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
Commit History
8 days ago
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Update FastPLMs files from Synthyra/FastPLMs 32d9951 (PR 52: ESMFold2 noise cap, DPLM2 parity)
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18 days ago
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Accept huggingface_hub's shared Xet blob store in the CCD loader (FastPLMs PR 51)
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24 days ago
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Sync current FastPLMs source, model cards and dependencies
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Embed evaluated confidence head and publish current training metrics
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Update FastPLMs files
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25 days ago
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Support verified live v2 confidence heads
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Apply coding standards from 1cb5747 (files only)
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Update FastPLMs runtime and cards from 328deef (files only)
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28 days ago
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Document the separately trained confidence head results
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Update FastPLMs runtime and model cards
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Record published checkpoint pins and validation status
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Publish ESMFold2 small backbone checkpoint with FastPLMs runtime
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