Update llama example information (#12640)

Co-authored-by: ATMxsp01 <shou.xu@intel.com>

python/llm/example/GPU/HuggingFace/LLM/llama3.1/README.md

@@ -1,5 +1,5 @@
 # Llama3.1
-In this directory, you will find examples on how you could apply IPEX-LLM INT4 optimizations on Llama3.1 models on [Intel GPUs](../../../README.md). For illustration purposes, we utilize the [meta-llama/Meta-Llama-3.1-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3.1-8B-Instruct) as a reference Llama3.1 model.
+In this directory, you will find examples on how you could apply IPEX-LLM INT4 optimizations on Llama3.1 models on [Intel GPUs](../../../README.md). For illustration purposes, we utilize the [meta-llama/Llama-3.1-8B-Instruct](https://huggingface.co/meta-llama/Llama-3.1-8B-Instruct) as a reference Llama3.1 model.
 
 ## 0. Requirements
 To run these examples with IPEX-LLM on Intel GPUs, we have some recommended requirements for your machine, please refer to [here](../../../README.md#requirements) for more information.
@@ -104,12 +104,12 @@ python ./generate.py --repo-id-or-model-path REPO_ID_OR_MODEL_PATH --prompt PROM
 ```
 
 Arguments info:
-- `--repo-id-or-model-path REPO_ID_OR_MODEL_PATH`: argument defining the huggingface repo id for the Llama3.1 model (e.g. `meta-llama/Meta-Llama-3.1-8B-Instruct`) to be downloaded, or the path to the huggingface checkpoint folder. It is default to be `'meta-llama/Meta-Llama-3.1-8B-Instruct'`.
+- `--repo-id-or-model-path REPO_ID_OR_MODEL_PATH`: argument defining the huggingface repo id for the Llama3.1 model (e.g. `meta-llama/Llama-3.1-8B-Instruct`) to be downloaded, or the path to the huggingface checkpoint folder. It is default to be `'meta-llama/Llama-3.1-8B-Instruct'`.
 - `--prompt PROMPT`: argument defining the prompt to be infered (with integrated prompt format for chat). It is default to be `'What is AI?'`.
 - `--n-predict N_PREDICT`: argument defining the max number of tokens to predict. It is default to be `32`.
 
 #### Sample Output
-#### [meta-llama/Meta-Llama-3.1-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3.1-8B-Instruct)
+#### [meta-llama/Llama-3.1-8B-Instruct](https://huggingface.co/meta-llama/Llama-3.1-8B-Instruct)
 ```log
 Inference time: xxxx s
 -------------------- Prompt --------------------

python/llm/example/GPU/HuggingFace/LLM/llama3.1/generate.py

@@ -42,8 +42,8 @@ def get_prompt(user_input: str, chat_history: list[tuple[str, str]],
 
 if __name__ == '__main__':
     parser = argparse.ArgumentParser(description='Predict Tokens using `generate()` API for Llama3.1 model')
-    parser.add_argument('--repo-id-or-model-path', type=str, default="meta-llama/Meta-Llama-3.1-8B-Instruct",
+    parser.add_argument('--repo-id-or-model-path', type=str, default="meta-llama/Llama-3.1-8B-Instruct",
-                        help='The huggingface repo id for the Llama3 (e.g. `meta-llama/Meta-Llama-3.1-8B-Instruct`) to be downloaded'
+                        help='The huggingface repo id for the Llama3 (e.g. `meta-llama/Llama-3.1-8B-Instruct`) to be downloaded'
                              ', or the path to the huggingface checkpoint folder')
     parser.add_argument('--prompt', type=str, default="What is AI?",
                         help='Prompt to infer')

python/llm/example/GPU/HuggingFace/LLM/llama3.2/README.md

@@ -1,5 +1,5 @@
 # Llama3.2
-In this directory, you will find examples on how you could apply IPEX-LLM INT4 optimizations on Llama3.2 models on [Intel GPUs](../../../README.md). For illustration purposes, we utilize the [meta-llama/Meta-Llama-3.2-3B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3.2-3B-Instruct) and [meta-llama/Meta-Llama-3.2-1B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3.2-1B-Instruct) as reference Llama3.2 models.
+In this directory, you will find examples on how you could apply IPEX-LLM INT4 optimizations on Llama3.2 models on [Intel GPUs](../../../README.md). For illustration purposes, we utilize the [meta-llama/Llama-3.2-3B-Instruct](https://huggingface.co/meta-llama/Llama-3.2-3B-Instruct) and [meta-llama/Llama-3.2-1B-Instruct](https://huggingface.co/meta-llama/Llama-3.2-1B-Instruct) as reference Llama3.2 models.
 
 ## 0. Requirements
 To run these examples with IPEX-LLM on Intel GPUs, we have some recommended requirements for your machine, please refer to [here](../../../README.md#requirements) for more information.
@@ -104,12 +104,12 @@ python ./generate.py --repo-id-or-model-path REPO_ID_OR_MODEL_PATH --prompt PROM
 ```
 
 Arguments info:
-- `--repo-id-or-model-path REPO_ID_OR_MODEL_PATH`: argument defining the huggingface repo id for the Llama3.2 model (e.g. `meta-llama/Meta-Llama-3.2-3B-Instruct`) to be downloaded, or the path to the huggingface checkpoint folder. It is default to be `'meta-llama/Meta-Llama-3.2-3B-Instruct'`.
+- `--repo-id-or-model-path REPO_ID_OR_MODEL_PATH`: argument defining the huggingface repo id for the Llama3.2 model (e.g. `meta-llama/Llama-3.2-3B-Instruct`) to be downloaded, or the path to the huggingface checkpoint folder. It is default to be `'meta-llama/Llama-3.2-3B-Instruct'`.
 - `--prompt PROMPT`: argument defining the prompt to be infered (with integrated prompt format for chat). It is default to be `'What is AI?'`.
 - `--n-predict N_PREDICT`: argument defining the max number of tokens to predict. It is default to be `32`.
 
 #### Sample Output
-#### [meta-llama/Meta-Llama-3.2-3B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3.2-3B-Instruct)
+#### [meta-llama/Llama-3.2-3B-Instruct](https://huggingface.co/meta-llama/Llama-3.2-3B-Instruct)
 ```log
 Inference time: xxxx s
 -------------------- Prompt --------------------
@@ -126,7 +126,7 @@ What is AI?<|eot_id|><|start_header_id|>assistant<|end_header_id|>
 Artificial Intelligence (AI) refers to the development of computer systems that can perform tasks that would typically require human intelligence, such as learning, problem-solving, and
 ```
 
-#### [meta-llama/Meta-Llama-3.2-1B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3.2-1B-Instruct)
+#### [meta-llama/Llama-3.2-1B-Instruct](https://huggingface.co/meta-llama/Llama-3.2-1B-Instruct)
 ```log
 Inference time: xxxx s
 -------------------- Prompt --------------------

python/llm/example/GPU/HuggingFace/LLM/llama3/README.md

@@ -14,9 +14,6 @@ conda create -n llm python=3.11
 conda activate llm
 # below command will install intel_extension_for_pytorch==2.1.10+xpu as default
 pip install --pre --upgrade ipex-llm[xpu] --extra-index-url https://pytorch-extension.intel.com/release-whl/stable/xpu/us/
-
-# transformers>=4.33.0 is required for Llama3 with IPEX-LLM optimizations
-pip install transformers==4.37.0 
 ```
 
 #### 1.2 Installation on Windows
@@ -27,9 +24,6 @@ conda activate llm
 
 # below command will install intel_extension_for_pytorch==2.1.10+xpu as default
 pip install --pre --upgrade ipex-llm[xpu] --extra-index-url https://pytorch-extension.intel.com/release-whl/stable/xpu/us/
-
-# transformers>=4.33.0 is required for Llama3 with IPEX-LLM optimizations
-pip install transformers==4.37.0 
 ```
 
 ### 2. Configures OneAPI environment variables for Linux