Feature Extraction
Transformers
PyTorch
Safetensors
English
magi
Manga
Object Detection
OCR
Clustering
Diarisation
custom_code
Instructions to use ragavsachdeva/magi with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ragavsachdeva/magi with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="ragavsachdeva/magi", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("ragavsachdeva/magi", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 2ad5e7209b3d00b0ffc5ecb7c3744cc99634d228f3cffa30abebcd8d7616d1e9
- Size of remote file:
- 2.06 GB
- SHA256:
- 219c2b80e741b1d02e92f22701a38358a5606d6460ad8b6335091e909b212011
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.