README.md

---
license: other
license_name: modified-mit
library_name: transformers
base_model:
- moonshotai/Kimi-K2-Thinking
pipeline_tag: text-generation
tags:
- quantum
- reasoning
- physics
- entropy-injection
---

# Hypnos-Colossus 1T (Quantum-Informed Reasoning)

<div align="center">
    <img src="https://cdn-uploads.huggingface.co/production/uploads/67329d3f69fded92d56ab41a/5D4f3nXi4caZu2Q4_YAAK.jpeg" width="100%" alt="Hypnos Colossus Header">
</div>
<div align="center">
    The Largest Quantum-Regularized Model in Existence.
</div>

**🪐 Overview**

**Hypnos-Colossus 1T** is a massive-scale reasoning engine derived from the [Kimi-K2-Thinking](https://huggingface.co/moonshotai/Kimi-K2-Thinking) architecture. It represents a radical experiment in Post-Training Weight Perturbation.

Instead of standard fine-tuning, we applied a Quantum Scale Injection protocol using real entropy data derived from three sources:

1. IBM Quantum Processors (Superconducting Qubit Decoherence).
2. IQM Quantum Processor (Superconducting Transmon Qubits with star topology).
3. Cosmic Microwave Background (CMB) data from the Planck satellite.

    <img src="https://cdn-uploads.huggingface.co/production/uploads/67329d3f69fded92d56ab41a/lYduUOLOljHUxF6iPvjzs.jpeg" width="60%" alt="Cosmic_Microwave_Background_(CMB)">
This process introduces a unique, non-deterministic "fingerprint" into the model's scaling tensors, aimed at breaking local minima overfitting and enforcing stricter logical adherence during inference.

📊 **Kimi-K2's Thinkings Model Summary & Reasoning Benchmarks**


| | |
|:---:|:---:|
| **Architecture** | Mixture-of-Experts (MoE) |
| **Total Parameters** | 1T |
| **Activated Parameters** | 32B |
| **Number of Layers** (Dense layer included) | 61 |
| **Number of Dense Layers** | 1 |
| **Attention Hidden Dimension** | 7168 |
| **MoE Hidden Dimension** (per Expert) | 2048 |
| **Number of Attention Heads** | 64 |
| **Number of Experts** | 384 |
| **Selected Experts per Token** | 8 |
| **Number of Shared Experts** | 1 |
| **Vocabulary Size** | 160K |
| **Context Length** | 256K |
| **Attention Mechanism** | MLA |
| **Activation Function** | SwiGLU |

**Reasoning Tasks**
| Benchmark | Setting | K2 Thinking | GPT-5<br> (High) | Claude Sonnet 4.5<br> (Thinking) | K2 0905 | DeepSeek-V3.2 | Grok-4 |
|:----------:|:--------:|:------------:|:------:|:----------------------------:|:--------:|:--------------:|:-------:|
| **HLE (Text-only)** | no tools | 23.9 | 26.3 | 19.8* | 7.9 | 19.8 | 25.4 |
| | w/ tools | 44.9 | 41.7* | 32.0* | 21.7 | 20.3* | 41.0 |
| | heavy | 51.0 | 42.0 | - | - | - | 50.7 |
| **AIME25** | no tools | 94.5 | 94.6 | 87.0 | 51.0 | 89.3 | 91.7 |
| | w/ python | 99.1 | 99.6 | 100.0 | 75.2 | 58.1* | 98.8 |
| | heavy | 100.0 | 100.0 | - | - | - | 100.0 |
| **HMMT25** | no tools | 89.4 | 93.3 | 74.6* | 38.8 | 83.6 | 90.0 |
| | w/ python | 95.1 | 96.7 | 88.8* | 70.4 | 49.5* | 93.9 |
| | heavy | 97.5 | 100.0 | - | - | - | 96.7 |
| **IMO-AnswerBench** | no tools | 78.6 | 76.0* | 65.9* | 45.8 | 76.0* | 73.1 |
| **GPQA** | no tools | 84.5 | 85.7 | 83.4 | 74.2 | 79.9 | 87.5 |

</div>

**Quantum Augmentation Specs**
Entropy Sources: IBM Quantum ibm_fez + IQM Sirius + Planck CMB Data

Injection Target: Scaling Tensors (Scales/Norms) via Direct Perturbation ($\epsilon=1e^{-5}$)

Format: Native INT4/FP8 Compressed 

<img src="https://cdn-uploads.huggingface.co/production/uploads/67329d3f69fded92d56ab41a/ii-jSyWx3KAXAi1j3ifVs.jpeg" width="70%" alt="qub">

**🔬 The "Quantum Injection" Hypothesis**

Standard quantization (INT4) often locks massive models into rigid behavioral patterns. 
By injecting high-quality quantum noise into the scales and norms of the model, we theoretically increase the model's epistemic uncertainty without degrading its knowledge base.
This forces the inference path to rely less on "memorized" token sequences and more on robust semantic links.

Source Data Integrity:
The noise injection was seeded using a cryptographically secure hash of the Planck CMB radiation map combined with raw qubit readouts from IBM's ibm_fez & IQM Sirius backends.

## 🧬 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.

**🚀 How to Run**

Inference with Transformers

```
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

model_id = "squ11z1/Hypnos-Colossus-1T"

tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
    model_id, 
    torch_dtype=torch.float16, 
    device_map="auto",
    trust_remote_code=True
)

prompt = "Analyze the implications of quantum entropy on AI reasoning:"
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")

output = model.generate(**inputs, max_new_tokens=512, temperature=0.6)
print(tokenizer.decode(output[0]))
```
---

<div align="center">

**🧬 The Largest Quantum-Regularized Model in Existence.**

<div align="center">
  <img src="https://cdn-uploads.huggingface.co/production/uploads/67329d3f69fded92d56ab41a/f7K5oDyo9dX7t72IlcKqb.jpeg" width="40%" alt="Hypnos Footer Image"/>
</div>

[Download](https://huggingface.co/squ11z1/Hypnos-Colossus-1T) • [Try i1 8B](https://huggingface.co/squ11z1/hypnos-i1-8b)

</div>