Instructions to use hf-tiny-model-private/tiny-random-FlaubertModel 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-FlaubertModel 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-FlaubertModel")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("hf-tiny-model-private/tiny-random-FlaubertModel") model = AutoModel.from_pretrained("hf-tiny-model-private/tiny-random-FlaubertModel", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- ced977a3d3d299542ab508f8b76005716a5b891a3929d5b98366517873202a82
- Size of remote file:
- 9.06 MB
- SHA256:
- 21dc5f964f1a11f5b4c302c800374cb3fb3cda0db19503e314b4435f2ba26049
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