Instructions to use Maxilicious20/Aether-2.3-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-2.3-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-2.3-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf Maxilicious20/Aether-2.3-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-2.3-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf Maxilicious20/Aether-2.3-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-2.3-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf Maxilicious20/Aether-2.3-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-2.3-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Maxilicious20/Aether-2.3-GGUF:Q4_K_M

Use Docker

docker model run hf.co/Maxilicious20/Aether-2.3-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use Maxilicious20/Aether-2.3-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Maxilicious20/Aether-2.3-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-2.3-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Maxilicious20/Aether-2.3-GGUF:Q4_K_M

Ollama
How to use Maxilicious20/Aether-2.3-GGUF with Ollama:
```
ollama run hf.co/Maxilicious20/Aether-2.3-GGUF:Q4_K_M
```

Unsloth Studio

How to use Maxilicious20/Aether-2.3-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-2.3-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-2.3-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-2.3-GGUF to start chatting

How to use Maxilicious20/Aether-2.3-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-2.3-GGUF:Q4_K_M

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": "Maxilicious20/Aether-2.3-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use Maxilicious20/Aether-2.3-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-2.3-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-2.3-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use Maxilicious20/Aether-2.3-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-2.3-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-2.3-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"

Docker Model Runner
How to use Maxilicious20/Aether-2.3-GGUF with Docker Model Runner:
```
docker model run hf.co/Maxilicious20/Aether-2.3-GGUF:Q4_K_M
```

Lemonade

How to use Maxilicious20/Aether-2.3-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Maxilicious20/Aether-2.3-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Aether-2.3-GGUF-Q4_K_M

List all available models

lemonade list

Aether 2.3 - GGUF

Pre-quantized GGUF binaries for Aether 2.3, scaling up to the powerful Qwen2.5-3B-Instruct base architecture.

Trained with SFT (Supervised Fine-Tuning) and PEFT (LoRA) on a custom 3 GB dataset using local NVIDIA RTX GPU acceleration, Aether 2.3 delivers high intelligence, robust conversational capabilities, and exceptional multilingual performance 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-2.3

📦 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_3_fp16.gguf`	FP16 / F16	Maximum	~5.75 GB	Uncompressed full precision. Best quality, requires more VRAM.
`aether_2_3_q8_0.gguf`	Q8_0	Very High	~3.05 GB	Near-lossless quantization. Excellent balance of precision and speed.
`aether_2_3_q4_k_m.gguf`	Q4_K_M	Balanced	~1.79 GB	Recommended. Best compromise between speed, size, and minimal quality loss.

🚀 How to Run Locally

1. LM Studio

Open LM Studio.
Search for Maxilicious20/Aether-2.3-GGUF or paste the repo ID.
Download your preferred quantization (e.g., aether_2_3_q4_k_m.gguf).
Load the model and start chatting!

2. Ollama / llama.cpp

You can run the GGUF file directly using llama.cpp:

./llama-cli -m aether_2_3_q4_k_m.gguf -p "Hello Aether!" -n 256

Downloads last month: -

GGUF

Model size

3B params

Architecture

qwen2

Hardware compatibility

4-bit

8-bit

View +1 variant

Model tree for Maxilicious20/Aether-2.3-GGUF

Base model

Qwen/Qwen2.5-3B

Finetuned

Qwen/Qwen2.5-3B-Instruct

Quantized

(262)

this model

Collection including Maxilicious20/Aether-2.3-GGUF

Aether

Collection

A collection of every Aether AI model • 9 items • Updated about 15 hours ago