Instructions to use Davlan/afro-xlmr-large-75L with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Davlan/afro-xlmr-large-75L with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="Davlan/afro-xlmr-large-75L")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("Davlan/afro-xlmr-large-75L") model = AutoModelForMaskedLM.from_pretrained("Davlan/afro-xlmr-large-75L", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from Davlan/afro-xlmr-large-75L: direct link, hf CLI and curl.
- Browser
- Download file 3.27 GB
-
https://huggingface.co/Davlan/afro-xlmr-large-75L/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://Davlan/afro-xlmr-large-75L/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/Davlan/afro-xlmr-large-75L/resolve/main/pytorch_model.bin
3.27 GB
- Xet hash:
- 2dd4f42f324056f448b99610e643aa5c9f64d9c5c2808f49b4b5fbb033d4aba9
- Size of remote file:
- 3.27 GB
- SHA256:
- 495d1756cb52df6a53c98d4f7abeed9eff1bdccaf8d46e551a90d4fc6c1282d9
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