Instructions to use AI4Protein/deep_bpe_1600 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use AI4Protein/deep_bpe_1600 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="AI4Protein/deep_bpe_1600")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("AI4Protein/deep_bpe_1600") model = AutoModelForMaskedLM.from_pretrained("AI4Protein/deep_bpe_1600", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from AI4Protein/deep_bpe_1600: direct link, hf CLI and curl.
- Browser
- Download file 348 MB
-
https://huggingface.co/AI4Protein/deep_bpe_1600/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://AI4Protein/deep_bpe_1600/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/AI4Protein/deep_bpe_1600/resolve/main/pytorch_model.bin
348 MB
- Xet hash:
- 634e2852daa44bbb96b849a6b01a2faf1223513f7dac7213d181e7aa3fd1407a
- Size of remote file:
- 348 MB
- SHA256:
- 7ce64d69bf034b0d6a80cdc1f73bab9c35672ea40a7845f2db7168607363c424
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