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:
- 4c0f331fddf8dc0cc4a8528ffd1df245ed62d219253e4d44366d07693c48c7d3
- Size of remote file:
- 1.33 MB
- SHA256:
- e74b420a22b790abd923323f7f811084a87572ba58b3c832e0961183f2a87649
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