diff --git a/docs/source/en/perf_infer_cpu.md b/docs/source/en/perf_infer_cpu.md
index c0e017c020870e..06b50f0b15e10d 100644
--- a/docs/source/en/perf_infer_cpu.md
+++ b/docs/source/en/perf_infer_cpu.md
@@ -42,7 +42,6 @@ Enable BetterTransformer with the [`PreTrainedModel.to_bettertransformer`] metho
 from transformers import AutoModelForCausalLM
 
 model = AutoModelForCausalLM.from_pretrained("bigcode/starcoder")
-model.to_bettertransformer()
 ```
 
 ## TorchScript
@@ -54,7 +53,7 @@ For a gentle introduction to TorchScript, see the [Introduction to PyTorch Torch
 With the [`Trainer`] class, you can enable JIT mode for CPU inference by setting the `--jit_mode_eval` flag:
 
 ```bash
-python run_qa.py \
+python examples/pytorch/question-answering/run_qa.py \
 --model_name_or_path csarron/bert-base-uncased-squad-v1 \
 --dataset_name squad \
 --do_eval \
@@ -86,7 +85,7 @@ pip install intel_extension_for_pytorch
 Set the `--use_ipex` and `--jit_mode_eval` flags in the [`Trainer`] class to enable JIT mode with the graph optimizations:
 
 ```bash
-python run_qa.py \
+python examples/pytorch/question-answering/run_qa.py \
 --model_name_or_path csarron/bert-base-uncased-squad-v1 \
 --dataset_name squad \
 --do_eval \
diff --git a/docs/source/en/perf_train_cpu.md b/docs/source/en/perf_train_cpu.md
index 7ef98932d537ac..ab2f735ecbdd50 100644
--- a/docs/source/en/perf_train_cpu.md
+++ b/docs/source/en/perf_train_cpu.md
@@ -51,7 +51,7 @@ To enable auto mixed precision with IPEX in Trainer, users should add `use_ipex`
 Take an example of the use cases on [Transformers question-answering](https://github.com/huggingface/transformers/tree/main/examples/pytorch/question-answering)
 
 - Training with IPEX using BF16 auto mixed precision on CPU:
-<pre> python run_qa.py \
+<pre> python examples/pytorch/question-answering/run_qa.py \
 --model_name_or_path google-bert/bert-base-uncased \
 --dataset_name squad \
 --do_train \
diff --git a/docs/source/en/perf_train_cpu_many.md b/docs/source/en/perf_train_cpu_many.md
index ed782caca3b1f1..d6a029c471de08 100644
--- a/docs/source/en/perf_train_cpu_many.md
+++ b/docs/source/en/perf_train_cpu_many.md
@@ -75,7 +75,7 @@ The following command enables training with 2 processes on one Xeon node, with o
  export CCL_WORKER_COUNT=1
  export MASTER_ADDR=127.0.0.1
  mpirun -n 2 -genv OMP_NUM_THREADS=23 \
- python3 run_qa.py \
+ python3 examples/pytorch/question-answering/run_qa.py \
  --model_name_or_path google-bert/bert-large-uncased \
  --dataset_name squad \
  --do_train \
@@ -104,7 +104,7 @@ Now, run the following command in node0 and **4DDP** will be enabled in node0 an
  export MASTER_ADDR=xxx.xxx.xxx.xxx #node0 ip
  mpirun -f hostfile -n 4 -ppn 2 \
  -genv OMP_NUM_THREADS=23 \
- python3 run_qa.py \
+ python3 examples/pytorch/question-answering/run_qa.py \
  --model_name_or_path google-bert/bert-large-uncased \
  --dataset_name squad \
  --do_train \