Instructions to use omergoldman/first-pancake with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use omergoldman/first-pancake with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="omergoldman/first-pancake")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("omergoldman/first-pancake") model = AutoModel.from_pretrained("omergoldman/first-pancake", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- cf8f2f4f4c870ab196f7204ae57a1e9d19ef34361d699b770a7c142dfe3bb548
- Size of remote file:
- 4.73 GB
- SHA256:
- 45c1429e538fe05eca0d08a562535b7846ad6e5305db61b44183be30628a77e9
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