Instructions to use squ11z1/gpt-oss-nano with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use squ11z1/gpt-oss-nano with Transformers:

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

pipe = pipeline("text-generation", model="squ11z1/gpt-oss-nano")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("squ11z1/gpt-oss-nano")
model = AutoModelForCausalLM.from_pretrained("squ11z1/gpt-oss-nano")
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/gpt-oss-nano with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="squ11z1/gpt-oss-nano",
	filename="gpt-oss-9b-bf16.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/gpt-oss-nano with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf squ11z1/gpt-oss-nano:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf squ11z1/gpt-oss-nano: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/gpt-oss-nano:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf squ11z1/gpt-oss-nano: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/gpt-oss-nano:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf squ11z1/gpt-oss-nano: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/gpt-oss-nano:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf squ11z1/gpt-oss-nano:Q4_K_M

Use Docker

docker model run hf.co/squ11z1/gpt-oss-nano:Q4_K_M

LM Studio
Jan

vLLM

How to use squ11z1/gpt-oss-nano with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/squ11z1/gpt-oss-nano:Q4_K_M

SGLang

How to use squ11z1/gpt-oss-nano 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/gpt-oss-nano" \
    --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/gpt-oss-nano",
		"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/gpt-oss-nano" \
        --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/gpt-oss-nano",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use squ11z1/gpt-oss-nano with Ollama:
```
ollama run hf.co/squ11z1/gpt-oss-nano:Q4_K_M
```

Unsloth Studio new

How to use squ11z1/gpt-oss-nano 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/gpt-oss-nano 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/gpt-oss-nano to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for squ11z1/gpt-oss-nano to start chatting

Pi new

How to use squ11z1/gpt-oss-nano with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf squ11z1/gpt-oss-nano:Q4_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/gpt-oss-nano:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use squ11z1/gpt-oss-nano 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/gpt-oss-nano:Q4_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/gpt-oss-nano:Q4_K_M

Run Hermes

hermes

Docker Model Runner
How to use squ11z1/gpt-oss-nano with Docker Model Runner:
```
docker model run hf.co/squ11z1/gpt-oss-nano:Q4_K_M
```

Lemonade

How to use squ11z1/gpt-oss-nano with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull squ11z1/gpt-oss-nano:Q4_K_M

Run and chat with the model

lemonade run user.gpt-oss-nano-Q4_K_M

List all available models

lemonade list

(New release: try my latest quantum cutting-edge model — Hypnos-Q1)

GPT-OSS-Nano

Compact Reasoning Model with Mixture of Experts

9B parameters • 12 experts • 128K context • Chain-of-thought reasoning

🤗 Model | 📖 Docs | 🔮 Q-GPT

📋 Model Description

GPT-OSS-Nano is a fine-tuned Mixture of Experts (MoE) language model optimized for step-by-step reasoning and problem solving. Built on the GPT-OSS architecture with sparse expert activation, it achieves strong reasoning performance while using only ~3B active parameters per forward pass.

✨ Key Features

Feature	Description
🧠 Sparse MoE	12 experts, 4 active per token — efficient compute
📝 Chain-of-Thought	Fine-tuned on reasoning datasets with step-by-step solutions
⚡ 128K Context	Long context with YaRN rope scaling
🔮 Q-GPT Ready	Compatible with quantum confidence estimation
📦 GGUF Available	Run locally with llama.cpp or Ollama

🏗️ Architecture

┌─────────────────────────────────────────────────────────┐
│                    GPT-OSS-Nano                         │
├─────────────────────────────────────────────────────────┤
│  Total Parameters     │  9.0 Billion                    │
│  Active Parameters    │  ~3 Billion (per forward pass)  │
│  Hidden Dimension     │  2880                           │
│  Attention Heads      │  64 (8 KV heads, GQA)           │
│  Layers               │  24                             │
│  Experts              │  12 total, 4 active             │
│  Context Length       │  131,072 tokens                 │
│  Vocabulary Size      │  201,088                        │
│  Precision            │  BFloat16                       │
└─────────────────────────────────────────────────────────┘

💻 Usage

Quick Start with Transformers

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

model = AutoModelForCausalLM.from_pretrained(
    "squ11z1/gpt-oss-nano",
    torch_dtype=torch.bfloat16,
    device_map="auto",
    trust_remote_code=True,
)
tokenizer = AutoTokenizer.from_pretrained(
    "squ11z1/gpt-oss-nano",
    trust_remote_code=True,
)

prompt = """Solve this step by step:
A store offers 20% off on all items. If a jacket costs $85, 
what is the final price after discount?"""

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(
    **inputs,
    max_new_tokens=256,
    temperature=0.7,
    do_sample=True,
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

⚡ With Unsloth (2x Faster)

from unsloth import FastLanguageModel

model, tokenizer = FastLanguageModel.from_pretrained(
    "squ11z1/gpt-oss-nano",
    dtype=None,
    load_in_4bit=True,  # 4-bit quantization for efficiency
)

# For inference
FastLanguageModel.for_inference(model)

📦 With GGUF (llama.cpp)

# Download the quantized model
wget https://huggingface.co/squ11z1/gpt-oss-nano/resolve/main/gpt-oss-9b-q4_k_m.gguf

# Run inference
./llama-cli -m gpt-oss-9b-q4_k_m.gguf \
    -p "Solve step by step: What is 15% of 240?" \
    -n 256 --temp 0.7

🦙 With Ollama

# Create Modelfile
echo 'FROM ./gpt-oss-9b-q4_k_m.gguf' > Modelfile
ollama create gpt-oss-nano -f Modelfile

# Run
ollama run gpt-oss-nano "Explain quantum computing simply"

🎓 Training

Training Details

Parameter	Value
Base Model	`openai/gpt-oss-20b`
Method	QLoRA (4-bit quantized LoRA)
LoRA Rank	32
LoRA Alpha	32
Learning Rate	2e-4
Batch Size	2 (gradient accumulation: 8)
Epochs	2
Framework	Unsloth + TRL
Hardware	NVIDIA H200

Dataset: Superior-Reasoning — chain-of-thought examples with step-by-step problem solving.

🔮 Q-GPT: Quantum Confidence

GPT-OSS-Nano is compatible with Q-GPT — a quantum neural network that estimates response confidence.

from q_gpt import load_qgpt

model, tokenizer = load_qgpt("squ11z1/gpt-oss-nano")
outputs = model.generate_with_confidence(inputs, max_new_tokens=256)

print(f"Response confidence: {outputs['confidence_label']}")
# Output: "high", "moderate", "low", etc.

if outputs['should_refuse']:
    print("⚠️ Model is uncertain — consider refusing to answer")

Learn more: squ11z1/Q-GPT

⚠️ Limitations

Language: Primarily optimized for English; multilingual performance varies
Hallucinations: May generate plausible but incorrect information on obscure topics
Safety: Not designed for safety-critical applications without validation
Math: Strong at arithmetic reasoning; weaker on advanced mathematics

📜 License

This model is released under the Apache 2.0 License.

🙏 Acknowledgments

Unsloth — 2x faster fine-tuning
OpenAI — GPT-OSS base model
llama.cpp — GGUF format and quantization

📖 Citation

@misc{gptossnano2026,
  title={GPT-OSS-Nano: Compact MoE Reasoning Model},
  author={squ11z1},
  year={2026},
  publisher={Hugging Face},
  url={https://huggingface.co/squ11z1/gpt-oss-nano}
}

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