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:
- 3e7c6a18fefa22dbe4588522a576c66c6b43a1e60bf2b901915c39157a5987bb
- Size of remote file:
- 4.19 MB
- SHA256:
- 0ea6600329d30fb9b7c7f566466801093f59e8aaf463eb65b665eb002810529e
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