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squ11z1/
gpt-oss-nano

Name: gpt-oss-nano
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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

python

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)

python

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)

bash

# 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

bash

# 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.

python

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

bibtex

@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}
}

Pro Mundi Vita