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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 66d417dc9868c6c952d20b7d2a26540a818af7d629b25dc8ebf784c624675f77
- Size of remote file:
- 18 MB
- SHA256:
- c86bdcc8a1e3f7c70aabd4f6c9210aa4329191225747a327d32f55d8b9c1067b
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