Instructions to use hf-tiny-model-private/tiny-random-XLMModel 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-XLMModel 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-XLMModel")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("hf-tiny-model-private/tiny-random-XLMModel") model = AutoModel.from_pretrained("hf-tiny-model-private/tiny-random-XLMModel") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 29043ca35addaccf4fa17aef678851e1c8e848d3c99b2b2da743e1c96f127c7f
- Size of remote file:
- 4.28 MB
- SHA256:
- 37bdfc34bd4a1f3320d7623b73bcef36783285597718e31a3186206968797769
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