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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 1cf62c777bea3dbacf7f5ae321293465f44ecb27affd3e9adc56ff2001d2b432
- Size of remote file:
- 1.25 MB
- SHA256:
- 0fec9452b788744a74c913cb936bc3b4a81369541d5aa7bb60ed4322ade97fe7
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