Safetensors
GGUF
Turkish
llama
Llama-3
instruct
finetune
chatml
gpt4
synthetic data
distillation
function calling
json mode
axolotl
roleplaying
chat
Instructions to use tda45/TdAI 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 tda45/TdAI 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 tda45/TdAI # Run inference directly in the terminal: llama cli -hf tda45/TdAI
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf tda45/TdAI # Run inference directly in the terminal: llama cli -hf tda45/TdAI
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 tda45/TdAI # Run inference directly in the terminal: ./llama-cli -hf tda45/TdAI
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 tda45/TdAI # Run inference directly in the terminal: ./build/bin/llama-cli -hf tda45/TdAI
Use Docker
docker model run hf.co/tda45/TdAI
- LM Studio
- Jan
- Ollama
How to use tda45/TdAI with Ollama:
ollama run hf.co/tda45/TdAI
- Unsloth Studio
How to use tda45/TdAI 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 tda45/TdAI 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 tda45/TdAI to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for tda45/TdAI to start chatting
- Atomic Chat new
- Docker Model Runner
How to use tda45/TdAI with Docker Model Runner:
docker model run hf.co/tda45/TdAI
- Lemonade
How to use tda45/TdAI with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull tda45/TdAI
Run and chat with the model
lemonade run user.TdAI-{{QUANT_TAG}}List all available models
lemonade list
File size: 4,340 Bytes
8efb28e | 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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