refine readme (#536)

Author: WeiweiZhang1

README.md

@@ -317,6 +317,8 @@ in [Gaudi Guide](https://docs.habana.ai/en/latest/).
 
 #### Gaudi/CPU/XPU/CUDA
 
+**Please avoid manually moving the quantized model to a different device** (e.g., model.to('cpu')) during inference, as this may cause unexpected exceptions.
+
 ```python
 from transformers import AutoModelForCausalLM, AutoTokenizer
 from auto_round import AutoRoundConfig  ## must import for auto-round format
@@ -512,3 +514,4 @@ If you find AutoRound useful for your research, please cite our paper:
 
 
 
+

docs/step_by_step.md

@@ -138,7 +138,7 @@ output_dir = "./tmp_autoround"
 autoround.quantize_and_save(output_dir, format='auto_round') 
 ```
 
-#### mixed bits Usage
+#### Mixed bits Usage
 ```python
 from transformers import AutoModelForCausalLM, AutoTokenizer
 from auto_round import AutoRound
@@ -320,6 +320,7 @@ autoround.quantize_and_save(output_dir, format='gguf:q4_0') # gguf:q4_1
 
 AutoRound automatically selects the best available backend based on the installed libraries and prompts the user to install additional libraries when a better backend is found.
 
+**Please avoid manually moving the quantized model to a different device** (e.g., model.to('cpu')) during inference, as this may cause unexpected exceptions.
 
 ###  CPU
 
@@ -398,7 +399,8 @@ The backend may not always be the most suitable for certain devices.
 You can specify your preferred backend such as "ipex" for CPU and XPU, "marlin/exllamav2/triton" for CUDA, according to your needs or hardware compatibility. Please note that additional corresponding libraries may be required.
 
 ```python
-from transformers import AutoModelForCausalLM, AutoTokenizer, AutoRoundConfig
+from transformers import AutoModelForCausalLM, AutoTokenizer
+from auto_round import AutoRoundConfig
 
 model_name = "OPEA/Qwen2.5-1.5B-Instruct-int4-sym-inc"
 quantization_config = AutoRoundConfig(backend="ipex")
@@ -415,7 +417,8 @@ print(tokenizer.decode(model.generate(**inputs, max_new_tokens=50, do_sample=Fal
 Most GPTQ/AWQ models can be converted to the AutoRound format for better compatibility and support with Intel devices. Please note that the quantization config will be changed if the model is serialized.
 
 ```python
-from transformers import AutoModelForCausalLM, AutoTokenizer, AutoRoundConfig
+from transformers import AutoModelForCausalLM, AutoTokenizer
+from auto_round import AutoRoundConfig
 
 model_name = "ybelkada/opt-125m-gptq-4bit"
 quantization_config = AutoRoundConfig()