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

Libraries

How to use muverqqw/Noir-Ultra with Transformers:

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

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

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

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

Inference
Local Apps Settings

vLLM

How to use muverqqw/Noir-Ultra with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/muverqqw/Noir-Ultra

SGLang

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

Unsloth Studio

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

Load model with FastModel

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

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

🌌 Noir-Ultra (7B)

Noir Family | GGUF Versions | Technical Report

Noir-Ultra is the elite 7-billion parameter model of the Noir series. It represents a breakthrough in training efficiency: where previous 7B iterations required 6 epochs to reach stability, Noir-Ultra achieved superior results in just a single epoch.

This model is a "compact titan," delivering scientific reasoning and mathematical accuracy that rival much larger architectures.

🚀 The Ultra Advantage

🧬 Unrivaled STEM: With a 91.0% score on SciQ, it is a specialized tool for scientific inquiry.
📐 Mathematical Precision: Scoring 84.0% on GSM8K, it handles complex chains of thought with ease.
🧠 Logical Depth: An ARC-Challenge score of 86.0% places it at the top of its weight class for reasoning tasks.

📊 Evaluation Dashboard (Noir Ultra Report)

Based on the latest evaluation, Noir-Ultra shows an exceptionally strong profile in technical domains:

Domain	Benchmark	Result (%)	Status
STEM	SciQ	91.0%	🏆 Master
Logic	ARC-C	86.0%	🔥 Elite
Math	GSM8K	84.0%	✅ Advanced
Medicine	MedQA	65.0%	🩺 Competent
Physics	MMLU-Physics	70.0%	🧪 Specialist

📦 Noir Model Family Matrix

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

🛠 Quick Start (Transformers)

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

model_name = "muverqqw/Noir-Ultra" 
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name, 
    load_in_4bit=True, # Recommended for 8GB VRAM
    device_map="auto"
)