Instructions to use gabrielbianchin/esm2_t6_long with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use gabrielbianchin/esm2_t6_long with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="gabrielbianchin/esm2_t6_long")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("gabrielbianchin/esm2_t6_long") model = AutoModelForMaskedLM.from_pretrained("gabrielbianchin/esm2_t6_long", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- ab8f0fa6bb3b0897a4be2dd27d70a1065cc4f0a80257dbb44aae19fa93932dbb
- Size of remote file:
- 40.1 MB
- SHA256:
- 0ec68b646aa82bb68fd56e30c2a752bfd0886647783b688e8136567930fe3312
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