Feature Extraction
Transformers
Safetensors
sdar
llama-factory
full
Generated from Trainer
custom_code
Instructions to use autoprogrammer/sdar_4b_random_mask-final with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use autoprogrammer/sdar_4b_random_mask-final with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="autoprogrammer/sdar_4b_random_mask-final", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("autoprogrammer/sdar_4b_random_mask-final", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 509fc0192179ace553fc474de7ce83a21e47dda426796ec36860c7d8e0cea8ca
- Size of remote file:
- 7.89 kB
- SHA256:
- 1aeccc5e63d4a2ae3131c6f6f2fcdb8a870f0c193b1ec1e2346f6359118895e0
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