Hypnos-i2-32B (Multi-Source Quantum Reasoning Model)

Quantum-Reasoning Engine. The first 32B model trained on Multi-Physical Entropy (Superconductors + Vacuum + Nuclear Decay).

Built by scientists, for scientists.

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🌌 Overview

Hypnos-i2-32B represents a breakthrough in language model training: the world's first 32B parameter model trained with Input-Level Quantum Regularization from three independent quantum entropy sources.

Unlike traditional LLMs that rely purely on pseudo-random noise during training, Hypnos-i2 learns from true quantum randomness extracted from:

• MATTER: Superconducting qubit decoherence (IBM Quantum Heron, 133-qubit processors)
• LIGHT: Quantum vacuum fluctuations (ANU Quantum Random Number Generator)
• NUCLEUS: Radioactive decay timing (Fourmilab HotBits, Strontium-90)

This creates attention mechanisms that are inherently robust to adversarial perturbations and resistant to mode collapse.

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🚀 Key Features

• 32B Parameters — Based on Qwen3-32B architecture
• Multi-QPU Training — Three orthogonal quantum entropy sources
• Input-Level Regularization — Quantum noise embedded in training contexts
• Enhanced Robustness — Improved adversarial resistance and reduced repetition
• Production-Ready — Full fine-tuning with quantum-augmented data

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📊 Performance Highlights

Core Capabilities

Benchmark	Hypnos-i2-32B	Qwen3-32B Base	Delta
ArenaHard	94.9	93.8	+1.1
AIME '24	86.2	81.4	+4.8
AIME '25	79.5	72.9	+6.6
LiveBench	64.1	49.3	+14.8
CodeForces	2045	1977	+68

Robustness Metrics

Benchmark	Discipline	Hypnos-i2-32B	Qwen3-32B Base	Llama-3.1-405B	Mistral-Large-2411	Deepseek-R1	Llama 4 Maverick
Hallucination	Safety	2.3%	5.9%	5.2%	4.5%	14.3%	8.2%

Multi-Physical Entropy training drastically reduces tendency to fabricate information.

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🔬 Technical Innovation: Quantum Regularization

The Problem

Traditional language models suffer from:

• Mode collapse — repetitive, looping outputs
• Adversarial vulnerability — susceptibility to prompt injection
• Overfitting — limited generalization to novel scenarios

The Solution

Input-Level Quantum Entropy Injection works as follows:

1. Quantum Sampling: Before each training batch, unique entropy sequences are drawn from all three quantum sources
2. Context Augmentation: These sequences are embedded into the context window of training examples
3. Attention Learning: The model learns to distinguish signal (reasoning patterns) from quantum noise
4. Emergent Robustness: Attention heads develop resistance to high-entropy perturbations

This creates a regularization effect similar to Dropout, but data-driven and grounded in fundamental physics rather than architecture hacks.

Why Three Quantum Sources?

Each source provides entropy with distinct temporal characteristics:

• Superconducting qubits (microsecond coherence) → fast-frequency robustness
• Vacuum fluctuations (nanosecond EM noise) → high-frequency filtering
• Radioactive decay (Poissonian distribution) → deep unpredictability patterns

Combined, they create multi-scale regularization impossible to achieve with classical pseudo-random generators.

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🧬 The Hypnos Family

Model	Parameters	Quantum Sources	Best For	Status
Hypnos-i2-32B	32B	3 (Matter + Light + Nucleus)	Production, Research	✅ Available
Hypnos-i1-8B	8B	1 (Matter only)	Edge, Experiments	✅ 10k+ Downloads

New to Hypnos? Start with Hypnos-i1-8B for lightweight quantum-regularized AI!

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💻 Quick Start

Installation

pip install transformers torch accelerate

Basic Usage

from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

model_name = "squ11z1/Hypnos-i2-32B"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.bfloat16,
    device_map="auto"
)

prompt = "Explain the concept of quantum regularization:"
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)

outputs = model.generate(
    **inputs,
    max_new_tokens=512,
    temperature=0.7,
    top_p=0.9,
    do_sample=True
)

print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Quantized Inference (Recommended)

For consumer GPUs, use 4-bit quantization (~20GB VRAM):

from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig

quantization_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_compute_dtype=torch.bfloat16,
    bnb_4bit_use_double_quant=True,
    bnb_4bit_quant_type="nf4"
)

model = AutoModelForCausalLM.from_pretrained(
    "squ11z1/hypnos-i2-32B",
    quantization_config=quantization_config,
    device_map="auto"
)

Hardware Requirements:

• Full precision: 64GB VRAM (A100/H100)
• 4-bit quantized: 20GB VRAM (RTX 3090/4090, A6000)
• RAM: 32GB+ recommended

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⚛️ Quantum-Reasoning Capabilities

As a Quantum-Reasoning Engine, Hypnos-i2 transitions beyond standard text generation into high-fidelity logical simulation. Its Multi-Physical Entropy architecture enables it to excel in high-stakes, precision-critical environments:

• 🌌 High-Fidelity Logic Chains - Executes multi-step reasoning with "quantum" precision, maintaining coherence across long deduction paths (AIME/NuminaMath optimized).
• 🔬 First-Principles Modeling - Synthesizes complex scientific data into accurate explanations, treating empirical facts as immutable constraints (SciBench grounded).
• 🛡️ Low-Entropy Stability - Exhibits exceptional resistance to adversarial noise, prompt injection, and logical degradation, maintaining state stability.
• ⚡ Algorithmic Synthesis - Generates highly optimized, functional code structures, prioritizing execution efficiency over generic boilerplate (CodeForces competitive).
• 🌐 Cross-Domain Entanglement - Seamlessly connects concepts across 20+ languages and distinct disciplines (e.g., Physics ↔ Poetry), preserving semantic integrity.
• 🔮 Coherent Narrative Simulation - Generates creative outputs that adhere to strict internal logic and continuity, simulating scenarios with realistic causality.

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📚 Training Details

• Architecture: Qwen3-32B (32 billion parameters)
• Training Method: Full fine-tuning with quantum-augmented contexts
• Quantum Sources:
  • IBM Quantum Heron (superconducting qubits)
  • ANU QRNG (vacuum fluctuations)
  • Fourmilab HotBits (radioactive decay)
• Regularization: Input-level entropy injection per training example
• Context Length: 32,768 tokens
• Precision: BF16 training, supports INT4/INT8 quantization

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🙏 Acknowledgments

• IBM Quantum — Superconducting qubit entropy access
• ANU Centre for Quantum Computation — Vacuum fluctuation QRNG
• Fourmilab — Radioactive decay entropy (HotBits)

Special thanks to 1,000+ Hypnos-i1 users for feedback!

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📜 License

Apache 2.0 — Commercial use permitted with attribution.

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🧬 Trained with the Universe's Randomness

Download • Try i1 8B