Instructions to use facebook/esm2_t6_8M_UR50D with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use facebook/esm2_t6_8M_UR50D with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="facebook/esm2_t6_8M_UR50D")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("facebook/esm2_t6_8M_UR50D") model = AutoModelForMaskedLM.from_pretrained("facebook/esm2_t6_8M_UR50D", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from facebook/esm2_t6_8M_UR50D: direct link, hf CLI and curl.
- Browser
- Download file 31.4 MB
-
https://huggingface.co/facebook/esm2_t6_8M_UR50D/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://facebook/esm2_t6_8M_UR50D/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/facebook/esm2_t6_8M_UR50D/resolve/main/pytorch_model.bin
31.4 MB
- Xet hash:
- e8d16cc287bdf5fc2d3484974b8d589bcbab0eb03e7201d6a1c128f29e78da43
- Size of remote file:
- 31.4 MB
- SHA256:
- 9edcf393212f3a26684cd68ca8095ec43c2c341ee0fcc3ba7a4d3a47c5dc138f
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