Instructions to use squ11z1/Hypnos-i1-8B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use squ11z1/Hypnos-i1-8B with Transformers:

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

pipe = pipeline("text-generation", model="squ11z1/Hypnos-i1-8B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("squ11z1/Hypnos-i1-8B")
model = AutoModelForCausalLM.from_pretrained("squ11z1/Hypnos-i1-8B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

llama-cpp-python

How to use squ11z1/Hypnos-i1-8B with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="squ11z1/Hypnos-i1-8B",
	filename="hypnos-i1-8B-Q4_K_M.gguf",
)

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

Inference
Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use squ11z1/Hypnos-i1-8B 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-i1-8B:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf squ11z1/Hypnos-i1-8B:Q4_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-i1-8B:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf squ11z1/Hypnos-i1-8B:Q4_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-i1-8B:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf squ11z1/Hypnos-i1-8B:Q4_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-i1-8B:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf squ11z1/Hypnos-i1-8B:Q4_K_M

Use Docker

docker model run hf.co/squ11z1/Hypnos-i1-8B:Q4_K_M

LM Studio
Jan

vLLM

How to use squ11z1/Hypnos-i1-8B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "squ11z1/Hypnos-i1-8B"
# 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-i1-8B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/squ11z1/Hypnos-i1-8B:Q4_K_M

SGLang

How to use squ11z1/Hypnos-i1-8B 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-i1-8B" \
    --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-i1-8B",
		"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-i1-8B" \
        --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-i1-8B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use squ11z1/Hypnos-i1-8B with Ollama:
```
ollama run hf.co/squ11z1/Hypnos-i1-8B:Q4_K_M
```

Unsloth Studio new

How to use squ11z1/Hypnos-i1-8B 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-i1-8B 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-i1-8B 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-i1-8B to start chatting

Docker Model Runner
How to use squ11z1/Hypnos-i1-8B with Docker Model Runner:
```
docker model run hf.co/squ11z1/Hypnos-i1-8B:Q4_K_M
```

Lemonade

How to use squ11z1/Hypnos-i1-8B with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull squ11z1/Hypnos-i1-8B:Q4_K_M

Run and chat with the model

lemonade run user.Hypnos-i1-8B-Q4_K_M

List all available models

lemonade list

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

🌌 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

🧬 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.