Update README.md

README.md

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 pipeline_tag: text-generation
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-[Phi4-mini](https://huggingface.co/microsoft/Phi-4-mini-instruct) is quantized with [torchao](https://huggingface.co/docs/transformers/main/en/quantization/torchao) with 8-bit embeddings, and 8-bit dynamic activation with int4 weights (8da4w) linears, by PyTorch team.
-You can export the quantized model to an [ExecuTorch](https://github.com/pytorch/executorch) pte file, or use the [quantized pte](https://huggingface.co/pytorch/Phi-4-mini-instruct-8da4w/blob/main/phi4-mini-8da4w.pte) file directly to run on a mobile device, see [Running in a mobile app](#running-in-a-mobile-app). 
+[Phi4-mini](https://huggingface.co/microsoft/Phi-4-mini-instruct) is quantized by the PyTorch team with [torchao](https://huggingface.co/docs/transformers/main/en/quantization/torchao) with 8-bit embeddings, and 8-bit dynamic activation with int4 weights (8da4w) linears.
+You can export the quantized model to an [ExecuTorch](https://github.com/pytorch/executorch) pte file, or use the [quantized pte](https://huggingface.co/pytorch/Phi-4-mini-instruct-8da4w/blob/main/phi4-mini-8da4w.pte) file directly to run on a mobile device. 
 
 # Running in a mobile app
 The [PTE file](https://huggingface.co/pytorch/Phi-4-mini-instruct-8da4w/blob/main/phi4-mini-8da4w.pte) 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.