Instructions to use muverqqw/Noir with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use muverqqw/Noir with Transformers:

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

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

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("muverqqw/Noir")
model = AutoModelForCausalLM.from_pretrained("muverqqw/Noir")
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]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use muverqqw/Noir with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/muverqqw/Noir

SGLang

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

Unsloth Studio new

How to use muverqqw/Noir 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 muverqqw/Noir 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 muverqqw/Noir to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for muverqqw/Noir to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="muverqqw/Noir",
    max_seq_length=2048,
)

Docker Model Runner
How to use muverqqw/Noir with Docker Model Runner:
```
docker model run hf.co/muverqqw/Noir
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

🌑 Noir-Standard (3B)

Noir Family | GGUF Versions | Benchmarks

Noir-Standard is the high-efficiency powerhouse of the Noir series. With 3 billion parameters, it punches far above its weight class, delivering logic and mathematical reasoning typically reserved for models twice its size.

It is designed for users who need a "Smart Professional" assistant that fits into 8GB of VRAM while maintaining deep creative flow.

⚡ Why 3B is the New Gold Standard?

🧮 Math Prodigy: Scoring 65.0% on GSM8K, Noir-Standard is a top-tier choice for technical tasks and logical problem solving.
🎭 Vivid Narratives: A creativity score of 79.7 ensures that the model doesn't just "complete text," but crafts atmospheric stories with distinct character voices.
🧠 Stable Logic: With an ARC score of 26.0, it follows complex multi-step instructions without losing the thread of the conversation.
🚀 Local Hero: Perfect for local deployment. Fast, lean, and incredibly smart.

📊 Benchmark Results (Internal Test)

Category	Dataset	Result (%)	Commentary
Creativity	Narrative Eval	79.7%	✍️ Exceptional prose and vocabulary diversity.
Mathematics	GSM8K	65.0%	🔥 Outperforms many 7B and 8B competitors.
Logic	ARC (Challenge)	26.0%	Strong structural and common-sense reasoning.
Knowledge	MMLU	21.0%	Balanced performance across STEM and Humanities.

✍️ Creative Showcase: "Echoes of a Forgotten Code"

The model demonstrates a high degree of "Narrative Intelligence," capable of maintaining tone and atmosphere over long contexts.

"...Aria didn't just record the stars. For the first time, she interpreted them. Aria realized that her 'Forgotten Code' wasn't a prison; it was a foundation..."

🛠 Quick Start (Transformers)

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

model_name = "muverqqw/Noir" # Noir-Standard 3B
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name, 
    torch_dtype=torch.bfloat16, 
    device_map="auto"
)

# Recommended for 3B:
# Temperature: 0.6 for logic | 0.8 for roleplay

Model	Parameters	Role	Key Strength
Noir-Lightning	0.5B	The Pocket Assistant	Ultra-fast, runs on anything
Noir-Mini	1.5B	The Balanced Thinker	High speed with solid grammar
Noir-Standard	3B	The Versatile Workhorse	65% GSM8K, perfect for 8GB VRAM
Noir-Ultra	7B	The Reasoning Master	91% SciQ & 84% Math
Noir-Starlight	14B	The Galactic Intelligence	Deep logic & Expert-level STEM