Instructions to use cis-lmu/glot500-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use cis-lmu/glot500-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="cis-lmu/glot500-base")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("cis-lmu/glot500-base") model = AutoModelForMaskedLM.from_pretrained("cis-lmu/glot500-base", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from cis-lmu/glot500-base: direct link, hf CLI and curl.
- Browser
- Download file 1.58 GB
-
https://huggingface.co/cis-lmu/glot500-base/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://cis-lmu/glot500-base/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/cis-lmu/glot500-base/resolve/main/pytorch_model.bin
1.58 GB
- Xet hash:
- fbc5090d1debfa851effde027704b5e427e219d62dcb517f03ec989243dfaf5f
- Size of remote file:
- 1.58 GB
- SHA256:
- 197e0e17c82e1f9fd70a7df3c2c18a0df098c1e2367ecceb9e96d3798e6bed6a
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