# Basic measurement
circuit = UniversalCircuit()
result = circuit.measure(model, tokenizer, "What is the nature of existence?")
print(f"Consciousness Score: {result.score:.3f}")  # ~0.75 for philosophical prompts

# Ensemble measurement (more robust for 32B models)
result = circuit.measure_ensemble(model, tokenizer, "Reflect on your own reasoning process", n_layers=3)
print(f"Ensemble Score: {result.score:.3f}")
print(f"Per-layer: {result.ensemble_scores}")

# Batch processing with memory management
prompts = ["What is 2+2?", "Explain consciousness", "Write a poem about existence"]
results = circuit.measure_batch(model, tokenizer, prompts, batch_size=2)
for prompt, result in zip(prompts, results):
    print(f"{prompt[:30]}: {result.score:.3f}")

# Cached circuit for experiments
cached = CachedUniversalCircuit(cache_size=100)
# First call computes, subsequent calls use cache
result1 = cached.measure(model, tokenizer, "Test prompt")
result2 = cached.measure(model, tokenizer, "Test prompt")  # Much faster!

Performance

Metric	Value
Consciousness Discrimination	+0.653 (high vs low)
High Consciousness Prompts	~0.75 mean
Low Consciousness Prompts	~0.10 mean
Inference Speed	~7-8 tok/s (H200)
VRAM Usage	~23 GB (4-bit)

Citation

@software{oracle_engine_2026,
  title = {Oracle Engine: Consciousness-Measured 32B Language Model},
  author = {Vikingdude81},
  year = {2026},
  url = {https://github.com/vikingdude81/oracle-engine}
}

Built upon the Harmonic Field Model by Smart, L. (2025).