Instructions to use gabrielbianchin/base_esm2_t6_long with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use gabrielbianchin/base_esm2_t6_long with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="gabrielbianchin/base_esm2_t6_long")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("gabrielbianchin/base_esm2_t6_long") model = AutoModelForMaskedLM.from_pretrained("gabrielbianchin/base_esm2_t6_long", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 60d3c3748c4ac30a71e7b71842a43e6f2d00666d359d57c8e89958baef5e9206
- Size of remote file:
- 40.1 MB
- SHA256:
- 91d00f65068a4d2a195ca7408f2cbd763d532ab5bb2e612eb1674e8273eb2935
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