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:
- 4dd679e8ac16c3d852a8fd66312c5cfcf873e88f4d4066b4b25b927e757159dc
- Size of remote file:
- 8.26 MB
- SHA256:
- 639f2070f170bfb47d52b9a39a88d41966946e6c37e132a9a411d42fbd1f80e8
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