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 sys | |
| def add_model_to_collection(collection_slug, model_id, note=""): | |
| """ | |
| Add a model to an existing collection | |
| Args: | |
| collection_slug: The slug of the collection (e.g., "username/collection-name-12345") | |
| model_id: The model repository ID (e.g., "username/model-name") | |
| note: Optional note about the model | |
| Returns: | |
| True if successful, False if failed | |
| """ | |
| # Initialize API | |
| api = HfApi() | |
| try: | |
| user_info = api.whoami() | |
| print(f"β Authenticated as: {user_info['name']}") | |
| # Verify the model exists | |
| print(f"π Checking if model exists: {model_id}") | |
| try: | |
| model_info = api.model_info(model_id) | |
| except Exception as e: | |
| print(f"β Model not found or not accessible: {model_id}") | |
| print(f"Error: {e}") | |
| return False | |
| print(f"π Adding model to collection...") | |
| api.add_collection_item( | |
| collection_slug=collection_slug, | |
| item_id=model_id, | |
| item_type="model", | |
| note=note | |
| ) | |
| print(f"β Model added to collection successfully!") | |
| print(f"π Collection URL: https://huggingface.co/collections/{collection_slug}") | |
| return True | |
| except Exception as e: | |
| print(f"β Error adding model to collection: {e}") | |
| return False | |
| 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='Add model to a Huggingface Collection') | |
| parser.add_argument('--collection', '-c', help='The collection slug username/collection-hash', required=True) | |
| parser.add_argument('--model', '-m', help='The model to add to the Collection', required=True) | |
| parser.add_argument('--note', '-n', help='An optional note/description', required=False) | |
| args = parser.parse_args() | |
| collection = args.collection | |
| model = args.model | |
| note = args.note | |
| success = add_model_to_collection( | |
| collection_slug=collection, | |
| model_id=model, | |
| note=note | |
| ) | |
| if success: | |
| print("\nπ Model added successfully!") | |
| else: | |
| print("\nβ Failed to add model to collection") | |
| sys.exit(1) | |
| if __name__ == "__main__": | |
| main() | |