Instructions to use gabrielbianchin/base_esm2_t30_long with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use gabrielbianchin/base_esm2_t30_long with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="gabrielbianchin/base_esm2_t30_long")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("gabrielbianchin/base_esm2_t30_long") model = AutoModelForMaskedLM.from_pretrained("gabrielbianchin/base_esm2_t30_long", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 99e79efb81d931f7e1bfdfeae098192ab83af61a63476942a4c82d0db9f31f7e
- Size of remote file:
- 746 MB
- SHA256:
- 81f69404b7862f39cd7ac4e4c8a93fb802d24bcdc6b21927f844a5dc8c180f7c
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