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"

Maxilicious20 commited on 11 days ago

Commit

fc1ea39

verified ·

1 Parent(s): c3f9730

Update README.md

Browse files

Files changed (1) hide show

README.md +12 -16

README.md CHANGED Viewed

@@ -5,7 +5,6 @@ tags:
 - llama.cpp
 - lm-studio
 - aether
-- aether-2.5
 - german
 - english
 - text-generation
@@ -15,15 +14,15 @@ language:
 - en
 ---
-# Aether 2.5 Pro - GGUF
-Pre-quantized GGUF binaries for **Aether 2.5 Pro**.
-Aether 2.5 Pro is a fine-tuned version of Qwen2.5-3B-Instruct, trained with SFT (Supervised Fine-Tuning) and PEFT (LoRA). It delivers improved reasoning, stronger instruction following, and better multilingual performance in German and English while remaining efficient for local use.
-> 🔗 **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-2.5-Pro](https://huggingface.co/Maxilicious20/Aether-2.5-Pro)**
 ---
@@ -33,11 +32,9 @@ Choose the right file depending on your system's VRAM/RAM and performance needs:
 | Filename | Quantization | Quality | Size | Description / Recommendation |
 | :--- | :--- | :--- | :--- | :--- |
-| `aether-2.5-pro-f16.gguf` | FP16 / F16 | Maximum | ~6.0 GB | Uncompressed full precision. Best quality. |
-| `aether-2.5-pro-q8_0.gguf` | Q8_0 | Very High | ~3.2 GB | Near-lossless quantization. Excellent quality. |
-| `aether-2.5-pro-q5_k_m.gguf` | Q5_K_M | High | ~2.3 GB | High quality with good performance. |
-| `aether-2.5-pro-q4_k_m.gguf` | Q4_K_M | Balanced | ~1.9 GB | **Recommended.** Best balance of quality, speed and VRAM usage. |
-| `aether-2.5-pro-q3_k_m.gguf` | Q3_K_M | Good | ~1.5 GB | Lower VRAM usage, still usable quality. |
 ---
@@ -45,12 +42,11 @@ Choose the right file depending on your system's VRAM/RAM and performance needs:
 ### 1. LM Studio
 1. Open LM Studio.
-2. Search for `Maxilicious20/Aether-2.5-Pro-GGUF` or paste the repository ID.
-3. Download your preferred quantization (recommended: `aether-2.5-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-2.5-pro-q4_k_m.gguf -p "Hello Aether 2.5 Pro!" -n 256

 - llama.cpp
 - lm-studio
 - aether
 - german
 - english
 - text-generation
 - 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)**
 ---
 | 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. |
 ---
 ### 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