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 training_args.bin from mgh6/esm_temp5: direct link, hf CLI and curl.
- Browser
- Download file 4.03 kB
-
https://huggingface.co/mgh6/esm_temp5/resolve/main/training_args.bin
- Command line
-
hf download hf://mgh6/esm_temp5/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/mgh6/esm_temp5/resolve/main/training_args.bin
4.03 kB
- Xet hash:
- 06c2f18f4a0d7c5834855881062f2b5f00bc4e997a3b2be966531d3422524d53
- Size of remote file:
- 4.03 kB
- SHA256:
- 2f127dc8b3bed72189fd5c8fd306e8b03a27f32d1341ea5d1de172b9859da437
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