Instructions to use mgh6/esm_temp8 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mgh6/esm_temp8 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="mgh6/esm_temp8")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("mgh6/esm_temp8") model = AutoModelForMaskedLM.from_pretrained("mgh6/esm_temp8", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from mgh6/esm_temp8: direct link, hf CLI and curl.
- Browser
- Download file 136 MB
-
https://huggingface.co/mgh6/esm_temp8/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://mgh6/esm_temp8/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/mgh6/esm_temp8/resolve/main/pytorch_model.bin
136 MB
- Xet hash:
- 56c2505da04f03f74504b6abda628f85910b06a8d4128991b05a475721ec7e08
- Size of remote file:
- 136 MB
- SHA256:
- 6f49ca6c4ffd8530847d56415fe6a46c3f1c9aa66d7f39f8f5ffc9a8e3b18a15
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