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

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 / llama.cpp /build /tools /ui /ui-src /node_modules /@tailwindcss /node /package.json

OmnipotentFool

Add files using upload-large-folder tool

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	{
	"name": "@tailwindcss/node",
	"version": "4.1.11",
	"description": "A utility-first CSS framework for rapidly building custom user interfaces.",
	"license": "MIT",
	"repository": {
	"type": "git",
	"url": "https://github.com/tailwindlabs/tailwindcss.git",
	"directory": "packages/@tailwindcss-node"
	},
	"bugs": "https://github.com/tailwindlabs/tailwindcss/issues",
	"homepage": "https://tailwindcss.com",
	"files": [
	"dist/"
	],
	"publishConfig": {
	"provenance": true,
	"access": "public"
	},
	"exports": {
	".": {
	"types": "./dist/index.d.ts",
	"import": "./dist/index.mjs",
	"require": "./dist/index.js"
	},
	"./require-cache": {
	"types": "./dist/require-cache.d.ts",
	"default": "./dist/require-cache.js"
	},
	"./esm-cache-loader": {
	"types": "./dist/esm-cache.loader.d.mts",
	"default": "./dist/esm-cache.loader.mjs"
	}
	},
	"dependencies": {
	"@ampproject/remapping": "^2.3.0",
	"enhanced-resolve": "^5.18.1",
	"jiti": "^2.4.2",
	"lightningcss": "1.30.1",
	"magic-string": "^0.30.17",
	"source-map-js": "^1.2.1",
	"tailwindcss": "4.1.11"
	},
	"scripts": {
	"build": "tsup-node",
	"dev": "pnpm run build -- --watch"
	}
	}