diff --git a/engines/python/setup/djl_python/deepspeed.py b/engines/python/setup/djl_python/deepspeed.py
index da6e68b32..e28d0a777 100644
--- a/engines/python/setup/djl_python/deepspeed.py
+++ b/engines/python/setup/djl_python/deepspeed.py
@@ -330,7 +330,7 @@ def inference(self, inputs: Input):
             if self.task == "text-generation":
                 tokenized_inputs = self.tokenizer(
                     input_data, padding=True,
-                    return_tensors="pt").to(torch.cuda.current_device())
+                    return_tensors="pt").to(self.device)
                 with torch.no_grad():
                     output_tokens = self.model.generate(
                         input_ids=tokenized_inputs.input_ids,
diff --git a/engines/python/setup/djl_python/huggingface.py b/engines/python/setup/djl_python/huggingface.py
index 3d307aab5..99f89db80 100644
--- a/engines/python/setup/djl_python/huggingface.py
+++ b/engines/python/setup/djl_python/huggingface.py
@@ -281,7 +281,7 @@ def wrapped_pipeline(inputs, *args, **kwargs):
             tokenizer = hf_pipeline.tokenizer
             input_tokens = tokenizer(inputs, padding=True, return_tensors="pt")
             if self.device:
-                input_tokens.to(self.device)
+                input_tokens = input_tokens.to(self.device)
 
             with torch.no_grad():
                 output_tokens = model.generate(
diff --git a/engines/python/setup/djl_python/streaming_utils.py b/engines/python/setup/djl_python/streaming_utils.py
index c5e71960f..87241dffb 100644
--- a/engines/python/setup/djl_python/streaming_utils.py
+++ b/engines/python/setup/djl_python/streaming_utils.py
@@ -71,8 +71,8 @@ def use_hf_default_streamer(model, tokenizer, inputs, device, **kwargs):
         if not tokenizer.pad_token:
             tokenizer.pad_token = tokenizer.eos_token
         input_tokens = tokenizer(inputs, padding=True, return_tensors="pt")
-        if device:
-            input_tokens.to(device)
+        if device is not None:
+            input_tokens = input_tokens.to(device)
 
         streamer = HFStreamer(tokenizer, skip_special_token=True)
         generation_kwargs = dict(input_tokens, streamer=streamer, **kwargs)
@@ -117,8 +117,8 @@ def _hf_model_stream_generator(model, tokenizer, inputs, device, **kwargs):
                                     StreamingUtils.DEFAULT_MAX_NEW_TOKENS)
         tokenized_inputs = tokenizer(inputs, return_tensors="pt", padding=True)
         input_ids = tokenized_inputs["input_ids"]
-        if device:
-            input_ids.to(device)
+        if device is not None:
+            input_ids = input_ids.to(device)
 
         past_key_values = None
         decoding_method = StreamingUtils._get_decoding_method(**kwargs)
@@ -144,18 +144,19 @@ def _hf_model_stream_generator(model, tokenizer, inputs, device, **kwargs):
                            input_length] = 1 if is_pad_token_equal_to_eos_token else tokenized_inputs[
                                "attention_mask"]
             curr_length = input_length
-
-        if generic_model_class == "Seq2SeqLM":
+        elif generic_model_class == "Seq2SeqLM":
             attention_mask = tokenized_inputs["attention_mask"]
-            if device:
-                attention_mask.to(device)
-
             decoder_attention_mask = None
             encoder_last_hidden_state = None
             decoder_input_ids = torch.tensor(tokenizer.bos_token_id,
                                              device=device).repeat(
                                                  len(inputs)).view(-1, 1)
             all_decoder_input_ids = decoder_input_ids
+        else:
+            raise ValueError(f"Unsupported model class: {generic_model_class}")
+
+        if device is not None:
+            attention_mask = attention_mask.to(device)
 
         while True:
             if stop_generation: