Instructions to use squ11z1/Hypnos-Colossus-1T with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use squ11z1/Hypnos-Colossus-1T with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="squ11z1/Hypnos-Colossus-1T", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("squ11z1/Hypnos-Colossus-1T", trust_remote_code=True, dtype="auto")

llama-cpp-python

How to use squ11z1/Hypnos-Colossus-1T with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="squ11z1/Hypnos-Colossus-1T",
	filename="Q3_K_M/Kimi-K2-Thinking-Q3_K_M-00001-of-00011.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use squ11z1/Hypnos-Colossus-1T with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf squ11z1/Hypnos-Colossus-1T:Q3_K_M
# Run inference directly in the terminal:
llama-cli -hf squ11z1/Hypnos-Colossus-1T:Q3_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf squ11z1/Hypnos-Colossus-1T:Q3_K_M
# Run inference directly in the terminal:
llama-cli -hf squ11z1/Hypnos-Colossus-1T:Q3_K_M

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf squ11z1/Hypnos-Colossus-1T:Q3_K_M
# Run inference directly in the terminal:
./llama-cli -hf squ11z1/Hypnos-Colossus-1T:Q3_K_M

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf squ11z1/Hypnos-Colossus-1T:Q3_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf squ11z1/Hypnos-Colossus-1T:Q3_K_M

Use Docker

docker model run hf.co/squ11z1/Hypnos-Colossus-1T:Q3_K_M

LM Studio
Jan

vLLM

How to use squ11z1/Hypnos-Colossus-1T with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "squ11z1/Hypnos-Colossus-1T"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "squ11z1/Hypnos-Colossus-1T",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/squ11z1/Hypnos-Colossus-1T:Q3_K_M

SGLang

How to use squ11z1/Hypnos-Colossus-1T with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "squ11z1/Hypnos-Colossus-1T" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "squ11z1/Hypnos-Colossus-1T",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "squ11z1/Hypnos-Colossus-1T" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "squ11z1/Hypnos-Colossus-1T",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use squ11z1/Hypnos-Colossus-1T with Ollama:
```
ollama run hf.co/squ11z1/Hypnos-Colossus-1T:Q3_K_M
```

Unsloth Studio new

How to use squ11z1/Hypnos-Colossus-1T with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for squ11z1/Hypnos-Colossus-1T to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for squ11z1/Hypnos-Colossus-1T to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for squ11z1/Hypnos-Colossus-1T to start chatting

Pi new

How to use squ11z1/Hypnos-Colossus-1T with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf squ11z1/Hypnos-Colossus-1T:Q3_K_M

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "squ11z1/Hypnos-Colossus-1T:Q3_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use squ11z1/Hypnos-Colossus-1T with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf squ11z1/Hypnos-Colossus-1T:Q3_K_M

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default squ11z1/Hypnos-Colossus-1T:Q3_K_M

Run Hermes

hermes

Docker Model Runner
How to use squ11z1/Hypnos-Colossus-1T with Docker Model Runner:
```
docker model run hf.co/squ11z1/Hypnos-Colossus-1T:Q3_K_M
```

Lemonade

How to use squ11z1/Hypnos-Colossus-1T with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull squ11z1/Hypnos-Colossus-1T:Q3_K_M

Run and chat with the model

lemonade run user.Hypnos-Colossus-1T-Q3_K_M

List all available models

lemonade list

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.