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.
- Libraries
- llama-cpp-python
How to use tda45/TdAI with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="tda45/TdAI", filename="llama.cpp/models/ggml-vocab-aquila.gguf", )
output = llm( "Once upon a time,", max_tokens=512, echo=True ) print(output)
- 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
| /** | |
| * 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(); | |