Instructions to use hf-internal-testing/tiny-random-XLMWithLMHeadModel with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-XLMWithLMHeadModel with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="hf-internal-testing/tiny-random-XLMWithLMHeadModel")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-XLMWithLMHeadModel") model = AutoModelForMaskedLM.from_pretrained("hf-internal-testing/tiny-random-XLMWithLMHeadModel") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- b4c9879cdde3749357b9343678734fbd621a441fd51b5c0d6f727566c567a6ea
- Size of remote file:
- 4.33 MB
- SHA256:
- b58dff1a2ac58d2d7b6e9f43227261c1ad3ec5f800f9043a845844a20de003ae
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