README
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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README
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 |