@@ -29,7 +29,7 @@ vice versa (see `scripts/convert_of_weights_to_jax.py`).
OpenFold has the following advantages over the reference implementation:
-**Faster inference** on GPU, sometimes by as much as 2x. The greatest speedups are achieved on (>= Ampere) GPUs.
-**Faster inference** on GPU, sometimes by as much as 2x. The greatest speedups are achieved on Ampere or higher architecture GPUs.
-**Inference on extremely long chains**, made possible by our implementation of low-memory attention
([Rabe & Staats 2021](https://arxiv.org/pdf/2112.05682.pdf)). OpenFold can predict the structures of
sequences with more than 4000 residues on a single A100, and even longer ones with CPU offloading.
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@@ -233,7 +233,7 @@ at once. The `run_pretrained_openfold.py` script can enable this config option w
`--long_sequence_inference` command line option
#### Single-Sequence Model Inference
To run inference for a sequence using the single-sequence model, first you would need the ESM-1b embedding for the sequence. For this you need to set up the ESM model on your system ([ESM](https://www.github.com/facebookresearch/esm.git)). Once you have the the setup ready, use the following command in the ESM model directory to generate an embedding:
To run inference for a sequence using the single-sequence model, first you would need the ESM-1b embedding for the sequence. For this you need to set up the [ESM](https://www.github.com/facebookresearch/esm.git) model on your system. Once you have the the setup ready, use the following command in the ESM model directory to generate an embedding:
