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 training_args.bin from mgh6/esm_temp8: direct link, hf CLI and curl.
- Browser
- Download file 4.03 kB
-
https://huggingface.co/mgh6/esm_temp8/resolve/main/training_args.bin
- Command line
-
hf download hf://mgh6/esm_temp8/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/mgh6/esm_temp8/resolve/main/training_args.bin
4.03 kB
- Xet hash:
- c37a84884e3c3b89c7489191d0306ded4ef03b1066001dee716e685497a533c1
- Size of remote file:
- 4.03 kB
- SHA256:
- 0b94a01339cafb3bc75d7c96c68e93053062f5d211ad4950c9048e7f39cbe0c5
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