Instructions to use hf-internal-testing/tiny-random-FlaubertWithLMHeadModel with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-FlaubertWithLMHeadModel with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="hf-internal-testing/tiny-random-FlaubertWithLMHeadModel")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-FlaubertWithLMHeadModel") model = AutoModelForMaskedLM.from_pretrained("hf-internal-testing/tiny-random-FlaubertWithLMHeadModel") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- a76b0f78c311a298bdb1f34723de7d6a1161773df5c4f8c6874abbd260c0b432
- Size of remote file:
- 9.24 MB
- SHA256:
- e71f8a381e71b5694ee987a86b61bad4e996655a4ca846a95c8a0bb2f958f318
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