Update README.md (#1068)

* update readme

Signed-off-by: jiqing-feng <jiqing.feng@intel.com>

* update readme

Signed-off-by: jiqing-feng <jiqing.feng@intel.com>

* update ipex readme

Signed-off-by: jiqing-feng <jiqing.feng@intel.com>

* update ipex notebook

Signed-off-by: jiqing-feng <jiqing.feng@intel.com>

* Update README.md

* Update notebooks/ipex/text_generation.ipynb

---------

Signed-off-by: jiqing-feng <jiqing.feng@intel.com>
Co-authored-by: Ella Charlaix <80481427+echarlaix@users.noreply.github.com>

Author: jiqing-feng

README.md

@@ -6,7 +6,7 @@
 
 🤗 Optimum Intel is the interface between the 🤗 Transformers and Diffusers libraries and the different tools and libraries provided by Intel to accelerate end-to-end pipelines on Intel architectures.
 
-[Intel Extension for PyTorch](https://intel.github.io/intel-extension-for-pytorch/#introduction) is an open-source library which provides optimizations for both eager mode and graph mode, however, compared to eager mode, graph mode in PyTorch* normally yields better performance from optimization techniques, such as operation fusion.
+[Intel Extension for PyTorch](https://intel.github.io/intel-extension-for-pytorch/#introduction) is an open-source library which provides optimizations like faster attention and operators fusion.
 
 Intel [Neural Compressor](https://www.intel.com/content/www/us/en/developer/tools/oneapi/neural-compressor.html) is an open-source library enabling the usage of the most popular compression techniques such as quantization, pruning and knowledge distillation. It supports automatic accuracy-driven tuning strategies in order for users to easily generate quantized model. The users can easily apply static, dynamic and aware-training quantization approaches while giving an expected accuracy criteria. It also supports different weight pruning techniques enabling the creation of pruned model giving a predefined sparsity target.
 
@@ -159,7 +159,7 @@ optimized_model = OVModelForSequenceClassification.from_pretrained(save_dir)
 
 
 ## IPEX
-To load your IPEX model, you can just replace your `AutoModelForXxx` class with the corresponding `IPEXModelForXxx` class. You can set `export=True` to load a PyTorch checkpoint, export your model via TorchScript and apply IPEX optimizations : both operators optimization (replaced with customized IPEX operators) and graph-level optimization (like operators fusion) will be applied on your model.
+To load your IPEX model, you can just replace your `AutoModelForXxx` class with the corresponding `IPEXModelForXxx` class. It will load a PyTorch checkpoint, and apply IPEX operators optimization (replaced with customized IPEX operators).
 ```diff
   from transformers import AutoTokenizer, pipeline
 - from transformers import AutoModelForCausalLM

notebooks/ipex/text_generation.ipynb

@@ -11,7 +11,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "To load your IPEX model, you can just replace your `AutoModelForXxx` class with the corresponding `IPEXModelForXxx` class. You can set `export=True` to load a PyTorch checkpoint, export your model via TorchScript and apply IPEX optimizations : both operators optimization (replaced with customized IPEX operators) and graph-level optimization (like operators fusion) will be applied on your model."
+    "To load your IPEX model, you can just replace your `AutoModelForXxx` class with the corresponding `IPEXModelForXxx` class. It could apply IPEX, providing optimizations like faster attention and operators fusion."
    ]
   },
   {
@@ -60,7 +60,7 @@
     }
    ],
    "source": [
-    "model = IPEXModelForCausalLM.from_pretrained(\"gpt2\", torch_dtype=torch.bfloat16, export=True)\n",
+    "model = IPEXModelForCausalLM.from_pretrained(\"gpt2\", torch_dtype=torch.bfloat16)\n",
     "tokenizer = AutoTokenizer.from_pretrained(\"gpt2\")\n",
     "input_sentence = [\"Answer the following yes/no question by reasoning step-by-step please. Can you write a whole Haiku in a single tweet?\"]\n",
     "model_inputs = tokenizer(input_sentence, return_tensors=\"pt\")\n",