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
| 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](https://huggingface.co/gabrielbianchin/esm2_t33_long) | 33 | | |
| | [gabrielbianchin/esm2_t30_long](https://huggingface.co/gabrielbianchin/esm2_t30_long) | 30 | | |
| | [gabrielbianchin/esm2_t12_long](https://huggingface.co/gabrielbianchin/esm2_t12_long) | 12 | | |
| | [gabrielbianchin/esm2_t6_long](https://huggingface.co/gabrielbianchin/esm2_t6_long) | 6 | | |
| For detailed information, please refer to the paper. |