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")# pip install -U transformers accelerate # 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") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from ChatterjeeLab/PepMLM-650M: direct link, hf CLI and curl.
- Browser
- Download file 2.61 GB
-
https://huggingface.co/ChatterjeeLab/PepMLM-650M/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://ChatterjeeLab/PepMLM-650M/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/ChatterjeeLab/PepMLM-650M/resolve/main/pytorch_model.bin
2.61 GB
- Xet hash:
- b3a5e39ea0c87d6c32b3597d8e0a89de924386512a838846e92a37094d028d31
- Size of remote file:
- 2.61 GB
- SHA256:
- 8a3225bca1f9acd9f701ca2e46597c12bab92320e32b68f380ddf3b6d3b20770
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