Instructions to use Synthyra/ESMplusplus_small with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Synthyra/ESMplusplus_small with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="Synthyra/ESMplusplus_small", trust_remote_code=True)# Load model directly from transformers import AutoModelForMaskedLM model = AutoModelForMaskedLM.from_pretrained("Synthyra/ESMplusplus_small", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
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README.md
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@@ -90,9 +90,9 @@ The plot below showcases performance normalized between the negative control (ra
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## Inference speeds
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We look at various ESM models and their throughput on an H100. Adding efficient batching between ESMC and ESM++ significantly improves the throughput. ESM++ small is even faster than ESM2-35M with long sequences!
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The most gains will be seen with PyTorch > 2.5 on linux machines.
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### Citation
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If you use any of this implementation or work please cite it (as well as the ESMC preprint). Bibtex for both coming soon.
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## Inference speeds
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We look at various ESM models and their throughput on an H100. Adding efficient batching between ESMC and ESM++ significantly improves the throughput, although ESM++ is also faster than ESMC for batch size one. ESM++ small is even faster than ESM2-35M with long sequences!
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The most gains will be seen with PyTorch > 2.5 on linux machines.
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### Citation
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If you use any of this implementation or work please cite it (as well as the ESMC preprint). Bibtex for both coming soon.
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