--- title: Hypnos-i2-32B canonical_url: "https://www.modelscope.cn/models/AI-ModelScope/Hypnos-i2-32B" md_url: "https://www.modelscope.cn/models/AI-ModelScope/Hypnos-i2-32B.md" repository: AI-ModelScope/Hypnos-i2-32B last_updated: 2026-07-14 license: apache-2.0 pipeline_tag: text-generation tasks: - text-generation model_type: - qwen3 architectures: - Qwen3ForCausalLM base_model: - Qwen/Qwen3-32B base_model_relation: quantized library_name: - pytorch - transformer - gguf frameworks: - pytorch language: - multilingual downloads: 61 stars: 0 tags: - quantum - physics - reasoning - quantum-computing - quantum-regularization - multi-qpu - adversarial-robust - qwen3 - text-generation - gguf --- # Hypnos-i2-32B > Hypnos-i2-32B - AI-ModelScope 在 ModelScope 开源的模型。Hypnos-i2-32B (Multi-Source Quantum Reasoning Model) AI-ModelScope/Hypnos-i2-32B 是 ModelScope 魔搭社区上的text-generation模型，采用 apache-2.0 许可，基于 Qwen/Qwen3-32B 构建。 - **Repository**: AI-ModelScope/Hypnos-i2-32B - **License**: apache-2.0 - **Tasks**: text-generation - **Base model**: Qwen/Qwen3-32B - **Tags**: quantum, physics, reasoning, quantum-computing, quantum-regularization, multi-qpu, adversarial-robust, qwen3, text-generation, gguf - **Downloads**: 61 - **Stars**: 0 - **Last updated**: 2026-07-14 Source: https://www.modelscope.cn/models/AI-ModelScope/Hypnos-i2-32B --- # 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 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 --- ## 📊 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 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. --- ## 🧬 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 ```bash pip install transformers torch accelerate ``` ### Basic Usage ```python 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): ```python 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 --- ## ⚛️ 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 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 --- ## 🙏 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! --- ## 📜 License Apache 2.0 — Commercial use permitted with attribution. ---
**🧬 Trained with the Universe's Randomness**
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