Instructions to use Davlan/afro-xlmr-base-76L_script with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Davlan/afro-xlmr-base-76L_script with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="Davlan/afro-xlmr-base-76L_script")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("Davlan/afro-xlmr-base-76L_script") model = AutoModelForMaskedLM.from_pretrained("Davlan/afro-xlmr-base-76L_script", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from Davlan/afro-xlmr-base-76L_script: direct link, hf CLI and curl.
- Browser
- Download file 2.01 GB
-
https://huggingface.co/Davlan/afro-xlmr-base-76L_script/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://Davlan/afro-xlmr-base-76L_script/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/Davlan/afro-xlmr-base-76L_script/resolve/main/pytorch_model.bin
2.01 GB
- Xet hash:
- 82e9c8f478c84bb6636c1daac1fedc6d9df3253419008c9f0413dea75c9864d0
- Size of remote file:
- 2.01 GB
- SHA256:
- 46cfc51808ed5cfadc567787507be43176918c02dda58c304a1894ca4def2403
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