Instructions to use mgh6/esm_temp4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mgh6/esm_temp4 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="mgh6/esm_temp4")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("mgh6/esm_temp4") model = AutoModelForMaskedLM.from_pretrained("mgh6/esm_temp4", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from mgh6/esm_temp4: direct link, hf CLI and curl.
- Browser
- Download file 136 MB
-
https://huggingface.co/mgh6/esm_temp4/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://mgh6/esm_temp4/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/mgh6/esm_temp4/resolve/main/pytorch_model.bin
136 MB
- Xet hash:
- b630d488a13754812103fef0dbf142cd7de0d8f0c24d6291cf8b4b782ef3471a
- Size of remote file:
- 136 MB
- SHA256:
- 429bdfe77a5a0194524fd40164d3209443c856d67df6fb1f2d6a99fe49ad02d9
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