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:
- d370d5c4cafdd5e6244a7914e01fa8301e8436ff4d0013160fb158fdabf3ed5f
- Size of remote file:
- 9.26 MB
- SHA256:
- 3034bf6150d6ed3e5ce4272367b9d553fce6c2bc35c3ca7766807ac3da8b0351
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