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 spiece.model from ElnaggarLab/ankh3-large: direct link, hf CLI and curl.
- Browser
- Download file 238 kB
-
https://huggingface.co/ElnaggarLab/ankh3-large/resolve/main/spiece.model
- Command line
-
hf download hf://ElnaggarLab/ankh3-large/spiece.model
-
curl -L -o spiece.model https://huggingface.co/ElnaggarLab/ankh3-large/resolve/main/spiece.model
238 kB
- Xet hash:
- c3a3ea0b4fdca519cadb811e56140686c8768e5acddad82b5f4b0ceda9fb9fd9
- Size of remote file:
- 238 kB
- SHA256:
- f2b5e1bbd110b71ca9b2878e1fcd3265610076ecc97bd696e8a745c9bacc54e0
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