Instructions to use ElnaggarLab/ankh-large-encoder with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ElnaggarLab/ankh-large-encoder with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("ElnaggarLab/ankh-large-encoder") model = AutoModel.from_pretrained("ElnaggarLab/ankh-large-encoder", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download flax_model.msgpack from ElnaggarLab/ankh-large-encoder: direct link, hf CLI and curl.
- Browser
- Download file 4.61 GB
-
https://huggingface.co/ElnaggarLab/ankh-large-encoder/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://ElnaggarLab/ankh-large-encoder/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/ElnaggarLab/ankh-large-encoder/resolve/main/flax_model.msgpack
4.61 GB
- Xet hash:
- 1cdcba1f50146e282bf91975ef8267f3366182bc13b97e0c83cbf21e9bbb4042
- Size of remote file:
- 4.61 GB
- SHA256:
- 7893ff1c586b7520b7bf7e08d63c951515fd678a426b8979e0aff179e347c1e8
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