Instructions to use google/muril-large-cased with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use google/muril-large-cased with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="google/muril-large-cased")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("google/muril-large-cased") model = AutoModel.from_pretrained("google/muril-large-cased", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from google/muril-large-cased: direct link, hf CLI and curl.
- Browser
- Download file 2.03 GB
-
https://huggingface.co/google/muril-large-cased/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://google/muril-large-cased/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/google/muril-large-cased/resolve/main/pytorch_model.bin
2.03 GB
- Xet hash:
- bc06154b226e7caa9026c780af01145871997aba350b0fb6dc601e4750056f24
- Size of remote file:
- 2.03 GB
- SHA256:
- 1a3bb993f09d053d285b7f301ddeef32934db4038593c7fc9eaa02c6057b27d0
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