Instructions to use North-ML1/Aurora-One with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use North-ML1/Aurora-One with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="North-ML1/Aurora-One",
	filename="aurora-one-generalization-repair-v4-f16.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use North-ML1/Aurora-One 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 North-ML1/Aurora-One:F16
# Run inference directly in the terminal:
llama cli -hf North-ML1/Aurora-One:F16

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf North-ML1/Aurora-One:F16
# Run inference directly in the terminal:
llama cli -hf North-ML1/Aurora-One:F16

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 North-ML1/Aurora-One:F16
# Run inference directly in the terminal:
./llama-cli -hf North-ML1/Aurora-One:F16

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 North-ML1/Aurora-One:F16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf North-ML1/Aurora-One:F16

Use Docker

docker model run hf.co/North-ML1/Aurora-One:F16

LM Studio
Jan

vLLM

How to use North-ML1/Aurora-One with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "North-ML1/Aurora-One"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "North-ML1/Aurora-One",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/North-ML1/Aurora-One:F16

Ollama
How to use North-ML1/Aurora-One with Ollama:
```
ollama run hf.co/North-ML1/Aurora-One:F16
```

Unsloth Studio

How to use North-ML1/Aurora-One 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 North-ML1/Aurora-One 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 North-ML1/Aurora-One to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for North-ML1/Aurora-One to start chatting

Atomic Chat new
Docker Model Runner
How to use North-ML1/Aurora-One with Docker Model Runner:
```
docker model run hf.co/North-ML1/Aurora-One:F16
```

Lemonade

How to use North-ML1/Aurora-One with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull North-ML1/Aurora-One:F16

Run and chat with the model

lemonade run user.Aurora-One-F16

List all available models

lemonade list

Aurora One GGUF

Aurora One is a small from-scratch decoder-only language model. This repository contains GGUF exports for local inference.

This is a custom Aurora architecture exported through a Qwen3-compatible GGUF path. It is not a Qwen model.

Files

aurora-one-generalization-repair-v4-f16.gguf - recommended GGUF for llama.cpp / LM Studio server API.
aurora-one-generalization-repair-v4-lmstudio-f16.gguf - alternate export with conditional ChatML template metadata.
SYSTEM_PROMPT.txt - recommended system prompt.
aurora_lmstudio_adapter.py - optional OpenAI-compatible middleware for deterministic arithmetic/sorting/live-data fallback/search.

Recommended Prompt Format

Use ChatML:

<|im_start|>system
You are Aurora One. Follow the user's instruction exactly. Be concise by default. Do not invent live facts or pretend to use tools. Only use a database, search, internet, or external tool if the system prompt explicitly says it is available. If the answer is not in your training data and no such access is explicitly available, say exactly: According to my training data, I cannot answer this question reliably. For code-only requests, output only working code.<|im_end|>
<|im_start|>user
Hello!<|im_end|>
<|im_start|>assistant

Recommended stop strings:

<|im_end|>
<eos>
<|end|>

LM Studio

The LM Studio lms chat wrapper can route custom qwen3-shaped GGUFs poorly. Use the LM Studio local server API instead.

lms server start
lms load aurora-one-generalization-repair-v4-f16.gguf --identifier aurora-one --gpu max -c 2048 -y

Call:

http://127.0.0.1:1234/v1/chat/completions

Use model: "aurora-one" and include the system prompt from SYSTEM_PROMPT.txt.

Optional Adapter

For a more useful server deployment, run the included adapter in front of LM Studio:

python3 aurora_lmstudio_adapter.py --listen-port 8088 --enable-search

Then call:

http://127.0.0.1:8088/v1/chat/completions

The adapter:

handles simple arithmetic deterministically,
sorts comma-separated numbers/words,
handles a few common deterministic translation/instruction cases,
returns the safe fallback for current/live facts unless search is explicitly enabled in the system prompt,
can use CoinGecko for BTC, wttr.in for weather, and modal.com/pricing for Modal GPU pricing.

For search/live access, include a system prompt sentence such as:

Search/internet/database access is available for current facts.

Known Limitations

Aurora One is a small experimental model. It is not a reliable general assistant by itself. It can fail on arithmetic, exact instruction following, factual recall, translation, and reasoning. For production use, keep deterministic tools/middleware around it.

Publish From Local Folder

From this folder:

hf auth login
hf repo create YOUR_USERNAME/aurora-one-gguf --type model
hf upload YOUR_USERNAME/aurora-one-gguf . .

Or with git-lfs:

git init
git lfs install
git remote add origin https://huggingface.co/YOUR_USERNAME/aurora-one-gguf
git add .
git commit -m "Publish Aurora One GGUF"
git push origin main

Downloads last month: -

GGUF

Model size

0.3B params

Architecture

qwen3

Hardware compatibility

16-bit