Instructions to use RoeAcquisitions/aether-mini-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

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

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
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

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

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:

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

Downloads last month: 113

GGUF

Model size

0.5B params

Architecture

qwen2

Hardware compatibility

We're not able to determine the quantization variants.

View all variants