Instructions to use AscoraDev/sitolub.molgen with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use AscoraDev/sitolub.molgen with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="AscoraDev/sitolub.molgen")# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("AscoraDev/sitolub.molgen") model = AutoModelForSeq2SeqLM.from_pretrained("AscoraDev/sitolub.molgen", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| { | |
| "cls_token": "[CLS]", | |
| "mask_token": "[MASK]", | |
| "model_max_length": 512, | |
| "name_or_path": "/Users/indrapriyadarsinis/Desktop/Indra/MVP_v1/downstream/rhizome/src/rhizome/modelstore/zinc22-16B-tokenizer", | |
| "pad_token": "[PAD]", | |
| "sep_token": "[SEP]", | |
| "special_tokens_map_file": "/u/indra/.cache/huggingface/transformers/410b9713b9ff338491c4439bb9c3426a17c79e66c5a02f0a729b5455a334037a.7da70648c6cb9951e284c9685f9ba7ae083dd59ed1d6d84bdfc0584a4ea94b6d", | |
| "tokenizer_class": "PreTrainedTokenizerFast", | |
| "unk_token": "[UNK]" | |
| } | |