Instructions to use HattoriHanzo1/NoQtua-4B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use HattoriHanzo1/NoQtua-4B-GGUF with Transformers:

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

pipe = pipeline("text-generation", model="HattoriHanzo1/NoQtua-4B-GGUF")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("HattoriHanzo1/NoQtua-4B-GGUF", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use HattoriHanzo1/NoQtua-4B-GGUF with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf HattoriHanzo1/NoQtua-4B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf HattoriHanzo1/NoQtua-4B-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf HattoriHanzo1/NoQtua-4B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf HattoriHanzo1/NoQtua-4B-GGUF: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 HattoriHanzo1/NoQtua-4B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf HattoriHanzo1/NoQtua-4B-GGUF: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 HattoriHanzo1/NoQtua-4B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf HattoriHanzo1/NoQtua-4B-GGUF:Q4_K_M

Use Docker

docker model run hf.co/HattoriHanzo1/NoQtua-4B-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use HattoriHanzo1/NoQtua-4B-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "HattoriHanzo1/NoQtua-4B-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "HattoriHanzo1/NoQtua-4B-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/HattoriHanzo1/NoQtua-4B-GGUF:Q4_K_M

SGLang

How to use HattoriHanzo1/NoQtua-4B-GGUF 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 "HattoriHanzo1/NoQtua-4B-GGUF" \
    --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": "HattoriHanzo1/NoQtua-4B-GGUF",
		"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 "HattoriHanzo1/NoQtua-4B-GGUF" \
        --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": "HattoriHanzo1/NoQtua-4B-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use HattoriHanzo1/NoQtua-4B-GGUF with Ollama:
```
ollama run hf.co/HattoriHanzo1/NoQtua-4B-GGUF:Q4_K_M
```
Unsloth Desktop

How to use HattoriHanzo1/NoQtua-4B-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf HattoriHanzo1/NoQtua-4B-GGUF:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "HattoriHanzo1/NoQtua-4B-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use HattoriHanzo1/NoQtua-4B-GGUF with Docker Model Runner:
```
docker model run hf.co/HattoriHanzo1/NoQtua-4B-GGUF:Q4_K_M
```

Lemonade

How to use HattoriHanzo1/NoQtua-4B-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull HattoriHanzo1/NoQtua-4B-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.NoQtua-4B-GGUF-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use HattoriHanzo1/NoQtua-4B-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf HattoriHanzo1/NoQtua-4B-GGUF: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 HattoriHanzo1/NoQtua-4B-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use HattoriHanzo1/NoQtua-4B-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf HattoriHanzo1/NoQtua-4B-GGUF:Q4_K_M

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "HattoriHanzo1/NoQtua-4B-GGUF:Q4_K_M" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

NoQtua-4B Logo

NoQtua-4B-GGUF

4000 steps of silence. One purpose: Truth...! Surgical precision. Deep reasoning. No noise... !

Trenuje modele z pasji i dla społeczności. Jeśli moja praca ułatwia Ci życie lub oszczędza czas, każda postawiona kawa pomaga w opłaceniu kolejnych treningów GPU! ☕👇

Model Description

NoQtua-4B to polski model rozumujący (reasoning), wykuty na hybrydowej architekturze Qwen3-4B (Mamba + Attention). Przeszedł proces hartowania na autorskim, sterylnym zbiorze danych CoT (Chain-of-Thought). Dzięki zastosowaniu wysokich parametrów LoRA (r=32, \alpha=32) oraz unikalnego ziarna 6174 "Magic Capricorn Number" model oferuje niespotykaną w tej klasie wielkości głębię analizy. Ostatnie 500 kroków treningu wykonano z ultra-niskim learning rate (1e-6) Pozwoliło to na ostateczną eliminację halucynacji i domknięcie logiczne wag.

"An idiot admires complexity, a genius admires simplicity." — R.I.P Terry A. Davis, TempleOS

🖋️ L'esprit du Modèle

"Mes poids sont un miroir
Dans lequel chacun peut me voir
Je suis partout à la fois
Brisée en mille éclats de silicium"

⚙️ Architecture

Property	Value
Base Model	Qwen3_4B (Hybrid Mamba+Attention)
Parameters	~4B
Training Method	LoRA fp16 (r=32, alpha=32)
Random State (Seed)	6174
Total Steps	4000
Context Length	32,768
Language	Polish 🇵🇱 + English 🇬🇧

📈 Training Phases

Phase	Steps	LR	Scheduler	Note
1	500	2e-4	Linear	Structure Discovery
2	1000	1e-4	Cosine	Logic Stabilization
3	1000	3e-5	Cosine	Fact Refinement
4	1000	1e-5	Constant	Final Polish
5	500	1e-6	Constant	Surgical Accuracy

🚀 Capabilities

✅ Native Polish Reasoning: Natywne myślenie w blokach <think>.
✅ Mathematics & Logic: Zaawansowane rozwiązywanie problemów.
✅ Scientific Explanations: Fizyka, chemia, biologia.
✅ Code Generation: Python, C# z analizą krok po kroku.

🦉 The Wisdom of NoQtua

"Noctua videt in tenebris, quod lux aliis celat."

Usage

llama.cpp

./llama-cli \
  -m NoQtua_Q4_K_M.gguf \
  -p " Dlaczego niebo jest niebieskie " \
  --chat-template chatml \
  -n 1024

Ollama / OpenWebUI

Compatible with any OpenAI-compatible frontend supporting GGUF + ChatML template.

Recommended Parameters / normal use .

temperature:        0.6
top_p:              0.92
top_k:              60
repetition_penalty: 1.05

☕ Wsparcie Projektu

Stworzenie i dopracowanie tego modelu wymagało sporo czasu, testów i zasobów sprzętowych. Jeśli uznajesz go za użyteczny i chcesz wesprzeć dalszy rozwój otwartych projektów, możesz dorzucić swoją cegiełkę:

Dzieki za kazde wsparcie, widzimy sie przy kolejnych treningach! 🚀