Instructions to use ElnaggarLab/ankh3-large with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ElnaggarLab/ankh3-large with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="ElnaggarLab/ankh3-large")# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("ElnaggarLab/ankh3-large") model = AutoModelForSeq2SeqLM.from_pretrained("ElnaggarLab/ankh3-large", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from ElnaggarLab/ankh3-large: direct link, hf CLI and curl.
- Browser
- Download file 7.52 GB
-
https://huggingface.co/ElnaggarLab/ankh3-large/resolve/121bb511c8a8527bbe3a2246014753805518f4be/pytorch_model.bin
- Command line
-
hf download hf://ElnaggarLab/ankh3-large@121bb511c8a8527bbe3a2246014753805518f4be/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/ElnaggarLab/ankh3-large/resolve/121bb511c8a8527bbe3a2246014753805518f4be/pytorch_model.bin
7.52 GB
- Xet hash:
- 92ad61d57e3ac8d1a13ba9f287cbc32bffad1032cc6cce5b4056a4eb068a8ca0
- Size of remote file:
- 7.52 GB
- SHA256:
- 26321a345e07a25b21c6c41b651c4db91b420892e52c0dcbc55bd7a8f510f95b
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