Update README.md

README.md

@@ -31,7 +31,7 @@ On iPhone 15 Pro, the model runs at 17.3 tokens/sec and uses 3206 Mb of memory.
 # Quantization Recipe
 
 First need to install the required packages:
-```
+```Shell
 pip install git+https://github.com/huggingface/transformers@main
 pip install --pre torchao --index-url https://download.pytorch.org/whl/nightly/cu126
 ```
@@ -39,7 +39,7 @@ pip install --pre torchao --index-url https://download.pytorch.org/whl/nightly/c
 ## Untie Embedding Weights
 Before quantization, since we need quantize input embedding and unembedding (lm_head) layer which are tied, but we want to quantize them separately, we first need to untie the model:
 
-```
+```Py
 from transformers import (
   AutoModelForCausalLM,
   AutoProcessor,
@@ -73,7 +73,7 @@ tokenizer.push_to_hub(save_to)
 
 We used following code to get the quantized model:
 
-```
+```Py
 from transformers import (
   AutoModelForCausalLM,
   AutoProcessor,
@@ -156,12 +156,12 @@ We rely on [lm-evaluation-harness](https://github.com/EleutherAI/lm-evaluation-h
 Need to install lm-eval from source: https://github.com/EleutherAI/lm-evaluation-harness#install
 
 ## baseline
-```
+```Shell
 lm_eval --model hf --model_args pretrained=microsoft/Phi-4-mini-instruct --tasks hellaswag --device cuda:0 --batch_size 64
 ```
 
 ## int8 dynamic activation and int4 weight quantization (8da4w)
-```
+```Shell
 lm_eval --model hf --model_args pretrained=pytorch/Phi-4-mini-instruct-8da4w --tasks hellaswag --device cuda:0 --batch_size 64
 ```
 
@@ -195,13 +195,13 @@ Once ExecuTorch is [set-up](https://pytorch.org/executorch/main/getting-started.
 
 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.
-```
+```Shell
 python -m executorch.examples.models.phi_4_mini.convert_weights pytorch_model.bin phi4-mini-8da4w-converted.bin
 ```
 
 Once the checkpoint is converted, we can export to ExecuTorch's PTE format with the XNNPACK delegate.
 
-```
+```Shell
 PARAMS="executorch/examples/models/phi_4_mini/config.json"
 python -m executorch.examples.models.llama.export_llama \
   --model "phi_4_mini" \