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 generation_config.json from ElnaggarLab/ankh3-large: direct link, hf CLI and curl.
- Browser
- Download file 142 Bytes
-
https://huggingface.co/ElnaggarLab/ankh3-large/resolve/4ba938beb8def1ac18f5ac58529e8273bf3f9e5c/generation_config.json
- Command line
-
hf download hf://ElnaggarLab/ankh3-large@4ba938beb8def1ac18f5ac58529e8273bf3f9e5c/generation_config.json
-
curl -L -o generation_config.json https://huggingface.co/ElnaggarLab/ankh3-large/resolve/4ba938beb8def1ac18f5ac58529e8273bf3f9e5c/generation_config.json
142 Bytes
| { | |
| "_from_model_config": true, | |
| "decoder_start_token_id": 0, | |
| "eos_token_id": 1, | |
| "pad_token_id": 0, | |
| "transformers_version": "4.26.0" | |
| } | |