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 '$lib/enums'; | |
| import { HEIC_JPEG_QUALITY } from '$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; | |
| } | |