Text Generation
GGUF
English
qwen3
chatml
causal-lm
aurora-one
aurora-ai
aurora-model
aurora-llm
north-ml
northml
language-model
large-language-model
llm
ai-model
chat-model
assistant-model
conversational-ai
generative-ai
openai-compatible
api-compatible
custom-llm
proprietary-model
research-model
experimental-ai
developer-ai
coding-assistant
code-generation
reasoning-model
instruction-following
chat-completion
completion-model
transformer
neural-network
machine-learning
deep-learning
nlp
natural-language-processing
text-ai
ai-assistant
smart-assistant
question-answering
qa-model
knowledge-model
prompting
prompt-engineering
system-prompt
developer-tools
devtools
ai-runtime
model-runtime
inference-api
fast-inference
low-latency
api-endpoint
cloud-ai
hosted-model
model-serving
ml-serving
inference-server
custom-api
north-api
aurora-api
aurora-family
foundation-model
small-language-model
slm
compact-llm
efficient-ai
lightweight-model
edge-ai
local-ai
server-ai
gpu-inference
cuda
benchmarking
evals
model-evaluation
accuracy-testing
gsm8k
gpqa
swe-bench
coding-benchmark
math-reasoning
logic-reasoning
instruction-tuned
fine-tuned
alignment
safe-ai
helpful-ai
agentic-ai
tool-use
function-calling
json-mode
structured-output
markdown-generation
readme-generator
chatbot
ai-chatbot
virtual-assistant
automation
productivity-ai
developer-preview
beta-model
next-gen-ai
future-ai
conversational
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
| license: apache-2.0 | |
| language: | |
| - en | |
| library_name: gguf | |
| pipeline_tag: text-generation | |
| tags: | |
| - gguf | |
| - qwen3 | |
| - chatml | |
| - causal-lm | |
| - aurora-one | |
| - aurora-one | |
| - aurora-ai | |
| - aurora-model | |
| - aurora-llm | |
| - north-ml | |
| - northml | |
| - language-model | |
| - large-language-model | |
| - llm | |
| - ai-model | |
| - chat-model | |
| - assistant-model | |
| - conversational-ai | |
| - text-generation | |
| - generative-ai | |
| - openai-compatible | |
| - api-compatible | |
| - custom-llm | |
| - proprietary-model | |
| - research-model | |
| - experimental-ai | |
| - developer-ai | |
| - coding-assistant | |
| - code-generation | |
| - reasoning-model | |
| - instruction-following | |
| - chat-completion | |
| - completion-model | |
| - transformer | |
| - neural-network | |
| - machine-learning | |
| - deep-learning | |
| - nlp | |
| - natural-language-processing | |
| - text-ai | |
| - ai-assistant | |
| - smart-assistant | |
| - question-answering | |
| - qa-model | |
| - knowledge-model | |
| - prompting | |
| - prompt-engineering | |
| - system-prompt | |
| - developer-tools | |
| - devtools | |
| - ai-runtime | |
| - model-runtime | |
| - inference-api | |
| - fast-inference | |
| - low-latency | |
| - api-endpoint | |
| - cloud-ai | |
| - hosted-model | |
| - model-serving | |
| - ml-serving | |
| - inference-server | |
| - custom-api | |
| - north-api | |
| - aurora-api | |
| - aurora-family | |
| - foundation-model | |
| - small-language-model | |
| - slm | |
| - compact-llm | |
| - efficient-ai | |
| - lightweight-model | |
| - edge-ai | |
| - local-ai | |
| - server-ai | |
| - gpu-inference | |
| - cuda | |
| - benchmarking | |
| - evals | |
| - model-evaluation | |
| - accuracy-testing | |
| - gsm8k | |
| - gpqa | |
| - swe-bench | |
| - coding-benchmark | |
| - math-reasoning | |
| - logic-reasoning | |
| - instruction-tuned | |
| - fine-tuned | |
| - alignment | |
| - safe-ai | |
| - helpful-ai | |
| - agentic-ai | |
| - tool-use | |
| - function-calling | |
| - json-mode | |
| - structured-output | |
| - markdown-generation | |
| - readme-generator | |
| - chatbot | |
| - ai-chatbot | |
| - virtual-assistant | |
| - automation | |
| - productivity-ai | |
| - developer-preview | |
| - beta-model | |
| - next-gen-ai | |
| - future-ai | |
| # 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: | |
| ```text | |
| <|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: | |
| ```text | |
| <|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. | |
| ```bash | |
| lms server start | |
| lms load aurora-one-generalization-repair-v4-f16.gguf --identifier aurora-one --gpu max -c 2048 -y | |
| ``` | |
| Call: | |
| ```text | |
| 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: | |
| ```bash | |
| python3 aurora_lmstudio_adapter.py --listen-port 8088 --enable-search | |
| ``` | |
| Then call: | |
| ```text | |
| 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: | |
| ```text | |
| 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. | |