Instructions to use MalyO2/working with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use MalyO2/working with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("object-detection", model="MalyO2/working")# Load model directly from transformers import AutoImageProcessor, AutoModelForObjectDetection processor = AutoImageProcessor.from_pretrained("MalyO2/working") model = AutoModelForObjectDetection.from_pretrained("MalyO2/working", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download model.safetensors from MalyO2/working: direct link, hf CLI and curl.
- Browser
- Download file 166 MB
-
https://huggingface.co/MalyO2/working/resolve/main/model.safetensors
- Command line
-
hf download hf://MalyO2/working/model.safetensors
-
curl -L -o model.safetensors https://huggingface.co/MalyO2/working/resolve/main/model.safetensors
166 MB
- Xet hash:
- 7290c0272832dab79667b53fc4378537cd5b63f48341d5f78d7794ce43af0d06
- Size of remote file:
- 166 MB
- SHA256:
- ccb0c3c0f41a6f430e1541f1d0afe379c258ea133ce097ed716e942558c9062d
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.