Instructions to use hf-tiny-model-private/tiny-random-SqueezeBertModel 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-SqueezeBertModel 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-SqueezeBertModel")# Load model directly from transformers import AutoTokenizer, AutoModelForMultimodalLM tokenizer = AutoTokenizer.from_pretrained("hf-tiny-model-private/tiny-random-SqueezeBertModel") model = AutoModelForMultimodalLM.from_pretrained("hf-tiny-model-private/tiny-random-SqueezeBertModel") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 4b6924c36a88c4a33136eb51a1534cbd168db0ace819cf00743c55dd04f51dd6
- Size of remote file:
- 345 kB
- SHA256:
- c440d0aaea17758556079243914281a049aca85d36de76e5c304b870dd51b75d
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