Instructions to use Synthyra/ESMFold2-600 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Synthyra/ESMFold2-600 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="Synthyra/ESMFold2-600", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("Synthyra/ESMFold2-600", trust_remote_code=True) model = AutoModel.from_pretrained("Synthyra/ESMFold2-600", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Record the current runtime bundle identity for the embedded confidence release
Browse files- config.json +1 -1
config.json
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"fastplms_checkpoint_hash": "4e3baff4574120dfa58a25f34c910a05943e343ce63ad268775d2ce33b3b8c64",
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"fastplms_checkpoint_repo_id": "Synthyra/ESMFold2-600",
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"fastplms_model_id": "esmfold2_600",
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"fastplms_runtime_bundle_sha256": "
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"folding_trunk": {
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"dropout": 0.25,
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"n_heads": 8,
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"fastplms_checkpoint_hash": "4e3baff4574120dfa58a25f34c910a05943e343ce63ad268775d2ce33b3b8c64",
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"fastplms_checkpoint_repo_id": "Synthyra/ESMFold2-600",
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"fastplms_model_id": "esmfold2_600",
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"fastplms_runtime_bundle_sha256": "2ef7cccec8dfd129c6621d7bbb4b9f91a0ed1be1fd3f62d670259a4a9e199af0",
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"folding_trunk": {
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"dropout": 0.25,
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"n_heads": 8,
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