Instructions to use Synthyra/ESM2-8M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Synthyra/ESM2-8M with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="Synthyra/ESM2-8M", trust_remote_code=True)# Load model directly from transformers import AutoModelForMaskedLM model = AutoModelForMaskedLM.from_pretrained("Synthyra/ESM2-8M", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
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README.md
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# FastESM
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FastESM is a Huggingface compatible plug in version of ESM2 rewritten with a newer PyTorch attention implementation.
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# NOTE
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There was previously a bug with Huggingface weight tieing that caused the logits of FastESM to differ from ESM2. That bug is now resolved.
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# FastESM
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FastESM is a Huggingface compatible plug in version of ESM2 rewritten with a newer PyTorch attention implementation.
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