Hypnos-i2-32B (Multi-Source Quantum Reasoning Model)
Hypnos-i2
Quantum-Reasoning Engine.
The first 32B model trained on Multi-Physical Entropy (Superconductors + Vacuum + Nuclear Decay).
Built by scientists, for scientists.
🌌 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.
🚀 Key Features
32B Parameters — Based on Qwen3-32B architectureMulti-QPU Training — Three orthogonal quantum entropy sourcesInput-Level Regularization — Quantum noise embedded in training contextsEnhanced Robustness — Improved adversarial resistance and reduced repetitionProduction-Ready — Full fine-tuning with quantum-augmented data
📊 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.
🔬 Technical Innovation: Quantum Regularization
The Problem
Traditional language models suffer from:
Mode collapse — repetitive, looping outputsAdversarial vulnerability — susceptibility to prompt injectionOverfitting — limited generalization to novel scenarios
The Solution
Input-Level Quantum Entropy Injection works as follows:
Quantum Sampling : Before each training batch, unique entropy sequences are drawn from all three quantum sourcesContext Augmentation : These sequences are embedded into the context window of training examplesAttention Learning : The model learns to distinguish signal (reasoning patterns) from quantum noiseEmergent 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 robustnessVacuum fluctuations (nanosecond EM noise) → high-frequency filteringRadioactive decay (Poissonian distribution) → deep unpredictability patterns
Combined, they create multi-scale regularization impossible to achieve with classical pseudo-random generators.
🧬 The Hypnos Family
Model Parameters Quantum Sources Best For Status Hypnos-Colossus-1T 1T (MoE) 3 (IBM + IQM + Cosmic) Deep Simulation, Grand Challenges 🌌 Flagship Hypnos-i2-32B 32B 3 (Matter + Light + Nucleus) Production, Research ✅ Stable Hypnos-i1-8B 8B 1 (Matter only) Edge, Experiments ✅ 10k+ Downloads
Which one to choose?
Colossus 1T: For when you need maximum reasoning depth.i2-32B: The "Giant Killer" - best balance of logic and efficiency for consumer GPUs.i1-8B: Perfect for laptops and rapid prototyping.
💻 Quick Start
Installation
pip install transformers torch accelerateBasic Usage
1 from transformers import AutoTokenizer , AutoModelForCausalLM
2 import torch
3
4 model_name = "squ11z1/Hypnos-i2-32B"
5 tokenizer = AutoTokenizer . from_pretrained ( model_name )
6 model = AutoModelForCausalLM . from_pretrained (
7 model_name ,
8 torch_dtype = torch . bfloat16 ,
9 device_map = "auto"
10 )
11
12 prompt = "Explain the concept of quantum regularization:"
13 inputs = tokenizer ( prompt , return_tensors = "pt" ) . to ( model . device )
14
15 outputs = model . generate (
16 ** inputs ,
17 max_new_tokens = 512 ,
18 temperature = 0.7 ,
19 top_p = 0.9 ,
20 do_sample = True
21 )
22
23 print ( tokenizer . decode ( outputs [ 0 ] , skip_special_tokens = True ) )
Quantized Inference (Recommended)
For consumer GPUs, use 4-bit quantization (~20GB VRAM):
1 from transformers import AutoTokenizer , AutoModelForCausalLM , BitsAndBytesConfig
2
3 quantization_config = BitsAndBytesConfig (
4 load_in_4bit = True ,
5 bnb_4bit_compute_dtype = torch . bfloat16 ,
6 bnb_4bit_use_double_quant = True ,
7 bnb_4bit_quant_type = "nf4"
8 )
9
10 model = AutoModelForCausalLM . from_pretrained (
11 "squ11z1/hypnos-i2-32B" ,
12 quantization_config = quantization_config ,
13 device_map = "auto"
14 )
Hardware Requirements:
Full precision : 64GB VRAM (A100/H100)4-bit quantized : 20GB VRAM (RTX 3090/4090, A6000)RAM : 32GB+ recommended
⚛️ 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.
📚 Training Details
Architecture : Qwen3-32B (32 billion parameters)Training Method : Full fine-tuning with quantum-augmented contextsQuantum Sources :
IBM Quantum Heron (superconducting qubits)
ANU QRNG (vacuum fluctuations)
Fourmilab HotBits (radioactive decay)
Regularization : Input-level entropy injection per training exampleContext Length : 32,768 tokensPrecision : BF16 training, supports INT4/INT8 quantization
🙏 Acknowledgments
IBM Quantum — Superconducting qubit entropy accessANU Centre for Quantum Computation — Vacuum fluctuation QRNGFourmilab — Radioactive decay entropy (HotBits)
Special thanks to 1,000+ Hypnos-i1 users for feedback!
📜 License
Apache 2.0 — Commercial use permitted with attribution.
🧬 Trained with the Universe's Randomness
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