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
Upload config(1).json
Browse files- config(1).json +29 -0
config(1).json
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{
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"architectures": [
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"EsmForMaskedLM"
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],
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"attention_probs_dropout_prob": 0.0,
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"classifier_dropout": null,
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"emb_layer_norm_before": false,
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"esmfold_config": null,
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"hidden_act": "gelu",
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"hidden_dropout_prob": 0.0,
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"hidden_size": 640,
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"initializer_range": 0.02,
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"intermediate_size": 2560,
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"is_folding_model": false,
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"layer_norm_eps": 1e-05,
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"mask_token_id": 32,
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"max_position_embeddings": 1026,
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"model_type": "esm",
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"num_attention_heads": 20,
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"num_hidden_layers": 30,
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"pad_token_id": 1,
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"position_embedding_type": "rotary",
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"token_dropout": true,
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"torch_dtype": "float32",
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"transformers_version": "4.54.1",
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"use_cache": true,
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"vocab_list": null,
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"vocab_size": 33
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}
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