Instructions to use UdS-LSV/smole-bert-guacamol-33 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use UdS-LSV/smole-bert-guacamol-33 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="UdS-LSV/smole-bert-guacamol-33")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("UdS-LSV/smole-bert-guacamol-33") model = AutoModel.from_pretrained("UdS-LSV/smole-bert-guacamol-33", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- f25f70e398b385a8f1f9a9ca51a42ce804064a8e190214f8111a980e792e91b3
- Size of remote file:
- 86.7 MB
- SHA256:
- 725299d94d9d5631cbca564898e3cb63e621e44a74003f25e3b1279f333dcab5
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