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 training_args.bin from avichr/ar_hd: direct link, hf CLI and curl.
- Browser
- Download file 1.78 kB
-
https://huggingface.co/avichr/ar_hd/resolve/main/training_args.bin
- Command line
-
hf download hf://avichr/ar_hd/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/avichr/ar_hd/resolve/main/training_args.bin
1.78 kB
- Xet hash:
- 584ae6505ecc8a38c5e4f5bec2bc21b0e0b0c2a3467cfcb3819f71fdc82b4992
- Size of remote file:
- 1.78 kB
- SHA256:
- d9eb8f4f79dbd291036d34873d237d35a3d6df964413f6e750e64382addb3ef7
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