Update README.md

README.md

@@ -210,9 +210,9 @@ We can run the quantized model on a mobile phone using [ExecuTorch](https://gith
 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/Phi-4-mini-instruct-8da4w/blob/main/pytorch_model.bin) to one ExecuTorch's LLM export script expects by renaming some of the checkpoint keys.
-The following script does this for you.  We have uploaded the converted checkpoint [phi4-mini-8da4w-converted.bin](https://huggingface.co/pytorch/Phi-4-mini-instruct-8da4w/blob/main/phi4-mini-8da4w-converted.bin) for convenience.
+The following script does this for you.  We have uploaded the converted checkpoint [pytorch_model_converted.bin](https://huggingface.co/pytorch/Phi-4-mini-instruct-8da4w/blob/main/pytorch_model_converted.bin) for convenience.
 ```Shell
-python -m executorch.examples.models.phi_4_mini.convert_weights pytorch_model.bin phi4-mini-8da4w-converted.bin
+python -m executorch.examples.models.phi_4_mini.convert_weights pytorch_model.bin pytorch_model_converted.bin
 ```
 
 Once the checkpoint is converted, we can export to ExecuTorch's pte format with the XNNPACK delegate.
@@ -222,7 +222,7 @@ The below command exports with a max_seq_length/max_context_length of 128, the d
 PARAMS="executorch/examples/models/phi_4_mini/config.json"
 python -m executorch.examples.models.llama.export_llama \
   --model "phi_4_mini" \
-  --checkpoint "phi4-mini-8da4w-converted.bin" \
+  --checkpoint "pytorch_model_converted.bin" \
   --params "$PARAMS" \
   -kv \
   --use_sdpa_with_kv_cache \