Instructions to use hf-internal-testing/tiny-random-SqueezeBertModel with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-SqueezeBertModel with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="hf-internal-testing/tiny-random-SqueezeBertModel")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-SqueezeBertModel") model = AutoModel.from_pretrained("hf-internal-testing/tiny-random-SqueezeBertModel", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 4166394ddcd1a8668efb53d3c309e408d869741aa39a0ea60b2d6b40fdfc7758
- Size of remote file:
- 345 kB
- SHA256:
- 1eec5f4d9d6bd51d4b7ff5444cb3600dabee89c2650ff7e49f0adc91a50fe76e
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