Instructions to use multimolecule/esmc-600m with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MultiMolecule
How to use multimolecule/esmc-600m with MultiMolecule:
pip install multimolecule
from multimolecule import AutoModel, AutoTokenizer tokenizer = AutoTokenizer.from_pretrained("multimolecule/esmc-600m") model = AutoModel.from_pretrained("multimolecule/esmc-600m") inputs = tokenizer("Paris is the A of France.", return_tensors="pt") outputs = model(**inputs) embeddings = outputs.last_hidden_stateimport multimolecule from transformers import pipeline predictor = pipeline("fill-mask", model="multimolecule/esmc-600m") output = predictor("Paris is the <mask> of France.") - Notebooks
- Google Colab
- Kaggle
File size: 1,143 Bytes
29a2fc4 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 | {
"architectures": [
"EsmCForPreTraining"
],
"attention_bias": false,
"attention_dropout": 0.0,
"attention_layer_norm_bias": true,
"bos_token_id": 1,
"dtype": "float32",
"eos_token_id": 2,
"feedforward_bias": false,
"final_layer_norm_bias": false,
"head": null,
"hidden_act": "swiglu",
"hidden_dropout": 0.0,
"hidden_size": 1152,
"id2label": null,
"initializer_range": 0.02,
"intermediate_size": 3072,
"label2id": null,
"layer_norm_eps": 1e-05,
"lm_head": {
"act": null,
"bias": true,
"dropout": 0.0,
"hidden_size": null,
"layer_norm_eps": 1e-05,
"loss_weight": null,
"num_labels": 37,
"output_name": null,
"transform": "nonlinear",
"transform_act": "gelu"
},
"mask_token_id": 4,
"max_position_embeddings": 2048,
"model_type": "esmc",
"null_token_id": 5,
"num_attention_heads": 18,
"num_hidden_layers": 36,
"num_labels": 1,
"pad_token_id": 0,
"qk_layer_norm": true,
"qk_layer_norm_bias": false,
"residue_scaling_factor": 1.0,
"tie_word_embeddings": false,
"transformers_version": "5.7.0",
"unk_token_id": 3,
"vocab_size": 37
}
|