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
metadata
title: ESM2 Long Models
ESM2 Long
ESM2 Long is an adapted version of the ESM2 architectures. It uses local attention instead of global attention, allowing for models with longer input sizes. ESM2 Long models have a context size of 2,050, double that of the standard ESM2 model. Several ESM2 Long models are available:
| Model | Num layers |
|---|---|
| gabrielbianchin/esm2_t33_long | 33 |
| gabrielbianchin/esm2_t30_long | 30 |
| gabrielbianchin/esm2_t12_long | 12 |
| gabrielbianchin/esm2_t6_long | 6 |
For detailed information, please refer to the paper.