Instructions to use ChatterjeeLab/PepMLM-650M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ChatterjeeLab/PepMLM-650M with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="ChatterjeeLab/PepMLM-650M")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("ChatterjeeLab/PepMLM-650M") model = AutoModelForMaskedLM.from_pretrained("ChatterjeeLab/PepMLM-650M", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
Download tokenizer_config.json from ChatterjeeLab/PepMLM-650M: direct link, hf CLI and curl.
- Browser
- Download file 135 Bytes
-
https://huggingface.co/ChatterjeeLab/PepMLM-650M/resolve/main/tokenizer_config.json
- Command line
-
hf download hf://ChatterjeeLab/PepMLM-650M/tokenizer_config.json
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curl -L -o tokenizer_config.json https://huggingface.co/ChatterjeeLab/PepMLM-650M/resolve/main/tokenizer_config.json
135 Bytes
| { | |
| "clean_up_tokenization_spaces": true, | |
| "model_max_length": 1000000000000000019884624838656, | |
| "tokenizer_class": "EsmTokenizer" | |
| } | |