Instructions to use hf-internal-testing/tiny-random-MraForMaskedLM with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-MraForMaskedLM with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="hf-internal-testing/tiny-random-MraForMaskedLM")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-MraForMaskedLM") model = AutoModelForMaskedLM.from_pretrained("hf-internal-testing/tiny-random-MraForMaskedLM", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 5b4814244ea5cac535f18702e011b354b7524c2f51586ad76558645485f902be
- Size of remote file:
- 185 kB
- SHA256:
- 4f14414fe08cfdc3c170dca0b3a3130bc26a77a5687c1299e52e83c105bcf3fb
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.