Instructions to use Haadini/xlm-large with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Haadini/xlm-large with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="Haadini/xlm-large")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("Haadini/xlm-large") model = AutoModelForMaskedLM.from_pretrained("Haadini/xlm-large", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from Haadini/xlm-large: direct link, hf CLI and curl.
- Browser
- Download file 17.1 MB
-
https://huggingface.co/Haadini/xlm-large/resolve/main/tokenizer.json
- Command line
-
hf download hf://Haadini/xlm-large/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/Haadini/xlm-large/resolve/main/tokenizer.json
17.1 MB
- Xet hash:
- ed991bc2bd1bacc1c8aa298083d9dc94d37e203962c88abb61a42c7ccf9a7320
- Size of remote file:
- 17.1 MB
- SHA256:
- a66380b0cac93d581b73339cbe9e269ed9f07af40cd8c0bde173eea9f200e4ab
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