Instructions to use hf-internal-testing/tiny-random-FlaubertModel with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-FlaubertModel with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="hf-internal-testing/tiny-random-FlaubertModel")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-FlaubertModel") model = AutoModel.from_pretrained("hf-internal-testing/tiny-random-FlaubertModel", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- abe417ba6133b481a78dcea3a91d6631b531343561030fb3f417273d3936449b
- Size of remote file:
- 9.06 MB
- SHA256:
- af5eeecbb1ad20135a83a5af808bb4261b8253043564f2cc3eef11e7f5d15b36
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