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
File size: 3,369 Bytes
0862f9d | 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 | const http = require('http');
const fs = require('fs').promises;
const path = require('path');
// This file is used for testing wasm build from emscripten
// Example build command:
// emcmake cmake -B build-wasm -DGGML_WEBGPU=ON -DLLAMA_OPENSSL=OFF
// cmake --build build-wasm --target test-backend-ops -j
const PORT = 8080;
const STATIC_DIR = path.join(__dirname, '../build-wasm/bin');
console.log(`Serving static files from: ${STATIC_DIR}`);
const mimeTypes = {
'.html': 'text/html',
'.js': 'text/javascript',
'.css': 'text/css',
'.png': 'image/png',
'.jpg': 'image/jpeg',
'.gif': 'image/gif',
'.svg': 'image/svg+xml',
'.json': 'application/json',
'.woff': 'font/woff',
'.woff2': 'font/woff2',
};
async function generateDirListing(dirPath, reqUrl) {
const files = await fs.readdir(dirPath);
let html = `
<!DOCTYPE html>
<html>
<head>
<title>Directory Listing</title>
<style>
body { font-family: Arial, sans-serif; padding: 20px; }
ul { list-style: none; padding: 0; }
li { margin: 5px 0; }
a { text-decoration: none; color: #0066cc; }
a:hover { text-decoration: underline; }
</style>
</head>
<body>
<h1>Directory: ${reqUrl}</h1>
<ul>
`;
if (reqUrl !== '/') {
html += `<li><a href="../">../ (Parent Directory)</a></li>`;
}
for (const file of files) {
const filePath = path.join(dirPath, file);
const stats = await fs.stat(filePath);
const link = encodeURIComponent(file) + (stats.isDirectory() ? '/' : '');
html += `<li><a href="${link}">${file}${stats.isDirectory() ? '/' : ''}</a></li>`;
}
html += `
</ul>
</body>
</html>
`;
return html;
}
const server = http.createServer(async (req, res) => {
try {
// Set COOP and COEP headers
res.setHeader('Cross-Origin-Opener-Policy', 'same-origin');
res.setHeader('Cross-Origin-Embedder-Policy', 'require-corp');
res.setHeader('Cache-Control', 'no-store, no-cache, must-revalidate, proxy-revalidate');
res.setHeader('Pragma', 'no-cache');
res.setHeader('Expires', '0');
const filePath = path.join(STATIC_DIR, decodeURIComponent(req.url));
const stats = await fs.stat(filePath);
if (stats.isDirectory()) {
const indexPath = path.join(filePath, 'index.html');
try {
const indexData = await fs.readFile(indexPath);
res.writeHeader(200, { 'Content-Type': 'text/html' });
res.end(indexData);
} catch {
// No index.html, generate directory listing
const dirListing = await generateDirListing(filePath, req.url);
res.writeHeader(200, { 'Content-Type': 'text/html' });
res.end(dirListing);
}
} else {
const ext = path.extname(filePath).toLowerCase();
const contentType = mimeTypes[ext] || 'application/octet-stream';
const data = await fs.readFile(filePath);
res.writeHeader(200, { 'Content-Type': contentType });
res.end(data);
}
} catch (err) {
if (err.code === 'ENOENT') {
res.writeHeader(404, { 'Content-Type': 'text/plain' });
res.end('404 Not Found');
} else {
res.writeHeader(500, { 'Content-Type': 'text/plain' });
res.end('500 Internal Server Error');
}
}
});
server.listen(PORT, () => {
console.log(`Server running at http://localhost:${PORT}/`);
});
|