Text Generation
Safetensors
GGUF
English
reasoning
reinforcement-learning
grpo
small-language-model
samsung-ennovatex
conversational
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
- llama.cpp
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
Build from source code
git clone https://github.com/ggml-org/llama.cpp.git cd llama.cpp cmake -B build cmake --build build -j --target llama-app # Start a local OpenAI-compatible server with a web UI: ./build/bin/llama serve -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
File size: 4,477 Bytes
6b9d0d8 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 | #!/usr/bin/env node
/**
* Apply circular mask to pwa-*.png icons.
* Uses the maskable icon as source (white bg, full logo) to avoid
* the small-colormap pwa icons looking bad when cropped to a circle.
*
* Usage: node scripts/make-icons-circular.js [--padding-pct <0-50>] [--scale-pct <50-100>]
*
* - padding-pct: percentage of icon size kept as padding around the circle (default: 25)
* - scale-pct: scale down the source image before cropping (default: 85)
*
* maskable-icon and apple-touch-icon are left untouched.
*/
import sharp from 'sharp';
import fs from 'fs';
import path from 'path';
import { fileURLToPath } from 'url';
const __filename = fileURLToPath(import.meta.url);
const __dirname = path.dirname(__filename);
const STATIC_DIR = path.resolve(__dirname, '..', 'static');
const paddingPct = process.argv.reduce((acc, arg, i, args) => {
if (arg === '--padding-pct' && args[i + 1]) return parseFloat(args[i + 1]);
return acc;
}, 0);
// Scale down the source image before cropping to circle
const scalePct = process.argv.reduce((acc, arg, i, args) => {
if (arg === '--scale-pct' && args[i + 1]) return parseFloat(args[i + 1]);
return acc;
}, 85); // default 85% - icon fills 85% of the circular area
// Source for circular icons: the maskable icon (white bg, full logo)
const sourceIcon = 'maskable-icon-512x512.png';
const targetIcons = ['pwa-64x64.png', 'pwa-192x192.png', 'pwa-512x512.png'];
// maskable-icon and apple-touch-icon stay square
const untouchedIcons = ['maskable-icon-512x512.png', 'apple-touch-icon-180x180.png'];
async function makeCircle(targetFilename) {
const targetPath = path.join(STATIC_DIR, targetFilename);
const sourcePath = path.join(STATIC_DIR, sourceIcon);
if (!fs.existsSync(sourcePath)) {
console.log(`⏭️ ${sourceIcon} not found, skipping`);
return;
}
if (!fs.existsSync(targetPath)) {
console.log(`⏭️ ${targetFilename} not found, skipping`);
return;
}
const metadata = await sharp(targetPath).metadata();
const size = Math.max(metadata.width, metadata.height);
const radius = Math.floor((size * (1 - paddingPct / 100)) / 2);
const center = Math.floor(size / 2);
// Build circular mask as RGBA buffer: white opaque circle on transparent bg
const maskBuf = Buffer.alloc(size * size * 4, 0);
for (let y = 0; y < size; y++) {
for (let x = 0; x < size; x++) {
const dx = x - center;
const dy = y - center;
const dist = Math.sqrt(dx * dx + dy * dy);
if (dist < radius) {
const i = (y * size + x) * 4;
maskBuf[i] = 255;
maskBuf[i + 1] = 255;
maskBuf[i + 2] = 255;
maskBuf[i + 3] = 255;
}
}
}
const tmpMask = path.join(STATIC_DIR, '.mask-tmp.png');
await sharp(maskBuf, {
raw: { width: size, height: size, channels: 4 }
})
.png()
.toFile(tmpMask);
// Step 1: Scale source relative to circle diameter (not full icon), composite centered onto white canvas of full size
const circleDiameter = Math.floor(size * (1 - paddingPct / 100));
const scaledSize = Math.floor((circleDiameter * scalePct) / 100);
const offset = Math.floor((size - scaledSize) / 2);
const scaledBuf = await sharp(sourcePath)
.resize(scaledSize, scaledSize, {
fit: 'cover',
background: { r: 255, g: 255, b: 255, alpha: 1 }
})
.ensureAlpha()
.png()
.toBuffer();
// Step 2: Composite scaled image onto white background, then apply circular mask
const output = await sharp({
create: {
width: size,
height: size,
channels: 4,
background: { r: 255, g: 255, b: 255, alpha: 1 }
}
})
.composite([
{ input: scaledBuf, top: offset, left: offset },
{ input: tmpMask, top: 0, left: 0, blend: 'dest-in' }
])
.png()
.toBuffer();
fs.writeFileSync(targetPath, output);
fs.unlinkSync(tmpMask);
console.log(
`✓ ${targetFilename} → circle from ${sourceIcon}, ${paddingPct}% padding (size=${size}, r=${radius}, scale=${scalePct}%, circleDiameter=${circleDiameter})`
);
}
async function main() {
console.log(`Circular mask: ${paddingPct}% padding, ${scalePct}% scale, source=${sourceIcon}\n`);
for (const icon of targetIcons) {
await makeCircle(icon);
}
console.log('\nUnchanged:');
for (const icon of untouchedIcons) {
const fp = path.join(STATIC_DIR, icon);
console.log(` ${icon} (${fs.existsSync(fp) ? fs.statSync(fp).size + ' bytes' : 'missing'})`);
}
}
main();
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