squ11z1/Hypnos-i1-8B
Hypnos i1-8B by squ11z1 is a specialized 8 billion parameter reasoning model based on Nous Hermes 3 (Llama 3.1 8B) designed for complex logic, chain-of-thought reasoning, and mathematical problem-solving. It features a unique Hybrid Quantum-Classical Machine Learning approach, trained with real entropy data from IBM Quantum Heron processors to enhance creativity and break deterministic patterns. This model excels in S-tier reasoning, outperforming standard 8B models in specific logic and math tasks, and is optimized for long-context reasoning.
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Hypnos i1-8B: Quantum-Informed Reasoning Model
Hypnos i1-8B, developed by squ11z1, is a specialized 8 billion parameter reasoning model built upon the Nous Hermes 3 (Llama 3.1 8B) architecture. It stands out due to its novel Hybrid Quantum-Classical Machine Learning approach, where it was fine-tuned on a dataset enriched with real entropy data generated by IBM Quantum Heron processors. This "Quantum Noise Injection" acts as a stochastic regularizer, aiming to improve the model's creativity and prevent deterministic generation patterns.
Key Capabilities
- S-Tier Reasoning: Demonstrates strong performance in complex logic, chain-of-thought reasoning, and mathematical problem-solving, rivaling 70B class models in specific narrow tasks.
- Quantum-Informed Training: Utilizes raw measurement data from 100+ qubit GHZ states generated on IBM's latest quantum hardware, making it the first known LLM fine-tuned with such data.
- Uncensored & Compliant: Based on Nous Hermes 3, it follows instructions without refusal while maintaining general safety.
- Deep Thinker: Optimized for long-context reasoning (4096+ tokens), often engaging in "thinking out loud" to ensure higher accuracy on complex queries.
Training Methodology
The model employs Data-Driven Stochastic Regularization via Quantum Entropy. During Supervised Fine-Tuning (SFT), it was exposed to raw bitstring measurements from entangled quantum states (GHZ) generated by IBM Quantum Heron r2 (156 Qubits) and r1 (133 Qubits) processors. This injection of high-entropy quantum data is theorized to make the model less prone to "mode collapse" and exhibit unique "temperature" in creative tasks.
Good For
- Complex Logic & Math: Ideal for tasks requiring multi-step logic puzzles and causal inference.
- Creative Generation: Its quantum-informed training may lead to less deterministic and more creative outputs.
- Edge & Prototyping: As an 8B model, it is suitable for deployment on laptops and for rapid experimentation.