Update README.md

README.md

@@ -38,9 +38,10 @@ uv pip install --pre --index-url https://download.pytorch.org/whl/nightly/cu126
 
 ## QAT Finetuning with PARQ
 
-We apply QAT with an optimizer-only package called [PARQ](https://github.com/pytorch/ao/tree/main/torchao/prototype/parq). The following script finetunes Phi-4-mini-instruct with 3-bit weight quantization and 4-bit embedding quantization using QAT, both at per-row granularity.
+We apply QAT with an optimizer-only package called [PARQ](https://github.com/pytorch/ao/tree/main/torchao/prototype/parq). The below script finetunes Phi-4-mini-instruct with 3-bit weight quantization and 4-bit embedding quantization using QAT, both at per-row granularity. Do the following before running it:
 
-First, fetch the training script with `curl -O https://huggingface.co/datasets/pytorch/parq-sft/resolve/main/qat_sft.py`. Set `dataset_name` to your desired dataset from the [HuggingFace datasets hub](https://huggingface.co/datasets) in addition to `max_steps`.
+1. `curl -O https://huggingface.co/datasets/pytorch/parq-sft/resolve/main/qat_sft.py`
+2. Set `dataset_name` to your desired dataset from the [HuggingFace datasets hub](https://huggingface.co/datasets) in addition to `max_steps`.
 
 ```bash
 source ~/.uv-hf/bin/activate