Update README.md (#11964)

python/llm/example/NPU/HF-Transformers-AutoModels/LLM/README.md

@@ -1,5 +1,5 @@
-# Run Large Language Model on Intel NPU
-In this directory, you will find examples on how you could apply IPEX-LLM INT4 or INT8 optimizations on LLM models on [Intel NPUs](../../../README.md). See the table blow for verified models.
+# Run HuggingFace `transformers` Models on Intel NPU
+In this directory, you will find examples on how to directly run HuggingFace `transformers` models on Intel NPUs (leveraging *Intel NPU Acceleration Library*). See the table blow for verified models.
 
 ## Verified Models
 
@@ -52,7 +52,7 @@ For optimal performance, it is recommended to set several environment variables.
 set BIGDL_USE_NPU=1
 ```
 
-## 3. Run models
+## 3. Run Models
 In the example [generate.py](./generate.py), we show a basic use case for a Llama2 model to predict the next N tokens using `generate()` API, with IPEX-LLM INT4 optimizations on Intel NPUs.
 
 ```
@@ -77,7 +77,7 @@ done
 ```
 
 ## 4. Run Optimized Models (Experimental)
-The example below shows how to run the **_optimized model implementations_** on Intel NPU, including
+The examples below show how to run the **_optimized HuggingFace model implementations_** on Intel NPU, including
 - [Llama2-7B](./llama.py)
 - [Llama3-8B](./llama.py)
 - [Qwen2-1.5B](./qwen2.py)
@@ -92,7 +92,7 @@ Supported models: Llama2-7B, Qwen2-1.5B, Qwen2-7B, MiniCPM-1B, Baichuan2-7B
 #### 32.0.101.2715
 Supported models: Llama3-8B, MiniCPM-2B
 
-### Run Models
+### Run
 ```bash
 # to run Llama-2-7b-chat-hf
 python llama.py