Instructions to use nvidia/esm2_t12_35M_UR50D with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use nvidia/esm2_t12_35M_UR50D with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="nvidia/esm2_t12_35M_UR50D", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoModelForMaskedLM model = AutoModelForMaskedLM.from_pretrained("nvidia/esm2_t12_35M_UR50D", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer_config.json from nvidia/esm2_t12_35M_UR50D: direct link, hf CLI and curl.
- Browser
- Download file 402 Bytes
-
https://huggingface.co/nvidia/esm2_t12_35M_UR50D/resolve/main/tokenizer_config.json
- Command line
-
hf download hf://nvidia/esm2_t12_35M_UR50D/tokenizer_config.json
-
curl -L -o tokenizer_config.json https://huggingface.co/nvidia/esm2_t12_35M_UR50D/resolve/main/tokenizer_config.json
402 Bytes
| { | |
| "backend": "tokenizers", | |
| "bos_token": "<cls>", | |
| "clean_up_tokenization_spaces": false, | |
| "cls_token": "<cls>", | |
| "eos_token": "<eos>", | |
| "is_local": true, | |
| "mask_token": "<mask>", | |
| "model_input_names": [ | |
| "input_ids", | |
| "attention_mask" | |
| ], | |
| "model_max_length": 1000000000000000019884624838656, | |
| "pad_token": "<pad>", | |
| "tokenizer_class": "TokenizersBackend", | |
| "unk_token": "<unk>" | |
| } | |