Instructions to use RoeAcquisitions/aether-mini-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use RoeAcquisitions/aether-mini-v1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="RoeAcquisitions/aether-mini-v1") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("RoeAcquisitions/aether-mini-v1", dtype="auto") - llama-cpp-python
How to use RoeAcquisitions/aether-mini-v1 with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="RoeAcquisitions/aether-mini-v1", filename="model.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 RoeAcquisitions/aether-mini-v1 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 RoeAcquisitions/aether-mini-v1 # Run inference directly in the terminal: llama cli -hf RoeAcquisitions/aether-mini-v1
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf RoeAcquisitions/aether-mini-v1 # Run inference directly in the terminal: llama cli -hf RoeAcquisitions/aether-mini-v1
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 RoeAcquisitions/aether-mini-v1 # Run inference directly in the terminal: ./llama-cli -hf RoeAcquisitions/aether-mini-v1
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 RoeAcquisitions/aether-mini-v1 # Run inference directly in the terminal: ./build/bin/llama-cli -hf RoeAcquisitions/aether-mini-v1
Use Docker
docker model run hf.co/RoeAcquisitions/aether-mini-v1
- LM Studio
- Jan
- vLLM
How to use RoeAcquisitions/aether-mini-v1 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "RoeAcquisitions/aether-mini-v1" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "RoeAcquisitions/aether-mini-v1", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/RoeAcquisitions/aether-mini-v1
- SGLang
How to use RoeAcquisitions/aether-mini-v1 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 "RoeAcquisitions/aether-mini-v1" \ --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": "RoeAcquisitions/aether-mini-v1", "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 "RoeAcquisitions/aether-mini-v1" \ --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": "RoeAcquisitions/aether-mini-v1", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Ollama
How to use RoeAcquisitions/aether-mini-v1 with Ollama:
ollama run hf.co/RoeAcquisitions/aether-mini-v1
- Unsloth Studio
How to use RoeAcquisitions/aether-mini-v1 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 RoeAcquisitions/aether-mini-v1 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 RoeAcquisitions/aether-mini-v1 to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for RoeAcquisitions/aether-mini-v1 to start chatting
- Pi
How to use RoeAcquisitions/aether-mini-v1 with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf RoeAcquisitions/aether-mini-v1
Configure the model in Pi
# Install Pi: npm install -g @mariozechner/pi-coding-agent # Add to ~/.pi/agent/models.json: { "providers": { "llama-cpp": { "baseUrl": "http://localhost:8080/v1", "api": "openai-completions", "apiKey": "none", "models": [ { "id": "RoeAcquisitions/aether-mini-v1" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use RoeAcquisitions/aether-mini-v1 with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf RoeAcquisitions/aether-mini-v1
Configure Hermes
# Install Hermes: curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash hermes setup # Point Hermes at the local server: hermes config set model.provider custom hermes config set model.base_url http://127.0.0.1:8080/v1 hermes config set model.default RoeAcquisitions/aether-mini-v1
Run Hermes
hermes
- Atomic Chat new
- OpenClaw new
How to use RoeAcquisitions/aether-mini-v1 with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf RoeAcquisitions/aether-mini-v1
Configure OpenClaw
# Install OpenClaw: npm install -g openclaw@latest # Register the local server and set it as the default model: openclaw onboard --non-interactive --mode local \ --auth-choice custom-api-key \ --custom-base-url http://127.0.0.1:8080/v1 \ --custom-model-id "RoeAcquisitions/aether-mini-v1" \ --custom-provider-id llama-cpp \ --custom-compatibility openai \ --custom-text-input \ --accept-risk \ --skip-health
Run OpenClaw
openclaw agent --local --agent main --message "Hello from Hugging Face"
- Docker Model Runner
How to use RoeAcquisitions/aether-mini-v1 with Docker Model Runner:
docker model run hf.co/RoeAcquisitions/aether-mini-v1
- Lemonade
How to use RoeAcquisitions/aether-mini-v1 with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull RoeAcquisitions/aether-mini-v1
Run and chat with the model
lemonade run user.aether-mini-v1-{{QUANT_TAG}}List all available models
lemonade list
| language: | |
| - en | |
| library_name: transformers | |
| pipeline_tag: text-generation | |
| tags: | |
| - text-generation | |
| - qwen | |
| - aether | |
| - code-generation | |
| inference: true | |
| # Aether Mini V1 | |
| Aether Mini V1 is a fast, efficient 0.5B parameter language model optimized for general tasks, code generation, and instruction following. Based on Qwen2.5 architecture, fine-tuned for enterprise use cases. | |
| ## Features | |
| - **Fast Inference**: Sub-second response times on consumer hardware | |
| - **Code Generation**: Optimized for Python, JavaScript, TypeScript, and more | |
| - **Instruction Following**: Fine-tuned for precise instruction adherence | |
| - **Low Resource**: Runs on CPU with minimal memory requirements | |
| ## Usage | |
| ### Transformers | |
| ```python | |
| from transformers import AutoModelForCausalLM, AutoTokenizer | |
| model = AutoModelForCausalLM.from_pretrained("RoeAcquisitions/aether-mini-v1") | |
| tokenizer = AutoTokenizer.from_pretrained("RoeAcquisitions/aether-mini-v1") | |
| messages = [ | |
| {"role": "user", "content": "Write a Python function to calculate fibonacci numbers"} | |
| ] | |
| text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) | |
| inputs = tokenizer([text], return_tensors="pt") | |
| generated_ids = model.generate(**inputs, max_new_tokens=512) | |
| response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0] | |
| print(response) | |
| ``` | |
| ### API | |
| Access via Aether Tech AI API: | |
| ```bash | |
| curl https://aether-models.nebulahq.work/v1/chat/completions \ | |
| -H "Authorization: Bearer YOUR_API_KEY" \ | |
| -H "Content-Type: application/json" \ | |
| -d '{ | |
| "model": "aether-mini-v1", | |
| "messages": [{"role": "user", "content": "Hello"}] | |
| }' | |
| ``` | |
| ## Pricing | |
| - **Input**: $0.10 per 1M tokens | |
| - **Output**: $0.30 per 1M tokens | |
| ## License | |
| Apache 2.0 | |
| ## Contact | |
| - Website: https://nebulahq.work | |
| - Email: api@nebulahq.work | |