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
| import { MimeTypeImage } from '$lib/enums'; | |
| /** | |
| * Convert an SVG base64 data URL to a PNG data URL | |
| * @param base64UrlSvg - The SVG base64 data URL to convert | |
| * @param backgroundColor - Background color for the PNG (default: 'white') | |
| * @returns Promise resolving to PNG data URL | |
| */ | |
| export function svgBase64UrlToPngDataURL( | |
| base64UrlSvg: string, | |
| backgroundColor: string = 'white' | |
| ): Promise<string> { | |
| return new Promise((resolve, reject) => { | |
| try { | |
| const img = new Image(); | |
| img.onload = () => { | |
| const canvas = document.createElement('canvas'); | |
| const ctx = canvas.getContext('2d'); | |
| if (!ctx) { | |
| reject(new Error('Failed to get 2D canvas context.')); | |
| return; | |
| } | |
| const targetWidth = img.naturalWidth || 300; | |
| const targetHeight = img.naturalHeight || 300; | |
| canvas.width = targetWidth; | |
| canvas.height = targetHeight; | |
| if (backgroundColor) { | |
| ctx.fillStyle = backgroundColor; | |
| ctx.fillRect(0, 0, canvas.width, canvas.height); | |
| } | |
| ctx.drawImage(img, 0, 0, targetWidth, targetHeight); | |
| resolve(canvas.toDataURL(MimeTypeImage.PNG)); | |
| }; | |
| img.onerror = () => { | |
| reject(new Error('Failed to load SVG image. Ensure the SVG data is valid.')); | |
| }; | |
| img.src = base64UrlSvg; | |
| } catch (error) { | |
| const message = error instanceof Error ? error.message : String(error); | |
| const errorMessage = `Error converting SVG to PNG: ${message}`; | |
| console.error(errorMessage, error); | |
| reject(new Error(errorMessage)); | |
| } | |
| }); | |
| } | |
| /** | |
| * Check if a file is an SVG based on its MIME type | |
| * @param file - The file to check | |
| * @returns True if the file is an SVG | |
| */ | |
| export function isSvgFile(file: File): boolean { | |
| return file.type === MimeTypeImage.SVG; | |
| } | |
| /** | |
| * Check if a MIME type represents an SVG | |
| * @param mimeType - The MIME type to check | |
| * @returns True if the MIME type is image/svg+xml | |
| */ | |
| export function isSvgMimeType(mimeType: string): boolean { | |
| return mimeType === MimeTypeImage.SVG; | |
| } | |