update doc of guidelines

doc/tutorial/fate_llm/GPT2-example.ipynb

@@ -5,15 +5,15 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "#  Federated GPT-2 Tuning with Parameter Efficient methods in FATE-1.11"
+    "#  Federated GPT-2 Tuning with Parameter Efficient methods in FATE-LLM"
    ]
   },
   {
    "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "In this tutorial, we will demonstrate how to efficiently train federated large language models using the FATE 1.11 framework. In FATE-1.11, we introduce the \"pellm\"(Parameter Efficient Large Language Model) module, specifically designed for federated learning with large language models. We enable the implementation of parameter-efficient methods in federated learning, reducing communication overhead while maintaining model performance. In this tutorial we particularlly focus on GPT-2, and we will also emphasize the use of the Adapter mechanism for fine-tuning GPT-2, which enables us to effectively reduce communication volume and improve overall efficiency.\n",
+    "In this tutorial, we will demonstrate how to efficiently train federated large language models using the FATE-LLM framework. In FATE-LLM, we introduce the \"pellm\"(Parameter Efficient Large Language Model) module, specifically designed for federated learning with large language models. We enable the implementation of parameter-efficient methods in federated learning, reducing communication overhead while maintaining model performance. In this tutorial we particularlly focus on GPT-2, and we will also emphasize the use of the Adapter mechanism for fine-tuning GPT-2, which enables us to effectively reduce communication volume and improve overall efficiency.\n",
     "\n",
     "By following this tutorial, you will learn how to leverage the FATE framework to rapidly fine-tune federated large language models, such as GPT-2, with ease and efficiency."
    ]

doc/tutorial/fate_llm/GPT2-multi-task.ipynb

@@ -5,15 +5,15 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Multi-Task Federated Learning with GPT-2 using FATE-1.11"
+    "# Multi-Task Federated Learning with GPT-2 using FATE-LLM"
    ]
   },
   {
    "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "In this tutorial, we will explore the implementation of multi-task federated learning with LM: GPT-2 using the FATE-1.11 framework. FATE-1.11 provides  the \"pellm\" module for efficient federated learning. It is specifically designed for large language models in a federated setting.\n",
+    "In this tutorial, we will explore the implementation of multi-task federated learning with LM: GPT-2 using the FATE-LLM framework. FATE-LLM provides  the \"pellm\" module for efficient federated learning. It is specifically designed for large language models in a federated setting.\n",
     "\n",
     "Multi-task learning involves training a model to perform multiple tasks simultaneously. In this tutorial, we will focus on two tasks - sentiment classification and named entity recognition (NER) - and show how they can be combined with GPT-2 in a federated learning setting. We will use the IMDB sentiment analysis dataset and the CoNLL-2003 NER dataset for our tasks.\n",
     "\n",