diff --git a/1_instruction_tuning/student_examples/KevinMenpara/additional_requirements.sh b/1_instruction_tuning/student_examples/KevinMenpara/additional_requirements.sh
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+pip install ipywidgets
\ No newline at end of file
diff --git a/1_instruction_tuning/student_examples/KevinMenpara/chat_templates_example.ipynb b/1_instruction_tuning/student_examples/KevinMenpara/chat_templates_example.ipynb
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+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "vZAvFVIAtFlq"
+      },
+      "source": [
+        "# Exploring Chat Templates with SmolLM2\n",
+        "\n",
+        "This notebook demonstrates how to use chat templates with the `SmolLM2` model. Chat templates help structure interactions between users and AI models, ensuring consistent and contextually appropriate responses."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 3,
+      "metadata": {
+        "id": "K-lZu8JvtwUN"
+      },
+      "outputs": [
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "534ba01e4dff442987849c62c8dde24e",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "VBox(children=(HTML(value='<center> <img\\nsrc=https://huggingface.co/front/assets/huggingface_logo-noborder.sv…"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "# Install the requirements in Google Colab\n",
+        "#pip install transformers datasets trl huggingface_hub\n",
+        "\n",
+        "# Authenticate to Hugging Face\n",
+        "from huggingface_hub import login\n",
+        "\n",
+        "login()\n",
+        "\n",
+        "# for convenience you can create an environment variable containing your hub token as HF_TOKEN"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 4,
+      "metadata": {
+        "id": "tnHzBR7vtFlr"
+      },
+      "outputs": [],
+      "source": [
+        "# Import necessary libraries\n",
+        "from transformers import AutoModelForCausalLM, AutoTokenizer\n",
+        "from trl import setup_chat_format\n",
+        "import torch"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "XTVOqbuetFlr"
+      },
+      "source": [
+        "## SmolLM2 Chat Template\n",
+        "\n",
+        "Let's explore how to use a chat template with the `SmolLM2` model. We'll define a simple conversation and apply the chat template."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 5,
+      "metadata": {
+        "id": "Nrxh0oX6tFls"
+      },
+      "outputs": [],
+      "source": [
+        "# Dynamically set the device\n",
+        "device = (\n",
+        "    \"cuda\"\n",
+        "    if torch.cuda.is_available()\n",
+        "    else \"mps\" if torch.backends.mps.is_available() else \"cpu\"\n",
+        ")\n",
+        "\n",
+        "model_name = \"HuggingFaceTB/SmolLM2-135M\"\n",
+        "model = AutoModelForCausalLM.from_pretrained(\n",
+        "    pretrained_model_name_or_path=model_name\n",
+        ").to(device)\n",
+        "tokenizer = AutoTokenizer.from_pretrained(pretrained_model_name_or_path=model_name)\n",
+        "model, tokenizer = setup_chat_format(model=model, tokenizer=tokenizer)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 6,
+      "metadata": {
+        "id": "zkJwILrbtFls"
+      },
+      "outputs": [],
+      "source": [
+        "# Define messages for SmolLM2\n",
+        "messages = [\n",
+        "    {\"role\": \"user\", \"content\": \"Hello, how are you?\"},\n",
+        "    {\n",
+        "        \"role\": \"assistant\",\n",
+        "        \"content\": \"I'm doing well, thank you! How can I assist you today?\",\n",
+        "    },\n",
+        "]"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "Ve4dgtjstFls"
+      },
+      "source": [
+        "# Apply chat template without tokenization\n",
+        "\n",
+        "The tokenizer represents the conversation as a string with special tokens to describe the role of the user and the assistant.\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 7,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "pbAg-5x-tFls",
+        "outputId": "dd5adad8-6a98-48f8-eaa6-c5271d1225a5"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Conversation with template: <|im_start|>user\n",
+            "Hello, how are you?<|im_end|>\n",
+            "<|im_start|>assistant\n",
+            "I'm doing well, thank you! How can I assist you today?<|im_end|>\n",
+            "\n"
+          ]
+        }
+      ],
+      "source": [
+        "input_text = tokenizer.apply_chat_template(messages, tokenize=False)\n",
+        "\n",
+        "print(\"Conversation with template:\", input_text)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "sfvdglOqtFls"
+      },
+      "source": [
+        "# Decode the conversation\n",
+        "\n",
+        "Note that the conversation is represented as above but with a further assistant message.\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 8,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "mXUVdPeytFls",
+        "outputId": "16c52bee-aa58-4a4c-bd9e-fe0cf3def3e9"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Conversation decoded: <|im_start|>user\n",
+            "Hello, how are you?<|im_end|>\n",
+            "<|im_start|>assistant\n",
+            "I'm doing well, thank you! How can I assist you today?<|im_end|>\n",
+            "<|im_start|>assistant\n",
+            "\n"
+          ]
+        }
+      ],
+      "source": [
+        "input_text = tokenizer.apply_chat_template(\n",
+        "    messages, tokenize=True, add_generation_prompt=True\n",
+        ")\n",
+        "\n",
+        "print(\"Conversation decoded:\", tokenizer.decode(token_ids=input_text))"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "UcZQpspEtFlt"
+      },
+      "source": [
+        "# Tokenize the conversation\n",
+        "\n",
+        "Of course, the tokenizer also tokenizes the conversation and special token as ids that relate to the model's vocabulary.\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 9,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "jc2PLxAMtFlt",
+        "outputId": "450fab64-ad81-4aae-b180-697edc3312ad"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Conversation tokenized: [1, 4093, 198, 19556, 28, 638, 359, 346, 47, 2, 198, 1, 520, 9531, 198, 57, 5248, 2567, 876, 28, 9984, 346, 17, 1073, 416, 339, 4237, 346, 1834, 47, 2, 198, 1, 520, 9531, 198]\n"
+          ]
+        }
+      ],
+      "source": [
+        "input_text = tokenizer.apply_chat_template(messages, add_generation_prompt=True)\n",
+        "\n",
+        "print(\"Conversation tokenized:\", input_text)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "m3eNp9a0tFlt"
+      },
+      "source": [
+        "<div style='background-color: lightblue; padding: 10px; border-radius: 5px; margin-bottom: 20px; color:black'>\n",
+        "    <h2 style='margin: 0;color:blue'>Exercise: Process a dataset for SFT</h2>\n",
+        "    <p>Take a dataset from the Hugging Face hub and process it for SFT. </p>\n",
+        "    <p><b>Difficulty Levels</b></p>\n",
+        "    <p>🐢 Convert the `HuggingFaceTB/smoltalk` dataset into chatml format.</p>\n",
+        "    <p>🐕 Convert the `openai/gsm8k` dataset into chatml format.</p>\n",
+        "</div>"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 10,
+      "metadata": {
+        "collapsed": true,
+        "id": "qbkXV2_ItFlt"
+      },
+      "outputs": [
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            "C:\\Users\\kevin menpara\\AppData\\Local\\Temp\\ipykernel_26164\\4068687702.py:1: DeprecationWarning: Importing display from IPython.core.display is deprecated since IPython 7.14, please import from IPython.display\n",
+            "  from IPython.core.display import display, HTML\n"
+          ]
+        },
+        {
+          "data": {
+            "text/html": [
+              "<iframe\n",
+              "  src=\"https://huggingface.co/datasets/HuggingFaceTB/smoltalk/embed/viewer/all/train?row=0\"\n",
+              "  frameborder=\"0\"\n",
+              "  width=\"100%\"\n",
+              "  height=\"360px\"\n",
+              "></iframe>\n"
+            ],
+            "text/plain": [
+              "<IPython.core.display.HTML object>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "from IPython.core.display import display, HTML\n",
+        "\n",
+        "display(\n",
+        "    HTML(\n",
+        "        \"\"\"<iframe\n",
+        "  src=\"https://huggingface.co/datasets/HuggingFaceTB/smoltalk/embed/viewer/all/train?row=0\"\n",
+        "  frameborder=\"0\"\n",
+        "  width=\"100%\"\n",
+        "  height=\"360px\"\n",
+        "></iframe>\n",
+        "\"\"\"\n",
+        "    )\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 11,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "4p3atw4_tFlu",
+        "outputId": "c90a92f0-2bb7-4691-e1e8-a5d2a8d641af"
+      },
+      "outputs": [
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            "Map: 100%|██████████| 2260/2260 [00:00<00:00, 10706.27 examples/s]\n",
+            "Map: 100%|██████████| 119/119 [00:00<00:00, 2495.12 examples/s]\n"
+          ]
+        },
+        {
+          "data": {
+            "text/plain": [
+              "{'full_topic': 'Work/Career development/Mentorship',\n",
+              " 'messages': [{'content': 'Hi', 'role': 'user'},\n",
+              "  {'content': 'Hello! How can I help you today?', 'role': 'assistant'},\n",
+              "  {'content': \"I'm looking for career advice. I want to find a new job, but I'm not sure what I want to do.\",\n",
+              "   'role': 'user'},\n",
+              "  {'content': 'Career development can be challenging. What are your current skills and interests that might help narrow down some options?',\n",
+              "   'role': 'assistant'},\n",
+              "  {'content': \"I have experience in marketing and enjoy working with people. I'm also interested in learning more about data analysis.\",\n",
+              "   'role': 'user'},\n",
+              "  {'content': \"That's a great combination. You might consider roles like marketing analyst or business development, which combine people skills with data analysis. I can provide more information on those careers if you'd like.\",\n",
+              "   'role': 'assistant'},\n",
+              "  {'content': 'That sounds helpful. Can you also suggest some resources for learning data analysis?',\n",
+              "   'role': 'user'},\n",
+              "  {'content': 'Absolutely. Online courses like Coursera, edX, and LinkedIn Learning offer a wide range of data analysis courses. Additionally, you can explore professional networks like LinkedIn groups or attend industry events to learn from others in the field.',\n",
+              "   'role': 'assistant'}]}"
+            ]
+          },
+          "execution_count": 11,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "from datasets import load_dataset\n",
+        "\n",
+        "ds = load_dataset(\"HuggingFaceTB/smoltalk\", \"everyday-conversations\")\n",
+        "\n",
+        "\n",
+        "def process_dataset(sample):\n",
+        "    # TODO: 🐢 Convert the sample into a chat format\n",
+        "    \n",
+        "    return sample\n",
+        "\n",
+        "\n",
+        "ds = ds.map(process_dataset)\n",
+        "ds[\"train\"][1]"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 13,
+      "metadata": {
+        "id": "81fQeazltFlu"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/html": [
+              "<iframe\n",
+              "  src=\"https://huggingface.co/datasets/openai/gsm8k/embed/viewer/main/train\"\n",
+              "  frameborder=\"0\"\n",
+              "  width=\"100%\"\n",
+              "  height=\"360px\"\n",
+              "></iframe>\n"
+            ],
+            "text/plain": [
+              "<IPython.core.display.HTML object>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "display(\n",
+        "    HTML(\n",
+        "        \"\"\"<iframe\n",
+        "  src=\"https://huggingface.co/datasets/openai/gsm8k/embed/viewer/main/train\"\n",
+        "  frameborder=\"0\"\n",
+        "  width=\"100%\"\n",
+        "  height=\"360px\"\n",
+        "></iframe>\n",
+        "\"\"\"\n",
+        "    )\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 12,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "collapsed": true,
+        "id": "bWUSv7NMtFlu",
+        "outputId": "e3d5bba4-4356-44fd-bad8-57f56099d7fe"
+      },
+      "outputs": [
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            "Map: 100%|██████████| 7473/7473 [00:10<00:00, 705.25 examples/s]\n",
+            "Map: 100%|██████████| 1319/1319 [00:01<00:00, 741.44 examples/s]\n"
+          ]
+        }
+      ],
+      "source": [
+        "ds = load_dataset(\"openai/gsm8k\", \"main\")\n",
+        "# print(ds['train'][0])\n",
+        "#Dataset -> '\n",
+        "           # train' , 'test'->\n",
+        "                                # 'features' -> \n",
+        "                                                 # 'question' , 'answer'      \n",
+        "\n",
+        "\n",
+        "def process_dataset(sample):\n",
+        "    # Create a message column with the role and content\n",
+        "    messages = []\n",
+        "    if sample['question']:\n",
+        "        messages.append({\n",
+        "            'role': 'user',\n",
+        "            'content': sample['question']\n",
+        "        })\n",
+        "    if sample['answer']:\n",
+        "        messages.append({\n",
+        "            'role': 'assistant',\n",
+        "            'content': sample['answer']\n",
+        "        })\n",
+        "    \n",
+        "    sample['messages'] = messages\n",
+        "    \n",
+        "    # Tokenized data    \n",
+        "    tokenized_data = tokenizer.apply_chat_template(\n",
+        "        messages,\n",
+        "        add_generation_prompt=True\n",
+        "    )\n",
+        "\n",
+        "    return {\n",
+        "        'tokenized_messages': tokenized_data\n",
+        "    }\n",
+        "\n",
+        "# Apply the process_dataset function to the training set\n",
+        "ds = ds.map(process_dataset)\n",
+        "\n",
+        "# Access the first tokenized message\n",
+        "# print(ds['train'][0]['tokenized_messages'])"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "qlXCuRKotFlu"
+      },
+      "source": [
+        "## Conclusion\n",
+        "\n",
+        "This notebook demonstrated how to apply chat templates to different models, `SmolLM2`. By structuring interactions with chat templates, we can ensure that AI models provide consistent and contextually relevant responses.\n",
+        "\n",
+        "In the exercise you tried out converting a dataset into chatml format. Luckily, TRL will do this for you, but it's useful to understand what's going on under the hood."
+      ]
+    }
+  ],
+  "metadata": {
+    "accelerator": "GPU",
+    "colab": {
+      "gpuType": "T4",
+      "provenance": []
+    },
+    "kernelspec": {
+      "display_name": ".venv",
+      "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": "3.11.3"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}