Transformers
Safetensors
English
esmfold2
biology
esm
protein
protein-structure-prediction
structure-prediction
protein-design
3d-structure
confidence-estimation
molecular-dynamics
Instructions to use biohub/ESMFold2-Fast with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use biohub/ESMFold2-Fast with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("biohub/ESMFold2-Fast", dtype="auto") - Notebooks
- Google Colab
- Kaggle
| { | |
| "architectures": [ | |
| "ESMFold2Model" | |
| ], | |
| "confidence_head": { | |
| "distogram_bins": 39, | |
| "enabled": true, | |
| "folding_trunk": { | |
| "dropout": 0.25, | |
| "n_heads": 8, | |
| "n_layers": 4 | |
| }, | |
| "max_dist": 50.75, | |
| "min_dist": 3.25, | |
| "num_pae_bins": 64, | |
| "num_pde_bins": 64, | |
| "num_plddt_bins": 50 | |
| }, | |
| "d_pair": 256, | |
| "d_single": 384, | |
| "disable_msa_features": false, | |
| "dtype": "float32", | |
| "esmc_id": "biohub/ESMC-6B", | |
| "folding_trunk": { | |
| "dropout": 0.25, | |
| "n_heads": 8, | |
| "n_layers": 24 | |
| }, | |
| "force_lm_dropout_during_inference": false, | |
| "inputs": { | |
| "atom_encoder": { | |
| "d_atom": 128, | |
| "d_token": 768, | |
| "expansion_ratio": 2, | |
| "n_blocks": 3, | |
| "n_heads": 4, | |
| "n_spatial_rope_pairs_per_axis": 2, | |
| "n_uid_rope_pairs": 10, | |
| "spatial_rope_base_frequency": 20.0, | |
| "swa_window_size": 128, | |
| "uid_rope_base_frequency": 10000.0 | |
| }, | |
| "d_inputs": 451 | |
| }, | |
| "lm_d_model": 2560, | |
| "lm_dropout": 0.0, | |
| "lm_encoder": { | |
| "enabled": true, | |
| "lm_dropout": 0.25, | |
| "n_layers": 4, | |
| "per_loop_lm_dropout": true | |
| }, | |
| "lm_num_layers": 80, | |
| "model_type": "esmfold2", | |
| "msa_encoder": { | |
| "d_hidden": 32, | |
| "d_msa": 128, | |
| "enabled": false, | |
| "msa_head_width": 32, | |
| "n_heads_msa": 8, | |
| "n_layers": 4 | |
| }, | |
| "msa_encoder_overwrite": true, | |
| "n_relative_chain_bins": 2, | |
| "n_relative_residx_bins": 32, | |
| "num_diffusion_samples": 32, | |
| "num_loops": 3, | |
| "parcae": { | |
| "coda_n_layers": 2, | |
| "enabled": true, | |
| "max_steps": 6, | |
| "min_steps": 1, | |
| "poisson_mean": 3.0 | |
| }, | |
| "structure_head": { | |
| "diffusion_module": { | |
| "atom_num_blocks": 3, | |
| "atom_num_heads": 4, | |
| "c_atom": 128, | |
| "c_s_inputs": 451, | |
| "c_token": 768, | |
| "c_z": 256, | |
| "fourier_dim": 256, | |
| "relpos_r_max": 32, | |
| "relpos_s_max": 2, | |
| "sigma_data": 16.0, | |
| "token_num_blocks": 12, | |
| "token_num_heads": 16, | |
| "transition_multiplier": 2 | |
| }, | |
| "distogram_bins": 64, | |
| "gamma_0": 0.8, | |
| "gamma_min": 1.0, | |
| "inference_num_steps": 14, | |
| "inference_p": 7.0, | |
| "inference_s_max": 160.0, | |
| "inference_s_min": 0.0004, | |
| "noise_scale": 1.003, | |
| "step_scale": 1.5, | |
| "train_noise_log_mean": -1.2, | |
| "train_noise_log_std": 1.5 | |
| }, | |
| "transformers_version": "4.57.6", | |
| "type": "release" | |
| } |