Instructions to use Maxilicious20/Aether-2.5-Coder-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-2.5-Coder-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-2.5-Coder-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf Maxilicious20/Aether-2.5-Coder-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-2.5-Coder-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf Maxilicious20/Aether-2.5-Coder-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-2.5-Coder-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf Maxilicious20/Aether-2.5-Coder-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-2.5-Coder-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Maxilicious20/Aether-2.5-Coder-GGUF:Q4_K_M

Use Docker

docker model run hf.co/Maxilicious20/Aether-2.5-Coder-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use Maxilicious20/Aether-2.5-Coder-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Maxilicious20/Aether-2.5-Coder-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-2.5-Coder-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Maxilicious20/Aether-2.5-Coder-GGUF:Q4_K_M

Ollama
How to use Maxilicious20/Aether-2.5-Coder-GGUF with Ollama:
```
ollama run hf.co/Maxilicious20/Aether-2.5-Coder-GGUF:Q4_K_M
```

Unsloth Studio

How to use Maxilicious20/Aether-2.5-Coder-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-2.5-Coder-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-2.5-Coder-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-2.5-Coder-GGUF to start chatting

How to use Maxilicious20/Aether-2.5-Coder-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-2.5-Coder-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-2.5-Coder-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

OpenClaw new

How to use Maxilicious20/Aether-2.5-Coder-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-2.5-Coder-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-2.5-Coder-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-2.5-Coder-GGUF with Docker Model Runner:
```
docker model run hf.co/Maxilicious20/Aether-2.5-Coder-GGUF:Q4_K_M
```

Lemonade

How to use Maxilicious20/Aether-2.5-Coder-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Maxilicious20/Aether-2.5-Coder-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Aether-2.5-Coder-GGUF-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use Maxilicious20/Aether-2.5-Coder-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-2.5-Coder-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-2.5-Coder-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat

Aether-2.5-Coder-GGUF

File size: 1,685 Bytes

---
base_model: Maxilicious20/Aether-2.5-Coder-3B
library_name: gguf
pipeline_tag: text-generation
tags:
- base_model:adapter:Qwen/Qwen2.5-Coder-3B-Instruct
- gguf
- quantized
- llama-cpp
- coding
- code-generation
- german
- english
- aether
license: apache-2.0
language:
- de
- en
---

# Aether 2.5 Coder - GGUF

This repository contains the quantized GGUF format binaries for **Aether 2.5 Coder**, fine-tuned from **Qwen2.5-Coder-3B-Instruct**. These files are optimized for local inference using tools like **LM Studio**, **Ollama**, **Jan**, **text-generation-webui**, or **llama.cpp**.

> 🔗 **Original LoRA / Adapter Repository:**
> 👉 **[Maxilicious20/Aether-2.5-Coder](https://huggingface.co/Maxilicious20/Aether-2.5-Coder)**

---

## 📦 Available Files

| File Name | Quant Method | File Size | Description |
| :--- | :--- | :--- | :--- |
| `aether_coder_q4_k_m.gguf` | Q4_K_M | ~2.0 GB | **Recommended:** Best balance between performance, low VRAM usage, and speed. |
| `aether_coder_q8_0.gguf` | Q8_0 | ~3.4 GB | **High Quality:** Extremely close to 16-bit precision with minimal loss in accuracy. |
| `aether_coder_f16.gguf` | F16 | ~6.2 GB | **Uncompressed:** Unquantized full-precision GGUF export. |

---

## 🚀 How to Use

### 1. LM Studio / Jan / Local WebUIs
1. Download `aether_coder_q4_k_m.gguf` or `aether_coder_q8_0.gguf`.
2. Move the `.gguf` file into your local models folder.
3. Select the model and start chatting or coding!

### 2. Ollama
Create a custom `Modelfile`:

```dockerfile
FROM ./aether_coder_q4_k_m.gguf

SYSTEM """You are Aether 2.5 Coder, an expert AI programming assistant."""

PARAMETER stop "<|im_start|>"
PARAMETER stop "<|im_end|>"