diff --git a/sdk/python/foundation-models/system/finetune/Llama-notebooks/distillation/distillation_conversational_task.ipynb b/sdk/python/foundation-models/system/finetune/Llama-notebooks/distillation/distillation_conversational_task.ipynb
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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Distillation with Large Language Models\n",
+    " \n",
+    "### Notebook details\n",
+    " \n",
+    "This sample demonstrates how to train the selected student model using the teacher model, resulting in the creation of the distilled model.\n",
+    " \n",
+    "We will use the Meta Llama 3.1 405B Instruct as the teacher model and the Meta Llama 3.1 8B Instruct as the student model.\n",
+    " \n",
+    "**Note :**\n",
+    " \n",
+    "- Distillation offering is only available in **West US 3** regions.\n",
+    "- Distillation should only be used for single turn chat completion format.\n",
+    "- The Meta Llama 3.1 405B Instruct model can only be used as a teacher model.\n",
+    "- The Meta Llama 3.1 8B Instruct can only be used as a student (target) model.\n",
+    "- Distllation is currently supported only for Natural Language Inference (NLI) task, Conversational single turn and multi turn (CONVERSATION) and Natural language understanding Question and Answering (NLU_QA) which is a standard task in benchmarking for Natural Language Understanding.\n",
+    "\n",
+    "**Prerequisites :**\n",
+    "- Subscribe to the Meta Llama 3.1 405B Instruct and Meta Llama 3.1 8B Instruct, see [how to subscribe your project to the model offering in MS Learn](https://learn.microsoft.com/en-us/azure/ai-studio/how-to/deploy-models-serverless?tabs=azure-ai-studio#subscribe-your-project-to-the-model-offering)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Install the SDK v2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# %pip install azure-ai-ml\n",
+    "# %pip install azure-identity\n",
+    "# %pip install tqdm\n",
+    "# %pip install mlflow\n",
+    "# %pip install azureml-mlflow\n",
+    "# %pip install datasets"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Import the required libraries"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# import required libraries\n",
+    "\n",
+    "import base64\n",
+    "import json\n",
+    "from tqdm.notebook import tqdm\n",
+    "from azure.identity import DefaultAzureCredential, InteractiveBrowserCredential\n",
+    "\n",
+    "from azure.ai.ml import MLClient, Input\n",
+    "from azure.ai.ml.constants import AssetTypes\n",
+    "from azure.ai.ml.dsl import pipeline\n",
+    "from azure.ai.ml.entities import Data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Prerequisites\n",
+    "\n",
+    "An AI Studio project in **West US 3** is required. Please follow [this](https://learn.microsoft.com/azure/ai-studio/how-to/fine-tune-model-llama?tabs=llama-two%2Cchatcompletion#prerequisites) document to setup your AI Studio project"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## AI Studio project settings\n",
+    "\n",
+    "Update following cell with the information of the AI Studio project just created."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "SUBSCRIPTION_ID = \"<SUBSCRIPTION>\"\n",
+    "RESOURCE_GROUP = \"<RESOURCE_GROUP>\"\n",
+    "AI_PROJECT_NAME = \"<AI_PROJECT_NAME>\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Configure credential\n",
+    "\n",
+    "We are using `DefaultAzureCredential` to get access to workspace. \n",
+    "`DefaultAzureCredential` should be capable of handling most Azure SDK authentication scenarios. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "try:\n",
+    "    credential = DefaultAzureCredential()\n",
+    "    # Check if given credential can get token successfully.\n",
+    "    credential.get_token(\"https://management.azure.com/.default\")\n",
+    "except Exception as ex:\n",
+    "    # Fall back to InteractiveBrowserCredential in case DefaultAzureCredential not work\n",
+    "    credential = InteractiveBrowserCredential()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Get handle to AI Studio project"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ml_client = MLClient(credential, SUBSCRIPTION_ID, RESOURCE_GROUP, AI_PROJECT_NAME)\n",
+    "\n",
+    "ai_project = ml_client._workspaces.get(ml_client.workspace_name)\n",
+    "ai_project._workspace_id"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Pick a teacher model\n",
+    "\n",
+    "We support **Meta-Llama-3.1-405B-Instruct** as the teacher model. \n",
+    "### First deploy the teacher model in Azure AI Studio\n",
+    "* Go to Azure AI Studio (ai.azure.com)\n",
+    "* Select Meta-Llama-3.1-405B-Instruct model from Model catalog.\n",
+    "* Deploy with \"Pay-as-you-go\"\n",
+    "* Once deployed successfully, you should be assigned for an API endpoint and a security key for inference.\n",
+    "\n",
+    "Update the following cell with the information of the deployment you just created."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Llama-3-405B Teacher model endpoint name\n",
+    "# The serverless model name is the name found in ML Studio > Endpoints > Serverless endpoints > Model column\n",
+    "TEACHER_MODEL_NAME = \"Meta-Llama-3.1-405B-Instruct\"\n",
+    "\n",
+    "# The serverless model endpoint name is the name found in ML Studio > Endpoints > Serverless endpoints > Name column\n",
+    "# The endpoint URL will be resolved from this name by the MLFlow component\n",
+    "TEACHER_MODEL_ENDPOINT_NAME = \"Meta-Llama-3-1-405B-Instruct-vum\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Pick a student model\n",
+    "\n",
+    "We will use **Meta-Llama-3.1-8B-Instruct** as student model. We only support chat completion models that are available for PayGo finetuning in Azure AI Studio."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "STUDENT_MODEL_NAME = \"Meta-Llama-3.1-8B-Instruct\"\n",
+    "STUDENT_MODEL_VERSION = 1\n",
+    "\n",
+    "# retrieve student model from model registry\n",
+    "mlclient_azureml_meta = MLClient(credential, registry_name=\"azureml-meta\")\n",
+    "student_model = mlclient_azureml_meta.models.get(\n",
+    "    STUDENT_MODEL_NAME, version=STUDENT_MODEL_VERSION\n",
+    ")\n",
+    "\n",
+    "print(\n",
+    "    \"\\n\\nUsing model name: {0}, version: {1}, id: {2} for fine tuning\".format(\n",
+    "        student_model.name, student_model.version, student_model.id\n",
+    "    )\n",
+    ")"
+   ]
+  },
+  {
+   "attachments": {
+    "image.png": {
+     "image/png": 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+    }
+   },
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Download the dataset from HuggingFace repo\n",
+    "\n",
+    "- For our example, we download and use the Quora dataset: https://huggingface.co/datasets/twodgirl/baize-quora\n",
+    "- The dataset from hugging face is in the below format\n",
+    "    \n",
+    "    ![image.png](attachment:image.png)\n",
+    "    \n",
+    "- For this example the data will be transformed into chat completion format and the system prompt is overriden with the one given below\n",
+    "    \n",
+    "    **SystemPrompt**\n",
+    "    ```text\n",
+    "    The following is a conversation between a human and an AI assistant. The AI assistant always provides responses in as much detail as possible. The AI assistant will never ask personal information. The AI assistant always declines to engage with topics, questions and instructions related to unethical, controversial, or sensitive issues. Complete the conversation transcript.\n",
+    "    ```\n",
+    "    \n",
+    "    ---\n",
+    "\n",
+    "    **Transformed Data**\n",
+    "\n",
+    "    ```JSON\n",
+    "    {\n",
+    "    \"messages\": [\n",
+    "        {\n",
+    "            \"role\": \"system\",\n",
+    "            \"content\": \"The following is a conversation between a human and an AI assistant. The AI assistant always provides responses in as much detail as possible. The AI assistant will never ask personal information. The AI assistant always declines to engage with topics, questions and instructions related to unethical, controversial, or sensitive issues. Complete the conversation transcript.\"\n",
+    "        },\n",
+    "        {\n",
+    "            \"role\": \"user\",\n",
+    "            \"content\": \"I want to know the step by step guide to invest in share market in India.\"\n",
+    "        },\n",
+    "        {\n",
+    "            \"role\": \"assistant\",\n",
+    "            \"content\": \"Sure, I can help with that. Firstly, you need to open a demat and trading account with a registered stockbroker.\"\n",
+    "        },\n",
+    "        {\n",
+    "            \"role\": \"user\",\n",
+    "            \"content\": \"How do I find a registered stockbroker in India?\"\n",
+    "        },\n",
+    "        {\n",
+    "            \"role\": \"assistant\",\n",
+    "            \"content\": \"You can visit the websites of National Stock Exchange (NSE) or Bombay Stock Exchange (BSE) to get a list of registered stockbrokers in India.\"\n",
+    "        }...\n",
+    "    ]\n",
+    "    }\n",
+    "    ```"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from datasets import load_dataset\n",
+    "from pathlib import Path\n",
+    "import json\n",
+    "\n",
+    "\n",
+    "def load_hf_dataset(\n",
+    "    dataset_name,\n",
+    "    system_prompt,\n",
+    "    train_sample_size=100,\n",
+    "    val_sample_size=100,\n",
+    "    test_sample_size=100,\n",
+    "    train_split_name=\"train\",\n",
+    "):\n",
+    "\n",
+    "    full_dataset = load_dataset(dataset_name)\n",
+    "    train_data = full_dataset[train_split_name]\n",
+    "    full_df = train_data.to_pandas()\n",
+    "    conversations = list(full_df.groupby(\"instruction\", sort=False))\n",
+    "    conversations = conversations[\n",
+    "        : train_sample_size + val_sample_size + test_sample_size\n",
+    "    ]\n",
+    "    all_conversations_chat_format = []\n",
+    "    for instruction, conversation_df in conversations:\n",
+    "        conversation = {\"messages\": []}\n",
+    "        conversation_df.reset_index(drop=True, inplace=True)\n",
+    "        for i, row in conversation_df.iterrows():\n",
+    "            if i == 0:\n",
+    "                conversation[\"messages\"].append(\n",
+    "                    {\"role\": \"system\", \"content\": system_prompt}\n",
+    "                )\n",
+    "            conversation[\"messages\"].append({\"role\": \"user\", \"content\": row[\"input\"]})\n",
+    "            conversation[\"messages\"].append(\n",
+    "                {\"role\": \"assistant\", \"content\": row[\"output\"]}\n",
+    "            )\n",
+    "        all_conversations_chat_format.append(conversation)\n",
+    "    train_data = all_conversations_chat_format[:train_sample_size]\n",
+    "    val_data = all_conversations_chat_format[\n",
+    "        train_sample_size : train_sample_size + val_sample_size\n",
+    "    ]\n",
+    "    test_data = all_conversations_chat_format[\n",
+    "        train_sample_size\n",
+    "        + val_sample_size : train_sample_size\n",
+    "        + val_sample_size\n",
+    "        + test_sample_size\n",
+    "    ]\n",
+    "\n",
+    "    return train_data, val_data, test_data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# We can define train and test sample sizes here.\n",
+    "train_sample_size = 100\n",
+    "val_sample_size = 100\n",
+    "system_prompt = \"The following is a conversation between a human and an AI assistant. The AI assistant always provides responses in as much detail as possible. The AI assistant will never ask personal information. The AI assistant always declines to engage with topics, questions and instructions related to unethical, controversial, or sensitive issues. Complete the conversation transcript.\"\n",
+    "# Sample notebook using the dataset: https://huggingface.co/datasets/cestwc/conjnli\n",
+    "dataset_name = \"twodgirl/baize-quora\"\n",
+    "\n",
+    "\n",
+    "# Note: train_split_name and test_split_name can vary by dataset. They are passed as arguments in load_hf_dataset.\n",
+    "# If val_split_name is None, the below function will split the train set to create the specified sized validation set.\n",
+    "train, val, _ = load_hf_dataset(\n",
+    "    dataset_name=dataset_name,\n",
+    "    system_prompt=system_prompt,\n",
+    "    train_sample_size=train_sample_size,\n",
+    "    val_sample_size=val_sample_size,\n",
+    ")\n",
+    "\n",
+    "print(\"Len of train data sample is \" + str(len(train)))\n",
+    "print(\"Len of validation data sample is \" + str(len(val)))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "! mkdir -p data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "train_data_path = Path(f\"data/train_quora_{train_sample_size}.jsonl\")\n",
+    "valid_data_path = Path(f\"data/valid_quora_{val_sample_size}.jsonl\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "with open(train_data_path, \"w\") as f:\n",
+    "    for row in train:\n",
+    "        f.write(json.dumps(row) + \"\\n\")\n",
+    "\n",
+    "with open(valid_data_path, \"w\") as f:\n",
+    "    for row in val:\n",
+    "        f.write(json.dumps(row) + \"\\n\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Prepare data inputs\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "train_data = None\n",
+    "train_data_name = \"quora_chat_train_data\"\n",
+    "\n",
+    "train_data = ml_client.data.create_or_update(\n",
+    "    Data(\n",
+    "        path=train_data_path,\n",
+    "        type=AssetTypes.URI_FILE,\n",
+    "        description=\"Training dataset\",\n",
+    "        name=train_data_name,\n",
+    "    )\n",
+    ")\n",
+    "\n",
+    "train_data_asset_id = f\"azureml://locations/{ai_project.location}/workspaces/{ai_project._workspace_id}/data/{train_data.name}/versions/{train_data.version}\"\n",
+    "print(f\"{train_data_asset_id=}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "valid_data = None\n",
+    "valid_data_name = \"quora_chat_valid_data\"\n",
+    "\n",
+    "valid_data = ml_client.data.create_or_update(\n",
+    "    Data(\n",
+    "        path=valid_data_path,\n",
+    "        type=AssetTypes.URI_FILE,\n",
+    "        description=\"validation dataset\",\n",
+    "        name=valid_data_name,\n",
+    "    )\n",
+    ")\n",
+    "\n",
+    "valid_data_asset_id = f\"azureml://locations/{ai_project.location}/workspaces/{ai_project._workspace_id}/data/{valid_data.name}/versions/{valid_data.version}\"\n",
+    "print(f\"{valid_data_asset_id=}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Configure distillation"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Note:** \n",
+    "- For each turn in the conversation data, the assistant's response is generated by the teacher model, utilizing the user prompt and the preceding chat history. This synthetic response then replaces the original assistant response in the dataset.\n",
+    "- The distillation process will proceed using the conversation data generated in this manner."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "mlclient_azureml = MLClient(credential, registry_name=\"azureml\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "distillation_pipeline_name = \"oss_distillation_pipeline\"\n",
+    "distillation_pipeline_component = mlclient_azureml.components.get(\n",
+    "    name=distillation_pipeline_name\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "@pipeline\n",
+    "def distillation_pipeline(\n",
+    "    teacher_model_endpoint_name: str,\n",
+    "    system_properties: str,\n",
+    "    input_finetune_model: Input,\n",
+    "    train_file_path: Input,\n",
+    "    validation_file_path: Input = None,\n",
+    "):\n",
+    "    oss_distillation = distillation_pipeline_component(\n",
+    "        teacher_model_endpoint_name=teacher_model_endpoint_name,\n",
+    "        train_file_path=train_file_path,\n",
+    "        validation_file_path=validation_file_path,\n",
+    "        # Finetune\n",
+    "        mlflow_model_path=input_finetune_model,\n",
+    "        model_asset_id=student_model.id,\n",
+    "        system_properties=system_properties,\n",
+    "        ## hyperparams\n",
+    "        learning_rate=2e-5,\n",
+    "        per_device_train_batch_size=1,\n",
+    "        num_train_epochs=3,\n",
+    "        data_generation_task_type=\"CONVERSATION\",\n",
+    "    )\n",
+    "\n",
+    "    return {\"output_model\": oss_distillation.outputs.output_model}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "system_properties = {\n",
+    "    \"finetune_oss\": \"True\",\n",
+    "    \"model_asset_id\": student_model.id,\n",
+    "    \"PipelineType\": \"Finetune\",\n",
+    "    \"azureml.PipelineType\": \"Finetune\",\n",
+    "    \"azureml.ModelName\": student_model.name,\n",
+    "    \"azureml.original_model_id\": student_model.id,\n",
+    "    \"azureml.trainingData.assetId\": train_data_asset_id,\n",
+    "}\n",
+    "\n",
+    "json_str = json.dumps(system_properties).replace(\" \", \"\")\n",
+    "\n",
+    "system_properties_b64_encoded = base64.b64encode(json_str.encode(\"utf-8\")).decode(\n",
+    "    \"utf-8\"\n",
+    ")\n",
+    "print(f\"System properties => {system_properties_b64_encoded}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "train_file_path_input = Input(type=\"uri_file\", path=train_data.path)\n",
+    "validation_file_path_input = Input(type=\"uri_file\", path=valid_data.path)\n",
+    "input_finetune_model = Input(type=\"mlflow_model\", path=student_model.id)\n",
+    "experiment_name = f\"distillation-{TEACHER_MODEL_NAME}\".replace(\".\", \"-\")\n",
+    "\n",
+    "finetuning_job = distillation_pipeline(\n",
+    "    teacher_model_endpoint_name=TEACHER_MODEL_ENDPOINT_NAME,\n",
+    "    system_properties=system_properties_b64_encoded,\n",
+    "    input_finetune_model=input_finetune_model,\n",
+    "    train_file_path=train_file_path_input,\n",
+    "    validation_file_path=validation_file_path_input,\n",
+    ")\n",
+    "\n",
+    "finetuning_job.properties.update(system_properties)\n",
+    "print(f\"job property: {finetuning_job.properties}\")\n",
+    "\n",
+    "# pipeline_job.identity = UserIdentityConfiguration()\n",
+    "finetuning_job.display_name = f\"finetune-{student_model.name}\"\n",
+    "finetuning_job.experiment_name = experiment_name\n",
+    "finetuning_job.settings.default_compute_type = \"serverless\"\n",
+    "finetuning_job.continue_on_step_failure = False\n",
+    "# pipeline_job.settings.force_rerun = True"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Submit pipeline job"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Submit pipeline job to workspace\n",
+    "ft_job = ml_client.jobs.create_or_update(finetuning_job)\n",
+    "print(f\"Submitted job, progress available at {ft_job.studio_url}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Consuming the distilled model\n",
+    "\n",
+    "Once the above job completes, you should be able to deploy the model and use it for inferencing. To deploy this model, do the following:\n",
+    "\n",
+    "* Go to AI Studio\n",
+    "* Navigate to the Fine-tuning tab on the left menu\n",
+    "* In the list of models you see, click on the model which got created from the distillation\n",
+    "* This should take you to the details page where you can see the model attributes and other details\n",
+    "* Click on the Deploy button on top of the page\n",
+    "* Follow the steps to deploy the model"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}