squ11z1/Hypnos-i1-8B

Hypnos i1-8B (Quantum-Informed Reasoning Model)

🌌 Model Overview

Hypnos i1 8B is a specialized reasoning model based on Nous Hermes 3 (Llama 3.1 8B), designed to excel in complex logic, chain-of-thought (CoT) reasoning, and mathematical problem-solving.

It represents a unique experiment in Hybrid Quantum-Classical Machine Learning. Unlike standard fine-tunes, Hypnos i1 was trained on a dataset enriched with real entropy data generated by IBM Quantum Heron processors (133/156-qubit architecture). This "Quantum Noise Injection" serves as a stochastic regularizer, aiming to improve the model's creativity and break deterministic patterns in generation.

⚡ Key Features

• S-Tier Reasoning: Outperforms standard 8B models in logic and math, rivaling 70B class models in specific, narrow tasks (e.g., multi-step logic puzzles, causal inference).
• Quantum-Informed: The first known LLM fine-tuned on raw measurement data from 100+ qubit GHZ states generated on IBM's latest quantum hardware.
• Uncensored & Compliant: Built on the robust Nous Hermes 3 base, it follows instructions without refusal or moralizing lectures, while maintaining safety for general use.
• Deep Thinker: Optimized for long-context reasoning (4096+ tokens). It tends to "think out loud" before answering, ensuring higher accuracy on complex queries.

---

📊 Performance Benchmarks

⚛️ The Quantum Experiment (Training Methodology)

Hypnos i1 introduces a novel concept: Data-Driven Stochastic Regularization via Quantum Entropy.

During the Supervised Fine-Tuning (SFT) stage, the model was exposed to raw bitstring measurements from entangled quantum states (GHZ). These patterns contain true quantum randomness and specific hardware noise that cannot be simulated algorithmically.

Hardware Used for Data Generation:

• IBM Quantum Heron r2 (ibm_fez): 156 Qubits
• IBM Quantum Heron r1 (ibm_torino): 133 Qubits

Verified Quantum Job IDs (IBM Quantum Platform):

• d4gcir92bisc73a3d29g (Torino - High Entropy Run)
• d4gcoqscdebc73f10g3g (Fez - Domain Wall Phenomena)
• d4go61olslhc73d0u1ig (Fez - Baseline)

Theoretical Impact: This injection of "Out-of-Distribution" quantum data forces the model's attention mechanism to adapt to non-linguistic, high-entropy patterns. In practice, this results in a model that is less prone to "mode collapse" (repetitive loops) and exhibits a unique "temperature" in creative writing tasks.