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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- a06a2e115c10eb6dcbf81eb4b14dedff9eea0d5c48a155b1e4efac9b906ee59a
- Size of remote file:
- 1.33 MB
- SHA256:
- c82ec98e1c1be3e56ccd77c99edb09494a8a87da38af1945660bf97797248cf3
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.