Instructions to use facebook/esm2_t36_3B_UR50D with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use facebook/esm2_t36_3B_UR50D with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="facebook/esm2_t36_3B_UR50D")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("facebook/esm2_t36_3B_UR50D") model = AutoModelForMaskedLM.from_pretrained("facebook/esm2_t36_3B_UR50D") - Notebooks
- Google Colab
- Kaggle
ESM-2
ESM-2 is a state-of-the-art protein model trained on a masked language modelling objective. It is suitable for fine-tuning on a wide range of tasks that take protein sequences as input. For detailed information on the model architecture and training data, please refer to the accompanying paper. You may also be interested in some demo notebooks (PyTorch, TensorFlow) which demonstrate how to fine-tune ESM-2 models on your tasks of interest.
Several ESM-2 checkpoints are available in the Hub with varying sizes. Larger sizes generally have somewhat better accuracy, but require much more memory and time to train:
| Checkpoint name | Num layers | Num parameters |
|---|---|---|
| esm2_t48_15B_UR50D | 48 | 15B |
| esm2_t36_3B_UR50D | 36 | 3B |
| esm2_t33_650M_UR50D | 33 | 650M |
| esm2_t30_150M_UR50D | 30 | 150M |
| esm2_t12_35M_UR50D | 12 | 35M |
| esm2_t6_8M_UR50D | 6 | 8M |
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