Instructions to use mgh6/esm_temp6 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mgh6/esm_temp6 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="mgh6/esm_temp6")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("mgh6/esm_temp6") model = AutoModelForMaskedLM.from_pretrained("mgh6/esm_temp6", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from mgh6/esm_temp6: direct link, hf CLI and curl.
- Browser
- Download file 136 MB
-
https://huggingface.co/mgh6/esm_temp6/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://mgh6/esm_temp6/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/mgh6/esm_temp6/resolve/main/pytorch_model.bin
136 MB
- Xet hash:
- 1b8c27656c85d400bf1ef33cf4bd75f036f8691a8cbd8e0ac356f6b3e331ebc5
- Size of remote file:
- 136 MB
- SHA256:
- cad7a5c8a4ac2d0a8e3218118c4a912e5b8c8687993570fbb2f3f96007cbd558
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