Update GPU HF-Transformers example structure (#11526)

docker/llm/inference/xpu/docker/Dockerfile

@@ -53,7 +53,7 @@ RUN wget -O- https://apt.repos.intel.com/intel-gpg-keys/GPG-PUB-KEY-INTEL-SW-PRO
     # Download all-in-one benchmark and examples
     git clone https://github.com/intel-analytics/ipex-llm && \
     cp -r ./ipex-llm/python/llm/dev/benchmark/ ./benchmark && \
-    cp -r ./ipex-llm/python/llm/example/GPU/HF-Transformers-AutoModels/Model ./examples && \
+    cp -r ./ipex-llm/python/llm/example/GPU/HuggingFace/LLM ./examples && \
     # Install vllm dependencies
     pip install --upgrade fastapi && \
     pip install --upgrade "uvicorn[standard]" && \

docs/mddocs/DockerGuides/docker_run_pytorch_inference_in_vscode.md

@@ -94,7 +94,7 @@ Start ipex-llm-xpu Docker Container. Choose one of the following commands to sta
 
 Press F1 to bring up the Command Palette and type in `Dev Containers: Attach to Running Container...` and select it and then select `my_container`
 
-Now you are in a running Docker Container, Open folder `/ipex-llm/python/llm/example/GPU/HF-Transformers-AutoModels/Model/`.
+Now you are in a running Docker Container, Open folder `/ipex-llm/python/llm/example/GPU/HuggingFace/LLM`.
 
 <a href="https://llm-assets.readthedocs.io/en/latest/_images/run_example_in_vscode.gif" target="_blank">
   <img src="https://llm-assets.readthedocs.io/en/latest/_images/run_example_in_vscode.gif" width=100%; />

docs/mddocs/Overview/FAQ/faq.md

@@ -4,7 +4,7 @@
 
 ### GGUF format usage with IPEX-LLM?
 
-IPEX-LLM supports running GGUF/AWQ/GPTQ models on both [CPU](https://github.com/intel-analytics/ipex-llm/tree/main/python/llm/example/CPU/HF-Transformers-AutoModels/Advanced-Quantizations) and [GPU](https://github.com/intel-analytics/ipex-llm/tree/main/python/llm/example/GPU/HF-Transformers-AutoModels/Advanced-Quantizations).
+IPEX-LLM supports running GGUF/AWQ/GPTQ models on both [CPU](https://github.com/intel-analytics/ipex-llm/tree/main/python/llm/example/CPU/HF-Transformers-AutoModels/Advanced-Quantizations) and [GPU](https://github.com/intel-analytics/ipex-llm/tree/main/python/llm/example/GPU/HuggingFace/Advanced-Quantizations).
 
 Please also refer to [here](https://github.com/intel-analytics/ipex-llm?tab=readme-ov-file#latest-update-) for our latest support.
 

docs/mddocs/Overview/KeyFeatures/hugging_face_format.md

@@ -23,7 +23,7 @@ output = tokenizer.batch_decode(output_ids)
 ```
 
 > [!TIP]
-> See the complete CPU examples [here](https://github.com/intel-analytics/ipex-llm/tree/main/python/llm/example/CPU/HF-Transformers-AutoModels) and GPU examples [here](https://github.com/intel-analytics/ipex-llm/tree/main/python/llm/example/GPU/HF-Transformers-AutoModels).
+> See the complete CPU examples [here](https://github.com/intel-analytics/ipex-llm/tree/main/python/llm/example/CPU/HF-Transformers-AutoModels) and GPU examples [here](https://github.com/intel-analytics/ipex-llm/tree/main/python/llm/example/GPU/HuggingFace).
 
 > [!NOTE]
 > You may apply more low bit optimizations (including INT8, INT5 and INT4) as follows:
@@ -32,7 +32,7 @@ output = tokenizer.batch_decode(output_ids)
 > model = AutoModelForCausalLM.from_pretrained('/path/to/model/', load_in_low_bit="sym_int5")
 > ```
 >
-> See the CPU example [here](https://github.com/intel-analytics/ipex-llm/tree/main/python/llm/example/CPU/HF-Transformers-AutoModels/More-Data-Types) and GPU example [here](https://github.com/intel-analytics/ipex-llm/tree/main/python/llm/example/GPU/HF-Transformers-AutoModels/More-Data-Types).
+> See the CPU example [here](https://github.com/intel-analytics/ipex-llm/tree/main/python/llm/example/CPU/HF-Transformers-AutoModels/More-Data-Types) and GPU example [here](https://github.com/intel-analytics/ipex-llm/tree/main/python/llm/example/GPU/HuggingFace/More-Data-Types).
 
 
 ## Save & Load
@@ -45,4 +45,4 @@ new_model = AutoModelForCausalLM.load_low_bit(model_path)
 ```
 
 > [!TIP]
-> See the complete CPU examples [here](https://github.com/intel-analytics/ipex-llm/tree/main/python/llm/example/CPU/HF-Transformers-AutoModels/Save-Load) and GPU examples [here](https://github.com/intel-analytics/ipex-llm/tree/main/python/llm/example/GPU/HF-Transformers-AutoModels/Save-Load).
+> See the complete CPU examples [here](https://github.com/intel-analytics/ipex-llm/tree/main/python/llm/example/CPU/HF-Transformers-AutoModels/Save-Load) and GPU examples [here](https://github.com/intel-analytics/ipex-llm/tree/main/python/llm/example/GPU/HuggingFace/Save-Load).

docs/readthedocs/source/doc/LLM/DockerGuides/docker_run_pytorch_inference_in_vscode.md

@@ -99,7 +99,7 @@ Start ipex-llm-xpu Docker Container:
 
 Press F1 to bring up the Command Palette and type in `Dev Containers: Attach to Running Container...` and select it and then select `my_container`
 
-Now you are in a running Docker Container, Open folder `/ipex-llm/python/llm/example/GPU/HF-Transformers-AutoModels/Model/`.
+Now you are in a running Docker Container, Open folder `/ipex-llm/python/llm/example/GPU/HuggingFace/LLM/`.
 
 <a href="https://llm-assets.readthedocs.io/en/latest/_images/run_example_in_vscode.gif" target="_blank">
   <img src="https://llm-assets.readthedocs.io/en/latest/_images/run_example_in_vscode.gif" width=100%; />

docs/readthedocs/source/doc/LLM/Overview/FAQ/faq.md

@@ -4,7 +4,7 @@
 
 ### GGUF format usage with IPEX-LLM?
 
-IPEX-LLM supports running GGUF/AWQ/GPTQ models on both [CPU](https://github.com/intel-analytics/ipex-llm/tree/main/python/llm/example/CPU/HF-Transformers-AutoModels/Advanced-Quantizations) and [GPU](https://github.com/intel-analytics/ipex-llm/tree/main/python/llm/example/GPU/HF-Transformers-AutoModels/Advanced-Quantizations).
+IPEX-LLM supports running GGUF/AWQ/GPTQ models on both [CPU](https://github.com/intel-analytics/ipex-llm/tree/main/python/llm/example/CPU/HF-Transformers-AutoModels/Advanced-Quantizations) and [GPU](https://github.com/intel-analytics/ipex-llm/tree/main/python/llm/example/GPU/HuggingFace/Advanced-Quantizations).
 Please also refer to [here](https://github.com/intel-analytics/ipex-llm?tab=readme-ov-file#latest-update-) for our latest support.
 
 ## How to Resolve Errors

docs/readthedocs/source/doc/LLM/Overview/KeyFeatures/hugging_face_format.md

@@ -25,7 +25,7 @@ output = tokenizer.batch_decode(output_ids)
 ```eval_rst
 .. seealso::
 
-   See the complete CPU examples `here <https://github.com/intel-analytics/ipex-llm/tree/main/python/llm/example/CPU/HF-Transformers-AutoModels>`_ and GPU examples `here <https://github.com/intel-analytics/ipex-llm/tree/main/python/llm/example/GPU/HF-Transformers-AutoModels>`_.
+   See the complete CPU examples `here <https://github.com/intel-analytics/ipex-llm/tree/main/python/llm/example/CPU/HF-Transformers-AutoModels>`_ and GPU examples `here <https://github.com/intel-analytics/ipex-llm/tree/main/python/llm/example/GPU/HuggingFace>`_.
 
 .. note::
 
@@ -35,7 +35,7 @@ output = tokenizer.batch_decode(output_ids)
 
       model = AutoModelForCausalLM.from_pretrained('/path/to/model/', load_in_low_bit="sym_int5")
 
-   See the CPU example `here <https://github.com/intel-analytics/ipex-llm/tree/main/python/llm/example/CPU/HF-Transformers-AutoModels/More-Data-Types>`_ and GPU example `here <https://github.com/intel-analytics/ipex-llm/tree/main/python/llm/example/GPU/HF-Transformers-AutoModels/More-Data-Types>`_.
+   See the CPU example `here <https://github.com/intel-analytics/ipex-llm/tree/main/python/llm/example/CPU/HF-Transformers-AutoModels/More-Data-Types>`_ and GPU example `here <https://github.com/intel-analytics/ipex-llm/tree/main/python/llm/example/GPU/HuggingFace/More-Data-Types>`_.
 ```
 
 ## Save & Load
@@ -50,5 +50,5 @@ new_model = AutoModelForCausalLM.load_low_bit(model_path)
 ```eval_rst
 .. seealso::
 
-   See the CPU example `here <https://github.com/intel-analytics/ipex-llm/tree/main/python/llm/example/CPU/HF-Transformers-AutoModels/Save-Load>`_ and GPU example `here <https://github.com/intel-analytics/ipex-llm/tree/main/python/llm/example/GPU/HF-Transformers-AutoModels/Save-Load>`_
+   See the CPU example `here <https://github.com/intel-analytics/ipex-llm/tree/main/python/llm/example/CPU/HF-Transformers-AutoModels/Save-Load>`_ and GPU example `here <https://github.com/intel-analytics/ipex-llm/tree/main/python/llm/example/GPU/HuggingFace/Save-Load>`_
 ```

docs/readthedocs/source/doc/LLM/Overview/examples_gpu.md

@@ -37,29 +37,29 @@ The following models have been verified on either servers or laptops with Intel
 
 | Model      | Example of `transformers`-style API                   |
 |------------|-------------------------------------------------------|
-| LLaMA *(such as Vicuna, Guanaco, Koala, Baize, WizardLM, etc.)* |[link](https://github.com/intel-analytics/ipex-llm/tree/main/python/llm/example/GPU/HF-Transformers-AutoModels/Model/vicuna)|
-| LLaMA 2    | [link](https://github.com/intel-analytics/ipex-llm/tree/main/python/llm/example/GPU/HF-Transformers-AutoModels/Model/llama2) |
-| ChatGLM2   | [link](https://github.com/intel-analytics/ipex-llm/tree/main/python/llm/example/GPU/HF-Transformers-AutoModels/Model/chatglm2)   |
-| Mistral    | [link](https://github.com/intel-analytics/ipex-llm/tree/main/python/llm/example/GPU/HF-Transformers-AutoModels/Model/mistral)    |
-| Falcon     | [link](https://github.com/intel-analytics/ipex-llm/tree/main/python/llm/example/GPU/HF-Transformers-AutoModels/Model/falcon)     |
+| LLaMA *(such as Vicuna, Guanaco, Koala, Baize, WizardLM, etc.)* |[link](https://github.com/intel-analytics/ipex-llm/tree/main/python/llm/example/GPU/HuggingFace/LLM/vicuna)|
+| LLaMA 2    | [link](https://github.com/intel-analytics/ipex-llm/tree/main/python/llm/example/GPU/HuggingFace/LLM/llama2) |
+| ChatGLM2   | [link](https://github.com/intel-analytics/ipex-llm/tree/main/python/llm/example/GPU/HuggingFace/LLM/chatglm2)   |
+| Mistral    | [link](https://github.com/intel-analytics/ipex-llm/tree/main/python/llm/example/GPU/HuggingFace/LLM/mistral)    |
+| Falcon     | [link](https://github.com/intel-analytics/ipex-llm/tree/main/python/llm/example/GPU/HuggingFace/LLM/falcon)     |
 | MPT        | [link](https://github.com/intel-analytics/ipex-llm/tree/main/python/llm/example/CPU/HF-Transformers-AutoModels/Model/mpt)        |
 | Dolly-v1   | [link](https://github.com/intel-analytics/ipex-llm/tree/main/python/llm/example/CPU/HF-Transformers-AutoModels/Model/dolly_v1)   | 
 | Dolly-v2   | [link](https://github.com/intel-analytics/ipex-llm/tree/main/python/llm/example/CPU/HF-Transformers-AutoModels/Model/dolly_v2)   | 
 | Replit     | [link](https://github.com/intel-analytics/ipex-llm/tree/main/python/llm/example/CPU/HF-Transformers-AutoModels/Model/replit)     |
-| StarCoder  | [link](https://github.com/intel-analytics/ipex-llm/tree/main/python/llm/example/GPU/HF-Transformers-AutoModels/Model/starcoder)  | 
+| StarCoder  | [link](https://github.com/intel-analytics/ipex-llm/tree/main/python/llm/example/GPU/HuggingFace/LLM/starcoder)  | 
 | Baichuan   | [link](https://github.com/intel-analytics/ipex-llm/tree/main/python/llm/example/CPU/HF-Transformers-AutoModels/Model/baichuan)   |
-| Baichuan2  | [link](https://github.com/intel-analytics/ipex-llm/tree/main/python/llm/example/GPU/HF-Transformers-AutoModels/Model/baichuan2)  |
-| InternLM   | [link](https://github.com/intel-analytics/ipex-llm/tree/main/python/llm/example/GPU/HF-Transformers-AutoModels/Model/internlm)   |
-| Qwen       | [link](https://github.com/intel-analytics/ipex-llm/tree/main/python/llm/example/GPU/HF-Transformers-AutoModels/Model/qwen)       |
-| Aquila     | [link](https://github.com/intel-analytics/ipex-llm/tree/main/python/llm/example/GPU/HF-Transformers-AutoModels/Model/aquila)     |
-| Whisper    | [link](https://github.com/intel-analytics/ipex-llm/tree/main/python/llm/example/GPU/HF-Transformers-AutoModels/Model/whisper)    |
-| Chinese Llama2	    | [link](https://github.com/intel-analytics/ipex-llm/tree/main/python/llm/example/GPU/HF-Transformers-AutoModels/Model/chinese-llama2)    |
-| GPT-J    | [link](https://github.com/intel-analytics/ipex-llm/tree/main/python/llm/example/GPU/HF-Transformers-AutoModels/Model/gpt-j)    |
+| Baichuan2  | [link](https://github.com/intel-analytics/ipex-llm/tree/main/python/llm/example/GPU/HuggingFace/LLM/baichuan2)  |
+| InternLM   | [link](https://github.com/intel-analytics/ipex-llm/tree/main/python/llm/example/GPU/HuggingFace/LLM/internlm)   |
+| Qwen       | [link](https://github.com/intel-analytics/ipex-llm/tree/main/python/llm/example/GPU/HuggingFace/LLM/qwen)       |
+| Aquila     | [link](https://github.com/intel-analytics/ipex-llm/tree/main/python/llm/example/GPU/HuggingFace/LLM/aquila)     |
+| Whisper    | [link](https://github.com/intel-analytics/ipex-llm/tree/main/python/llm/example/GPU/HuggingFace/Multimodal/whisper)    |
+| Chinese Llama2	    | [link](https://github.com/intel-analytics/ipex-llm/tree/main/python/llm/example/GPU/HuggingFace/LLM/chinese-llama2)    |
+| GPT-J    | [link](https://github.com/intel-analytics/ipex-llm/tree/main/python/llm/example/GPU/HuggingFace/LLM/gpt-j)    |
 
 ```eval_rst
 .. important::
 
-   In addition to INT4 optimization, IPEX-LLM also provides other low bit optimizations (such as INT8, INT5, NF4, etc.). You may apply other low bit optimizations through ``transformers``-style API as `example <https://github.com/intel-analytics/ipex-llm/tree/main/python/llm/example/GPU/HF-Transformers-AutoModels/More-Data-Types>`_.
+   In addition to INT4 optimization, IPEX-LLM also provides other low bit optimizations (such as INT8, INT5, NF4, etc.). You may apply other low bit optimizations through ``transformers``-style API as `example <https://github.com/intel-analytics/ipex-llm/tree/main/python/llm/example/GPU/HuggingFace/More-Data-Types>`_.
 ```