diff --git a/examples/corda_finetuning/README.md b/examples/corda_finetuning/README.md
index 9f5d0a8ff3..d39d2cc09e 100644
--- a/examples/corda_finetuning/README.md
+++ b/examples/corda_finetuning/README.md
@@ -150,7 +150,10 @@ corda_config = CordaConfig(
 ####  Knowledge-preserved adaptation mode
 
 ```bash
-CUDA_VISIBLE_DEVICES=0 python -u preprocess.py --model_id="meta-llama/Llama-2-7b-hf" \
+export CUDA_VISIBLE_DEVICES=0  # force to use device 0 of CUDA GPU
+export ZE_AFFINITY_MASK=0   # force to use device 0 of Intel XPU
+
+python -u preprocess.py --model_id="meta-llama/Llama-2-7b-hf" \
     --r 128 --seed 233 \
     --save_model --save_path {path_to_residual_model} \
     --calib_dataset "nqopen"
@@ -165,7 +168,10 @@ Arguments:
 #### Instruction-previewed adaptation mode
 
 ```bash
-CUDA_VISIBLE_DEVICES=0 python -u preprocess.py --model_id="meta-llama/Llama-2-7b-hf" \
+export CUDA_VISIBLE_DEVICES=0  # force to use device 0 of CUDA GPU
+export ZE_AFFINITY_MASK=0   # force to use device 0 of Intel XPU
+
+python -u preprocess.py --model_id="meta-llama/Llama-2-7b-hf" \
     --r 128 --seed 233 \
     --save_model --save_path {path_to_residual_model} \
     --first_eigen --calib_dataset "MetaMATH"
@@ -248,4 +254,4 @@ Note that this conversion is not supported if `rslora` is used in combination wi
   booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
   year={2024},
 }
-```
\ No newline at end of file
+```
diff --git a/examples/corda_finetuning/preprocess.py b/examples/corda_finetuning/preprocess.py
index 15bb18cb6b..765242f15e 100644
--- a/examples/corda_finetuning/preprocess.py
+++ b/examples/corda_finetuning/preprocess.py
@@ -38,8 +38,11 @@ def main(args):
     # Setting random seed of numpy and torch
     np.random.seed(args.seed)
     torch.manual_seed(args.seed)
-    torch.cuda.manual_seed_all(args.seed)
-    torch.backends.cudnn.deterministic = True
+    if torch.cuda.is_available():
+        torch.cuda.manual_seed_all(args.seed)
+    elif torch.xpu.is_available():
+        torch.xpu.manual_seed_all(args.seed)
+    torch.use_deterministic_algorithms(True)
 
     # Load model
     model_id = args.model_id