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")# pip install -U transformers accelerate # 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 flax_model.msgpack from avichr/ar_hd: direct link, hf CLI and curl.
- Browser
- Download file 443 MB
-
https://huggingface.co/avichr/ar_hd/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://avichr/ar_hd/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/avichr/ar_hd/resolve/main/flax_model.msgpack
443 MB
- Xet hash:
- da9173d48c16f983992b55a5e93ca36e824b79b048ac2b1b11b39f98f4f80ee0
- Size of remote file:
- 443 MB
- SHA256:
- 839ddbcda048918e8a047c7b9d2f9c79c0bbf5de932829efd23d117fdbab5355
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