Update README.md

README.md

@@ -156,17 +156,7 @@ lm_eval --model hf --model_args pretrained=microsoft/Phi-4-mini-instruct --tasks
 
 ## int8 dynamic activation and int4 weight quantization (8da4w)
 ```
-import lm_eval
-from lm_eval import evaluator
-from lm_eval.utils import (
-    make_table,
-)
-
-lm_eval_model = lm_eval.models.huggingface.HFLM(pretrained=quantized_model, batch_size=64)
-results = evaluator.simple_evaluate(
-    lm_eval_model, tasks=["hellaswag"], device="cuda:0", batch_size="auto"
-)
-print(make_table(results))
+lm_eval --model hf --model_args pretrained=pytorch/Phi-4-mini-instruct-8da4w --tasks hellaswag --device cuda:0 --batch_size 64
 ```
 
 | Benchmark                        |             |                   |
@@ -199,7 +189,7 @@ Once ExecuTorch is [set-up](https://pytorch.org/executorch/main/getting-started.
 We first convert the quantized checkpoint to one ExecuTorch's LLM export script expects by renaming some of the checkpoint keys.
 The following script does this for you.
 ```
-python -m executorch.examples.models.phi_4_mini.convert_weights phi4-mini-8dq4w.bin phi4-mini-8dq4w-converted.bin
+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.