Instructions to use mgh6/esm_temp5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mgh6/esm_temp5 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="mgh6/esm_temp5")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("mgh6/esm_temp5") model = AutoModelForMaskedLM.from_pretrained("mgh6/esm_temp5", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from mgh6/esm_temp5: direct link, hf CLI and curl.
- Browser
- Download file 136 MB
-
https://huggingface.co/mgh6/esm_temp5/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://mgh6/esm_temp5/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/mgh6/esm_temp5/resolve/main/pytorch_model.bin
136 MB
- Xet hash:
- e3ab5d76dd9c7858aea3f6c6a4e2d7c78aceba0275b0c57143134a6756e21662
- Size of remote file:
- 136 MB
- SHA256:
- 4cafe9b5927bd3e8c7070a7300af9fb364a454e6df6ed4034e09cd4297ec1fe8
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