Instructions to use lauraha/esm_mhc with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use lauraha/esm_mhc with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="lauraha/esm_mhc")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("lauraha/esm_mhc") model = AutoModelForMaskedLM.from_pretrained("lauraha/esm_mhc", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download optimizer.pt from lauraha/esm_mhc: direct link, hf CLI and curl.
- Browser
- Download file 1.19 GB
-
https://huggingface.co/lauraha/esm_mhc/resolve/main/optimizer.pt
- Command line
-
hf download hf://lauraha/esm_mhc/optimizer.pt
-
curl -L -o optimizer.pt https://huggingface.co/lauraha/esm_mhc/resolve/main/optimizer.pt
1.19 GB
- Xet hash:
- 680e514c63e3740278db7f03bb4b3f923cfb795a5fada1b13f13a68668701eb3
- Size of remote file:
- 1.19 GB
- SHA256:
- e14630e468b6cc230730502ed8aa0cfddb9bc090473e7038417cb6cfc7bd6e85
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