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@@ -92,7 +92,7 @@ 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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- ![image/png](https://cdn-uploads.huggingface.co/production/uploads/62f2bd3bdb7cbd214b658c48/NNmbupdIxuieuyjc8vMuj.png)
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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. ESM++ small is even faster than ESM2-35M with long sequences!
94
  The most gains will be seen with PyTorch > 2.5 on linux machines.
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+ ![image/png](https://cdn-uploads.huggingface.co/production/uploads/62f2bd3bdb7cbd214b658c48/GWm69UubVABjsmAjcuN6N.png)
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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.