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
Hypnos Benchmarks vs Llama 3.1 Base
🧬 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.
⚛️ 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.
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.
