Instructions to use hf-tiny-model-private/tiny-random-LEDModel with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-tiny-model-private/tiny-random-LEDModel with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="hf-tiny-model-private/tiny-random-LEDModel")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("hf-tiny-model-private/tiny-random-LEDModel") model = AutoModel.from_pretrained("hf-tiny-model-private/tiny-random-LEDModel") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 28e9499b940f1bfcc1c917b939450ffb6bc810108a003e58da75f051ccd4771f
- Size of remote file:
- 1.23 MB
- SHA256:
- 99f606a64e61927dd8d8bafc3caed133da71c2d9c9cbe3d8dc4250e36aa54794
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