Instructions to use hf-tiny-model-private/tiny-random-FlaubertWithLMHeadModel 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-FlaubertWithLMHeadModel 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-FlaubertWithLMHeadModel")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("hf-tiny-model-private/tiny-random-FlaubertWithLMHeadModel") model = AutoModelForMaskedLM.from_pretrained("hf-tiny-model-private/tiny-random-FlaubertWithLMHeadModel", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 31edc0e57a065815dd79f16d253e02ad132f4bfeece3d3d9d6ccc099adf6d38c
- Size of remote file:
- 18 MB
- SHA256:
- c195bc0ef574bf0180bd9aff90b4cc87afd8aff78db823dd0b3644c4a80426ab
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