Hypnos-Colossus 1T (Quantum-Informed Reasoning)

The Largest Quantum-Regularized Model in Existence.

🪐 Overview

Hypnos-Colossus 1T is a massive-scale reasoning engine derived from the 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:

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

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 (High)	Claude Sonnet 4.5 (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

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

🔬 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]))

🧬 The Largest Quantum-Regularized Model in Existence.