Instructions to use InstaDeepAI/IDP-ESM2-150M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use InstaDeepAI/IDP-ESM2-150M with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="InstaDeepAI/IDP-ESM2-150M")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("InstaDeepAI/IDP-ESM2-150M") model = AutoModelForMaskedLM.from_pretrained("InstaDeepAI/IDP-ESM2-150M") - Notebooks
- Google Colab
- Kaggle
File size: 718 Bytes
bf16897 | 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 | {
"architectures": [
"EsmForMaskedLM"
],
"attention_probs_dropout_prob": 0.0,
"classifier_dropout": null,
"emb_layer_norm_before": false,
"esmfold_config": null,
"hidden_act": "gelu",
"hidden_dropout_prob": 0.0,
"hidden_size": 640,
"initializer_range": 0.02,
"intermediate_size": 2560,
"is_folding_model": false,
"layer_norm_eps": 1e-05,
"mask_token_id": 32,
"max_position_embeddings": 1026,
"model_type": "esm",
"num_attention_heads": 20,
"num_hidden_layers": 30,
"pad_token_id": 1,
"position_embedding_type": "rotary",
"token_dropout": true,
"torch_dtype": "float32",
"transformers_version": "4.54.1",
"use_cache": true,
"vocab_list": null,
"vocab_size": 33
}
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