Transformers
Safetensors
English
esmfold2
biology
esm
protein
protein-structure-prediction
structure-prediction
protein-design
3d-structure
confidence-estimation
molecular-dynamics
custom_code
Instructions to use biohub/ESMFold2-Fast with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use biohub/ESMFold2-Fast with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("biohub/ESMFold2-Fast", trust_remote_code=True) model = AutoModel.from_pretrained("biohub/ESMFold2-Fast", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer_config.json from biohub/ESMFold2-Fast: direct link, hf CLI and curl.
- Browser
- Download file 327 Bytes
-
https://huggingface.co/biohub/ESMFold2-Fast/resolve/main/tokenizer_config.json
- Command line
-
hf download hf://biohub/ESMFold2-Fast/tokenizer_config.json
-
curl -L -o tokenizer_config.json https://huggingface.co/biohub/ESMFold2-Fast/resolve/main/tokenizer_config.json
327 Bytes
| { | |
| "backend": "tokenizers", | |
| "bos_token": "<cls>", | |
| "chain_break_token": "|", | |
| "cls_token": "<cls>", | |
| "eos_token": "<eos>", | |
| "extra_special_tokens": {}, | |
| "mask_token": "<mask>", | |
| "model_max_length": 1000000000000000019884624838656, | |
| "pad_token": "<pad>", | |
| "tokenizer_class": "EsmcTokenizer", | |
| "unk_token": "<unk>" | |
| } | |