Instructions to use hf-internal-testing/tiny-random-LEDModel with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-LEDModel with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="hf-internal-testing/tiny-random-LEDModel")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-LEDModel") model = AutoModel.from_pretrained("hf-internal-testing/tiny-random-LEDModel") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 03bcb494104a7fc65c5f23991a314dbce88ebbe01f484e646464e7f41dc69c26
- Size of remote file:
- 1.23 MB
- SHA256:
- bd13f43c6033e900f7b8b0acbab7f1e27507fd98326e9f1209c9f0c908f1433d
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