Instructions to use hf-internal-testing/tiny-random-XLMWithLMHeadModel with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-XLMWithLMHeadModel with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="hf-internal-testing/tiny-random-XLMWithLMHeadModel")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-XLMWithLMHeadModel") model = AutoModelForMaskedLM.from_pretrained("hf-internal-testing/tiny-random-XLMWithLMHeadModel") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 4a8a04d38a9e02f30fb41bc538b48ddb3258d72d9880580bd5288207d0db7e75
- Size of remote file:
- 4.31 MB
- SHA256:
- 4539d2cd627ec41aa2ee0b2ecfdb274b14d84ebee213d42360454fd3006fe067
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