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
| #!/usr/bin/env python3 | |
| from huggingface_hub import HfApi | |
| import argparse | |
| import os | |
| import sys | |
| def create_collection(title, description, private=False, namespace=None, return_slug=False): | |
| """ | |
| Create a new collection on Hugging Face | |
| Args: | |
| title: Collection title | |
| description: Collection description | |
| private: Whether the collection should be private (default: False) | |
| namespace: Optional namespace (defaults to your username) | |
| Returns: | |
| Collection object if successful, None if failed | |
| """ | |
| # Check if HF_TOKEN is available | |
| token = os.getenv("HF_TOKEN") or os.getenv("HUGGINGFACE_HUB_TOKEN") | |
| if not token: | |
| print("β No HF_TOKEN or HUGGINGFACE_HUB_TOKEN found in environment variables") | |
| print("Please set your Hugging Face token as an environment variable") | |
| return None | |
| # Initialize API | |
| api = HfApi() | |
| try: | |
| # Test authentication first | |
| user_info = api.whoami() | |
| if not return_slug: | |
| print(f"β Authenticated as: {user_info['name']}") | |
| # Create the collection | |
| if not return_slug: | |
| print(f"π Creating collection: '{title}'...") | |
| collection = api.create_collection( | |
| title=title, | |
| description=description, | |
| private=private, | |
| namespace=namespace | |
| ) | |
| if not return_slug: | |
| print(f"β Collection created successfully!") | |
| print(f"π Collection slug: {collection.slug}") | |
| print(f"π Collection URL: https://huggingface.co/collections/{collection.slug}") | |
| return collection | |
| except Exception as e: | |
| print(f"β Error creating collection: {e}") | |
| return None | |
| def main(): | |
| # This script requires that the environment variable HF_TOKEN is set with your | |
| # Hugging Face API token. | |
| api = HfApi() | |
| parser = argparse.ArgumentParser(description='Create a Huggingface Collection') | |
| parser.add_argument('--name', '-n', help='The name/title of the Collection', required=True) | |
| parser.add_argument('--description', '-d', help='The description for the Collection', required=True) | |
| parser.add_argument('--namespace', '-ns', help='The namespace to add the Collection to', required=True) | |
| parser.add_argument('--private', '-p', help='Create a private Collection', action='store_true') # Fixed | |
| parser.add_argument('--return-slug', '-s', help='Only output the collection slug', action='store_true') # Fixed | |
| args = parser.parse_args() | |
| name = args.name | |
| description = args.description | |
| private = args.private | |
| namespace = args.namespace | |
| return_slug = args.return_slug | |
| if not return_slug: | |
| print("π Creating Hugging Face Collection") | |
| print(f"Title: {name}") | |
| print(f"Description: {description}") | |
| print(f"Namespace: {namespace}") | |
| print(f"Private: {private}") | |
| collection = create_collection( | |
| title=name, | |
| description=description, | |
| private=private, | |
| namespace=namespace, | |
| return_slug=return_slug | |
| ) | |
| if collection: | |
| if return_slug: | |
| print(collection.slug) | |
| else: | |
| print("\nπ Collection created successfully!") | |
| print(f"Use this slug to add models: {collection.slug}") | |
| else: | |
| print("\nβ Failed to create collection") | |
| sys.exit(1) | |
| if __name__ == "__main__": | |
| main() | |