Instructions to use avichr/ar_hd with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use avichr/ar_hd with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="avichr/ar_hd")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("avichr/ar_hd") model = AutoModelForMaskedLM.from_pretrained("avichr/ar_hd", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from avichr/ar_hd: direct link, hf CLI and curl.
- Browser
- Download file 443 MB
-
https://huggingface.co/avichr/ar_hd/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://avichr/ar_hd/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/avichr/ar_hd/resolve/main/pytorch_model.bin
443 MB
- Xet hash:
- 036d4e4a151855f80cd391d8f65745474581ce4a164c9b4401379fa9f8a7112c
- Size of remote file:
- 443 MB
- SHA256:
- c6d02f375b6e362b3696e01bcb5ad4cf3c265eacc7822d8838b3d2104d7b54cb
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