Instructions to use Maxilicious20/Aether-2.2-Pro-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use Maxilicious20/Aether-2.2-Pro-GGUF 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 Maxilicious20/Aether-2.2-Pro-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf Maxilicious20/Aether-2.2-Pro-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf Maxilicious20/Aether-2.2-Pro-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf Maxilicious20/Aether-2.2-Pro-GGUF:Q4_K_M

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 Maxilicious20/Aether-2.2-Pro-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf Maxilicious20/Aether-2.2-Pro-GGUF:Q4_K_M

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 Maxilicious20/Aether-2.2-Pro-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Maxilicious20/Aether-2.2-Pro-GGUF:Q4_K_M

Use Docker

docker model run hf.co/Maxilicious20/Aether-2.2-Pro-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use Maxilicious20/Aether-2.2-Pro-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Maxilicious20/Aether-2.2-Pro-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Maxilicious20/Aether-2.2-Pro-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Maxilicious20/Aether-2.2-Pro-GGUF:Q4_K_M

Ollama
How to use Maxilicious20/Aether-2.2-Pro-GGUF with Ollama:
```
ollama run hf.co/Maxilicious20/Aether-2.2-Pro-GGUF:Q4_K_M
```

Unsloth Studio

How to use Maxilicious20/Aether-2.2-Pro-GGUF 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 Maxilicious20/Aether-2.2-Pro-GGUF 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 Maxilicious20/Aether-2.2-Pro-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Maxilicious20/Aether-2.2-Pro-GGUF to start chatting

How to use Maxilicious20/Aether-2.2-Pro-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Maxilicious20/Aether-2.2-Pro-GGUF:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "Maxilicious20/Aether-2.2-Pro-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use Maxilicious20/Aether-2.2-Pro-GGUF with Docker Model Runner:
```
docker model run hf.co/Maxilicious20/Aether-2.2-Pro-GGUF:Q4_K_M
```

Lemonade

How to use Maxilicious20/Aether-2.2-Pro-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Maxilicious20/Aether-2.2-Pro-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Aether-2.2-Pro-GGUF-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use Maxilicious20/Aether-2.2-Pro-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Maxilicious20/Aether-2.2-Pro-GGUF:Q4_K_M

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 Maxilicious20/Aether-2.2-Pro-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use Maxilicious20/Aether-2.2-Pro-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Maxilicious20/Aether-2.2-Pro-GGUF:Q4_K_M

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 "Maxilicious20/Aether-2.2-Pro-GGUF:Q4_K_M" \
  --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"

Aether-2.2-Pro-GGUF / README.md

Maxilicious20

Update README.md

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	---
	library_name: gguf
	tags:
	- gguf
	- llama.cpp
	- lm-studio
	- aether
	- german
	- english
	- text-generation
	license: apache-2.0
	language:
	- de
	- en
	---

	# Aether 2.2 Pro - GGUF

	Pre-quantized GGUF binaries for Aether 2.2 Pro.

	Trained with SFT (Supervised Fine-Tuning) and PEFT (LoRA) on a custom dataset using local NVIDIA RTX GPU acceleration, Aether 2.2 Pro delivers optimized performance, strong conversational capabilities, and reliable multilingual responses in German and English.

	> 🔗 Looking for the Base / LoRA Adapter?
	> If you want to use the Hugging Face Transformers PEFT adapter instead, check out the main repository:
	> 👉 [Maxilicious20/Aether-2.2-Pro](https://huggingface.co/Maxilicious20/Aether-2.2-Pro)

	---

	## 📦 Available Files & Quantizations

	Choose the right file depending on your system's VRAM/RAM and performance needs:

	\| Filename \| Quantization \| Quality \| Size \| Description / Recommendation \|
	\| :--- \| :--- \| :--- \| :--- \| :--- \|
	\| `aether_2_2_pro_f16.gguf` \| FP16 / F16 \| Maximum \| ~2.88 GB \| Uncompressed full precision. Best quality. \|
	\| `aether_2_2_pro_q8_0.gguf` \| Q8_0 \| Very High \| ~1.53 GB \| Near-lossless quantization. Excellent balance of precision and speed. \|
	\| `aether_2_2_pro_q4_k_m.gguf` \| Q4_K_M \| Balanced \| ~940 MB \| Recommended. Lightweight, fast, and optimized for low VRAM/RAM setups. \|

	---

	## 🚀 How to Run Locally

	### 1. LM Studio
	1. Open LM Studio.
	2. Search for `Maxilicious20/Aether-2.2-Pro-GGUF` or paste the repository ID.
	3. Download your preferred quantization (e.g., `aether_2_2_pro_q4_k_m.gguf`).
	4. Load the model and start chatting!

	### 2. Ollama / llama.cpp
	You can run the GGUF file directly using `llama.cpp`:
	```bash
	./llama-cli -m aether_2_2_pro_q4_k_m.gguf -p "Hello Aether Pro!" -n 256