Instructions to use westlake-repl/SaProt_650M_AF2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use westlake-repl/SaProt_650M_AF2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="westlake-repl/SaProt_650M_AF2")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("westlake-repl/SaProt_650M_AF2") model = AutoModelForMaskedLM.from_pretrained("westlake-repl/SaProt_650M_AF2", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
File size: 647 Bytes
1b8cddd | 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 | {
"architectures": [
"EsmForMaskedLM"
],
"attention_probs_dropout_prob": 0.1,
"classifier_dropout": null,
"emb_layer_norm_before": false,
"hidden_act": "gelu",
"hidden_dropout_prob": 0.1,
"hidden_size": 1280,
"initializer_range": 0.02,
"intermediate_size": 5120,
"layer_norm_eps": 1e-05,
"mask_token_id": 4,
"max_position_embeddings": 1026,
"model_type": "esm",
"num_attention_heads": 20,
"num_hidden_layers": 33,
"pad_token_id": 1,
"position_embedding_type": "rotary",
"token_dropout": true,
"torch_dtype": "float32",
"transformers_version": "4.23.0.dev0",
"use_cache": true,
"vocab_size": 446
}
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