Instructions to use Canstralian/RabbitRedux with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Canstralian/RabbitRedux with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Canstralian/RabbitRedux", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download requirements.txt from Canstralian/RabbitRedux: direct link, hf CLI and curl.
- Browser
- Download file 1.18 kB
-
https://huggingface.co/Canstralian/RabbitRedux/resolve/main/requirements.txt
- Command line
-
hf download hf://Canstralian/RabbitRedux/requirements.txt
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curl -L -o requirements.txt https://huggingface.co/Canstralian/RabbitRedux/resolve/main/requirements.txt
1.18 kB
| # Base libraries for Hugging Face models | |
| transformers==4.30.0 # Hugging Face Transformers library for model management | |
| torch==2.0.0 # PyTorch library for model training and inference | |
| einops==0.6.0 # Einops for tensor operations and manipulation | |
| # or for TensorFlow | |
| # tensorflow==2.12.0 # Uncomment if using TensorFlow | |
| # Other libraries commonly used in machine learning projects | |
| numpy==1.24.0 # NumPy for numerical operations | |
| pandas==1.5.3 # Pandas for data manipulation | |
| scikit-learn==1.2.0 # Scikit-learn for machine learning utilities | |
| datasets==2.12.0 # Hugging Face Datasets library for dataset handling | |
| # Add other libraries as needed | |
| # Web app dependencies | |
| fastapi==0.95.0 # FastAPI for building the API | |
| uvicorn==0.22.0 # Uvicorn for serving the FastAPI app | |
| pydantic==1.11.1 # Pydantic for data validation | |
| # Optional: For logging and monitoring (if used in your project) | |
| wandb==0.15.0 # Weights & Biases for experiment tracking | |