Update README.md

README.md

@@ -209,14 +209,16 @@ lm_eval --model hf --model_args pretrained=pytorch/Qwen3-4B-INT8-INT4 --tasks mm
 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.
 
-We first convert the [quantized checkpoint](https://huggingface.co/pytorch/Qwen3-4B-INT8-INT4/blob/main/pytorch_model.bin) to one ExecuTorch's LLM export script expects by renaming some of the checkpoint keys.
+ExecuTorch's LLM export scripts require the checkpoint keys and parameters have certain names, which differ from those used in Hugging Face.
+So we first use a script that converts the Hugging Face checkpoint key names to ones that ExecuTorch expects:
 The following script does this for you.
 ```Shell
 python -m executorch.examples.models.qwen3.convert_weights $(hf download pytorch/Qwen3-4B-INT8-INT4) pytorch_model_converted.bin
 ```
 
-Once the checkpoint is converted, we can export to ExecuTorch's pte format with the XNNPACK delegate.
-The below command exports with a max_seq_length/max_context_length of 1024, but it can be changed as desired.
+Once we have the checkpoint, we export it to ExecuTorch with a max_seq_length/max_context_length of 1024 to the XNNPACK backend as follows. 
+
+(Note: ExecuTorch LLM export script requires config.json have certain key names. The correct config to use for the LLM export script is located at examples/models/qwen3/config/4b_config.json within the ExecuTorch repo.)
 
 ```Shell
 python -m executorch.examples.models.llama.export_llama \
@@ -236,6 +238,8 @@ python -m executorch.examples.models.llama.export_llama \
 
 After that you can run the model in a mobile app (see [Running in a mobile app](#running-in-a-mobile-app)).
 
+(We try to keep these instructions up-to-date, but if you find they do not work, check out our [CI test in ExecuTorch](https://github.com/pytorch/executorch/blob/main/.ci/scripts/test_torchao_huggingface_checkpoints.sh) for the latest source of truth, and let us know we need to update our model card.)
+
 # Paper: TorchAO: PyTorch-Native Training-to-Serving Model Optimization
 The model's quantization is powered by **TorchAO**, a framework presented in the paper [TorchAO: PyTorch-Native Training-to-Serving Model Optimization](https://huggingface.co/papers/2507.16099).