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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 38d67c4ed595b81eabfa6b6bc6bb5c68862c86219977210844a6e1c0ae7a66da
- Size of remote file:
- 8.26 MB
- SHA256:
- c08c605cfd54a19d3f7f48d8aa4e4cf111f5a8ac3629289ddc1a746bcf45afbf
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