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:
- 0c8cf600d9c111a0bd4571158c84ec8124453fee63c0ae6401d9ed800ce3a14b
- Size of remote file:
- 8.99 MB
- SHA256:
- c5f973a31031923145ea7bb010448a8381cba8f0a844406df2a3a4ed7df8574e
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