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: 1,817 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 | import { MimeTypeImage } from '$lib/enums';
import { HEIC_JPEG_QUALITY } from '$lib/constants/image-size';
// heic requires a relatively large decoder, in order to reduce primary bundle size
// we lazily load this decoder from a CDN when needed, and cache it for future conversions
const HEIC_TO_CDN_URL = 'https://cdn.jsdelivr.net/npm/heic-to@1.5.2/dist/heic-to.js';
interface HeicToModule {
heicTo(args: { blob: Blob; type: string; quality?: number }): Promise<Blob>;
}
let modulePromise: Promise<HeicToModule> | null = null;
/**
* Lazily load the heic-to decoder from the CDN and cache it
* @returns Promise resolving to the heic-to module
*/
function getHeicTo(): Promise<HeicToModule> {
if (!modulePromise) {
modulePromise = import(/* @vite-ignore */ HEIC_TO_CDN_URL) as Promise<HeicToModule>;
}
return modulePromise;
}
/**
* Convert a HEIC/HEIF file to a compressed JPEG data URL
* @param file - The HEIC/HEIF file to convert
* @returns Promise resolving to JPEG data URL
*/
export async function heicFileToJpegDataURL(file: File | Blob): Promise<string> {
const { heicTo } = await getHeicTo();
const jpegBlob = await heicTo({
blob: file,
type: MimeTypeImage.JPEG,
quality: HEIC_JPEG_QUALITY
});
return new Promise((resolve, reject) => {
const reader = new FileReader();
reader.onload = () => resolve(reader.result as string);
reader.onerror = () => reject(reader.error);
reader.readAsDataURL(jpegBlob);
});
}
/**
* Check if a MIME type represents a HEIC/HEIF image
* @param mimeType - The MIME type to check
* @returns True if the MIME type is image/heic or image/heif
*/
export function isHeicMimeType(mimeType: string): boolean {
const normalized = mimeType.trim().toLowerCase();
return normalized === MimeTypeImage.HEIC || normalized === MimeTypeImage.HEIF;
}
|