Instructions to use facebook/xmod-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use facebook/xmod-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="facebook/xmod-base")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("facebook/xmod-base") model = AutoModelForMaskedLM.from_pretrained("facebook/xmod-base", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from facebook/xmod-base: direct link, hf CLI and curl.
- Browser
- Download file 3.41 GB
-
https://huggingface.co/facebook/xmod-base/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://facebook/xmod-base/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/facebook/xmod-base/resolve/main/pytorch_model.bin
3.41 GB
- Xet hash:
- 7c5476540e5a09fe574b4e89c373d15ec963ecc2ec9697525de183bce6112cbc
- Size of remote file:
- 3.41 GB
- SHA256:
- 92baaaebc4fdd9888dba10d13187be424a922b5a062ad72b4eb5add4baee6292
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