Instructions to use hf-tiny-model-private/tiny-random-XLMWithLMHeadModel 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-XLMWithLMHeadModel with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="hf-tiny-model-private/tiny-random-XLMWithLMHeadModel")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("hf-tiny-model-private/tiny-random-XLMWithLMHeadModel") model = AutoModelForMaskedLM.from_pretrained("hf-tiny-model-private/tiny-random-XLMWithLMHeadModel") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 5474ddf461b53e9abf0ec40dcbfad5bc5349632e687ac36d360d7ab360120297
- Size of remote file:
- 4.31 MB
- SHA256:
- 8a687d627f94247e3384fb861f16868a5ff0257a4e8d10db990c8822e1b7d4bb
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