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 Desktop
- 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
- Atomic Chat
File size: 3,098 Bytes
15c3607 | 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 | import { MEGAPIXELS_TO_PIXELS } from '$lib/constants/image-size';
import { BASE64_IMAGE_URI_REGEX } from '$lib/constants/uri-template';
import { getJpegOrientationFromDataURL, isJpegMimeType } from './jpeg-orientation';
import { MimeTypeImage } from '$lib/enums';
/**
* Converts an Image base64 data URL to another Image data URL with capped dimensions to reduce file size.
*
* For JPEGs the EXIF orientation is baked into the pixels in the same canvas
* pass, the browser applies the rotation when decoding so naturalWidth and
* naturalHeight already describe the upright image. Backends decoding with
* stb_image ignore EXIF, see ggml-org/llama.cpp#20870. Images that need
* neither capping nor rotation pass through untouched, so at most one
* re-encode ever happens.
* @param base64UrlImage - The Image base64 data URL to convert
* @param maxMegapixels - The maximum image size in megapixels for the output Image, 0 disables capping
* @returns Promise resolving to Image data URL
*/
export function capImageDataURLSize(
base64UrlImage: string,
maxMegapixels: number
): Promise<string> {
return new Promise((resolve, reject) => {
try {
const mimeMatch = base64UrlImage.match(BASE64_IMAGE_URI_REGEX);
if (!mimeMatch) {
return reject(new Error('Invalid data URL format.'));
}
const mimeType = mimeMatch[1] as MimeTypeImage;
if (!Object.values(MimeTypeImage).includes(mimeType)) {
return reject(new Error(`Unsupported image MIME type: ${mimeType}`));
}
const orientation = isJpegMimeType(mimeType)
? getJpegOrientationFromDataURL(base64UrlImage)
: 1;
const img = new Image();
img.onload = () => {
try {
const canvas = document.createElement('canvas');
const ctx = canvas.getContext('2d');
if (!ctx) {
throw new Error('Failed to get 2D canvas context.');
}
const targetWidth = img.naturalWidth;
const targetHeight = img.naturalHeight;
const totalPixels = targetWidth * targetHeight;
const maxPixels = Math.floor(maxMegapixels * MEGAPIXELS_TO_PIXELS);
if (maxPixels > 0 && totalPixels > maxPixels) {
const scaleFactor = Math.sqrt(maxPixels / totalPixels);
canvas.width = Math.floor(targetWidth * scaleFactor);
canvas.height = Math.floor(targetHeight * scaleFactor);
} else if (orientation > 1) {
// No capping needed but the pixels still need the rotation baked in
canvas.width = targetWidth;
canvas.height = targetHeight;
} else {
return resolve(base64UrlImage);
}
ctx.drawImage(img, 0, 0, canvas.width, canvas.height);
resolve(canvas.toDataURL(mimeType));
} catch (err) {
reject(err instanceof Error ? err : new Error(String(err)));
}
};
img.onerror = () => {
reject(new Error('Failed to load image.'));
};
img.src = base64UrlImage;
} catch (error) {
const message = error instanceof Error ? error.message : String(error);
const errorMessage = `Error resizing image: ${message}`;
console.error(errorMessage, error);
reject(new Error(errorMessage));
}
});
}
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