Image Feature Extraction
Transformers
Safetensors
PyTorch
English
eden
text-enhancement
grammar-correction
text-rewriting
encoder-decoder
transformer
custom_code
Instructions to use Rybib/EDEN with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Rybib/EDEN with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-feature-extraction", model="Rybib/EDEN", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Rybib/EDEN", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
| # Core runtime for using and training EDEN. | |
| torch>=2.1 | |
| transformers>=4.40 | |
| tokenizers>=0.15 | |
| safetensors>=0.4 | |
| numpy>=1.24 | |
| # Used by the training data pipeline and runtime safety checks. | |
| datasets>=2.16 | |
| tqdm>=4.66 | |
| psutil>=5.9 | |