Update README

README.md

@@ -29,6 +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.
 - **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.
@@ -37,7 +38,7 @@ kernels support in-place attention during inference and training. They use
 4x and 5x less GPU memory than equivalent FastFold and stock PyTorch 
 implementations, respectively.
 - **Efficient alignment scripts** using the original AlphaFold HHblits/JackHMMER pipeline or [ColabFold](https://github.com/sokrypton/ColabFold)'s, which uses the faster MMseqs2 instead. We've used them to generate millions of alignments.
-- **Faster inference** on GPU for short chains.
+- **FlashAttention** support greatly speeds up MSA attention.
 
 ## Installation (Linux)
 
@@ -142,7 +143,7 @@ python3 run_pretrained_openfold.py \
     --hhsearch_binary_path lib/conda/envs/openfold_venv/bin/hhsearch \
     --kalign_binary_path lib/conda/envs/openfold_venv/bin/kalign
     --config_preset "model_1_ptm"
-    --openfold_checkpoint_path openfold/resources/openfold_params/finetuning_2_ptm.pt
+    --openfold_checkpoint_path openfold/resources/openfold_params/finetuning_ptm_2.pt
 ```
 
 where `data` is the same directory as in the previous step. If `jackhmmer`, 
@@ -172,6 +173,14 @@ variability, which may be undesirable for certain users. It is also recommended
 to disable this feature for very long chains (see below). To do so, set the 
 `tune_chunk_size` option in the config to `False`.
 
+For large-scale batch inference, we offer an optional tracing mode, which
+massively improves runtimes at the cost of a lengthy model compilation process.
+To enable it, add `--trace_model` to the inference command.
+
+By default, [FlashAttention](https://github.com/HazyResearch/flash-attention)
+is enabled in the config. This speeds up computation of the evoformer's MSA
+column attention module.
+
 Input FASTA files containing multiple sequences are treated as complexes. In
 this case, the inference script runs AlphaFold-Gap, a hack proposed
 [here](https://twitter.com/minkbaek/status/1417538291709071362?lang=en), using
@@ -395,7 +404,7 @@ python3 /opt/openfold/run_pretrained_openfold.py \
 --hhblits_binary_path /opt/conda/bin/hhblits \
 --hhsearch_binary_path /opt/conda/bin/hhsearch \
 --kalign_binary_path /opt/conda/bin/kalign \
---openfold_checkpoint_path /database/openfold_params/finetuning_2_ptm.pt
+--openfold_checkpoint_path /database/openfold_params/finetuning_ptm_2.pt
 ```
 
 ## Copyright notice