Instructions to use hf-tiny-model-private/tiny-random-DistilBertModel 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-DistilBertModel 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-DistilBertModel")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("hf-tiny-model-private/tiny-random-DistilBertModel") model = AutoModel.from_pretrained("hf-tiny-model-private/tiny-random-DistilBertModel", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 55734fd8cf1afeccc0a7a840fc9faaba91af6bdc8e4d398e25d362b5256f86be
- Size of remote file:
- 372 kB
- SHA256:
- f08c33e2a56103382636e44f486f086fe7b20e66737918062db49e63217aa661
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