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 { isElementInViewport } from '$lib/utils/viewport'; | |
| /** | |
| * Svelte action that fades in an element when it enters the viewport. | |
| * Uses IntersectionObserver for efficient viewport detection. | |
| * | |
| * If skipIfVisible is set and the element is already visible in the viewport | |
| * when the action attaches (e.g. a markdown block promoted from unstable | |
| * during streaming), the fade is skipped entirely to avoid a flash. | |
| */ | |
| export function fadeInView( | |
| node: HTMLElement, | |
| options: { duration?: number; y?: number; delay?: number; skipIfVisible?: boolean } = {} | |
| ) { | |
| const { duration = 300, y = 0, delay = 0, skipIfVisible = false } = options; | |
| if (skipIfVisible && isElementInViewport(node)) { | |
| return; | |
| } | |
| node.style.opacity = '0'; | |
| node.style.transform = `translateY(${y}px)`; | |
| node.style.transition = `opacity ${duration}ms ease-out, transform ${duration}ms ease-out`; | |
| $effect(() => { | |
| const observer = new IntersectionObserver( | |
| (entries) => { | |
| for (const entry of entries) { | |
| if (entry.isIntersecting) { | |
| setTimeout(() => { | |
| requestAnimationFrame(() => { | |
| node.style.opacity = '1'; | |
| node.style.transform = 'translateY(0)'; | |
| }); | |
| }, delay); | |
| observer.disconnect(); | |
| } | |
| } | |
| }, | |
| { threshold: 0.05 } | |
| ); | |
| observer.observe(node); | |
| return () => { | |
| observer.disconnect(); | |
| }; | |
| }); | |
| } | |