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:
- 4c7eeb96bac050f2341e88526c5bafa66c51774e27a0f424721d7ed1dd113aad
- Size of remote file:
- 9.26 MB
- SHA256:
- 1ede8beaeae80a51fb09785acba18e312741ebd2dac5b91fe448387fb7912748
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