Instructions to use OmnipotentFool/Aurvion with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use OmnipotentFool/Aurvion 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 OmnipotentFool/Aurvion:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf OmnipotentFool/Aurvion:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf OmnipotentFool/Aurvion:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf OmnipotentFool/Aurvion: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 OmnipotentFool/Aurvion:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf OmnipotentFool/Aurvion: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 OmnipotentFool/Aurvion:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf OmnipotentFool/Aurvion:Q4_K_M

Use Docker

docker model run hf.co/OmnipotentFool/Aurvion:Q4_K_M

Llama new
LM Studio
Jan

vLLM

How to use OmnipotentFool/Aurvion with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/OmnipotentFool/Aurvion:Q4_K_M

Ollama
How to use OmnipotentFool/Aurvion with Ollama:
```
ollama run hf.co/OmnipotentFool/Aurvion:Q4_K_M
```
Unsloth Desktop
Docker Model Runner
How to use OmnipotentFool/Aurvion with Docker Model Runner:
```
docker model run hf.co/OmnipotentFool/Aurvion:Q4_K_M
```

Lemonade

How to use OmnipotentFool/Aurvion with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull OmnipotentFool/Aurvion:Q4_K_M

Run and chat with the model

lemonade run user.Aurvion-Q4_K_M

List all available models

lemonade list

Atomic Chat

Aurvion

File size: 589 Bytes

6b9d0d8

export const API_MODELS = {
	LIST: '/v1/models',
	LOAD: '/models/load',
	UNLOAD: '/models/unload'
};

// chat completion routes, the control route drives realtime inference (e.g. end reasoning)
export const API_CHAT = {
	COMPLETIONS: './v1/chat/completions',
	CONTROL: './v1/chat/completions/control'
};

// slot introspection, requires the --slots flag on the server
export const API_SLOTS = {
	LIST: './slots'
};

export const API_TOOLS = {
	LIST: '/tools',
	EXECUTE: '/tools'
};

/** CORS proxy endpoint path */
export const CORS_PROXY_ENDPOINT = '/cors-proxy';