Update README.md

README.md

@@ -166,18 +166,14 @@ print(make_table(results))
 We can run the quantized model on a mobile phone using [ExecuTorch](https://github.com/pytorch/executorch).
 Once ExecuTorch is [set-up](https://pytorch.org/executorch/main/getting-started.html), exporting and running the model on device is a breeze.
 
-
-## Convert quantized checkpoint to ExecuTorch's format
-
 We first convert the quantized checkpoint to one ExecuTorch's LLM export script expects by renaming some of the checkpoint keys.
 The following script does this for you.
 ```
 python -m executorch.examples.models.phi_4_mini.convert_weights phi4-mini-8dq4w.bin phi4-mini-8dq4w-converted.bin
 ```
 
-Once the checkpoint is converted, we can export to ExecuTorch's PTE format.
+Once the checkpoint is converted, we can export to ExecuTorch's PTE format with the XNNPACK delegate.
 
-## Export to an ExecuTorch *.pte with XNNPACK
 ```
 PARAMS="executorch/examples/models/phi_4_mini/config.json"
 python -m executorch.examples.models.llama.export_llama \
@@ -192,7 +188,7 @@ python -m executorch.examples.models.llama.export_llama \
 ```
 
 ## Running in a mobile app
-The PTE file can be run with ExecuTorch.  See the [instructions](https://pytorch.org/executorch/main/llm/llama-demo-ios.html) for doing this in iOS.
+The PTE file can be run with ExecuTorch on a mobile phone.  See the [instructions](https://pytorch.org/executorch/main/llm/llama-demo-ios.html) for doing this in iOS.
 On iPhone 15 Pro, the model runs at 17.3 tokens/sec and uses 3206 Mb of memory. 
 
 ![image/png](https://cdn-uploads.huggingface.co/production/uploads/66049fc71116cebd1d3bdcf4/AEdAJjGK2lED7tr6seWGf.png)