Instructions to use saracandu/stldec_arch with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use saracandu/stldec_arch with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="saracandu/stldec_arch", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("saracandu/stldec_arch", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 6eaa7b9a663bec56f422b69df1065df0ecb44f64cb9d6675554792c2a1f350aa
- Size of remote file:
- 5.97 kB
- SHA256:
- 384783424ccfe07aa128538dd4a18c5b10d9d3120424651bda62088cf9eabf4b
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.