Instructions to use North-ML1/Aurora-One-Main with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use North-ML1/Aurora-One-Main with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="North-ML1/Aurora-One-Main", 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-Main 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-Main:F16 # Run inference directly in the terminal: llama cli -hf North-ML1/Aurora-One-Main: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-Main:F16 # Run inference directly in the terminal: llama cli -hf North-ML1/Aurora-One-Main: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-Main:F16 # Run inference directly in the terminal: ./llama-cli -hf North-ML1/Aurora-One-Main: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-Main:F16 # Run inference directly in the terminal: ./build/bin/llama-cli -hf North-ML1/Aurora-One-Main:F16
Use Docker
docker model run hf.co/North-ML1/Aurora-One-Main:F16
- LM Studio
- Jan
- vLLM
How to use North-ML1/Aurora-One-Main 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-Main" # 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-Main", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/North-ML1/Aurora-One-Main:F16
- Ollama
How to use North-ML1/Aurora-One-Main with Ollama:
ollama run hf.co/North-ML1/Aurora-One-Main:F16
- Unsloth Studio
How to use North-ML1/Aurora-One-Main 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-Main 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-Main 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-Main to start chatting
- Atomic Chat new
- Docker Model Runner
How to use North-ML1/Aurora-One-Main with Docker Model Runner:
docker model run hf.co/North-ML1/Aurora-One-Main:F16
- Lemonade
How to use North-ML1/Aurora-One-Main with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull North-ML1/Aurora-One-Main:F16
Run and chat with the model
lemonade run user.Aurora-One-Main-F16
List all available models
lemonade list
Aurora One Main
Aurora One is a small from-scratch decoder-only language model. This repository contains GGUF exports for local inference. However, this is not the full Aurora model. Aurora One's tokens is also corrected through our systems to provide accurate, up-to-facts info. JESUS is king.
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.
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