ModelHub XC 0e8926cd4e 初始化项目,由ModelHub XC社区提供模型
Model: Yingyaeliae/Hypnos-i1-8B-heretic
Source: Original Platform
2026-07-18 22:44:13 +08:00

license, language, base_model, tags, pipeline_tag, library_name, datasets
license language base_model tags pipeline_tag library_name datasets
apache-2.0
en
NousResearch/Hermes-3-Llama-3.1-8B
reasoning
mathematics
logic
chain-of-thought
quantum
physics
llama-3
gguf
text-generation-inference
chatml
roleplaying
conversational
synthetic data
arxiv:2408.11857
heretic
uncensored
decensored
abliterated
reproducible
text-generation transformers
open-thoughts/OpenThoughts-114k
KingNish/reasoning-base-20k
nvidia/OpenMathReasoning
amphora/QwQ-LongCoT-130K
openai/gsm8k

This is a decensored version of adamm-hf/Hypnos-i1-8B, made using Heretic v1.4.0

Tip

This model is reproducible!

See the README in the reproduce directory for more information.

Abliteration parameters

Parameter Value
direction_index 19.33
attn.o_proj.max_weight 1.48
attn.o_proj.max_weight_position 20.62
attn.o_proj.min_weight 1.39
attn.o_proj.min_weight_distance 15.11
mlp.down_proj.max_weight 1.22
mlp.down_proj.max_weight_position 21.39
mlp.down_proj.min_weight 1.16
mlp.down_proj.min_weight_distance 13.56

Performance

Metric This model Original model (adamm-hf/Hypnos-i1-8B)
KL divergence 0.0431 0 (by definition)
Refusals 3/100 15/100

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

Hypnos Header Image

🌌 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

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.

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.


Hypnos Footer Image
Description
Model synced from source: Yingyaeliae/Hypnos-i1-8B-heretic
Readme 148 KiB
Languages
Jinja 100%