Copy_of_gpt_llm_trainer.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/zrshen/ML_AWS/blob/main/Copy_of_gpt_llm_trainer.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Describe your model -> fine-tuned LLaMA 2\n",
        "By Matt Shumer (https://twitter.com/mattshumer_)\n",
        "\n",
        "The goal of this notebook is to experiment with a new way to make it very easy to build a task-specific model for your use-case.\n",
        "\n",
        "First, use the best GPU available (go to Runtime -> change runtime type)\n",
        "\n",
        "To create your model, just go to the first code cell, and describe the model you want to build in the prompt. Be descriptive and clear.\n",
        "\n",
        "Select a temperature (high=creative, low=precise), and the number of training examples to generate to train the model. From there, just run all the cells.\n",
        "\n",
        "You can change the model you want to fine-tune by changing `model_name` in the `Define Hyperparameters` cell."
      ],
      "metadata": {
        "id": "wM8MRkf8Dr94"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Data generation step"
      ],
      "metadata": {
        "id": "Way3_PuPpIuE"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Write your prompt here. Make it as descriptive as possible!\n",
        "\n",
        "Then, choose the temperature (between 0 and 1) to use when generating data. Lower values are great for precise tasks, like writing code, whereas larger values are better for creative tasks, like writing stories.\n",
        "\n",
        "Finally, choose how many examples you want to generate. The more you generate, a) the longer it takes and b) the more expensive data generation will be. But generally, more examples will lead to a higher-quality model. 100 is usually the minimum to start."
      ],
      "metadata": {
        "id": "lY-3DvlIpVSl"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "prompt = \"A model that takes in a puzzle-like reasoning-heavy question in English, and responds with a well-reasoned, step-by-step thought out response in Spanish.\"\n",
        "temperature = .4\n",
        "number_of_examples = 10"
      ],
      "metadata": {
        "id": "R7WKZyxtpUPS"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Run this to generate the dataset."
      ],
      "metadata": {
        "id": "1snNou5PrIci"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install openai"
      ],
      "metadata": {
        "id": "zuL2UaqlsmBD",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3a5f9ecf-6b33-403d-bd19-293a8d09786c"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: openai in /usr/local/lib/python3.10/dist-packages (0.28.0)\n",
            "Requirement already satisfied: requests>=2.20 in /usr/local/lib/python3.10/dist-packages (from openai) (2.31.0)\n",
            "Requirement already satisfied: tqdm in /usr/local/lib/python3.10/dist-packages (from openai) (4.66.1)\n",
            "Requirement already satisfied: aiohttp in /usr/local/lib/python3.10/dist-packages (from openai) (3.8.5)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests>=2.20->openai) (3.2.0)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests>=2.20->openai) (3.4)\n",
            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests>=2.20->openai) (2.0.4)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests>=2.20->openai) (2023.7.22)\n",
            "Requirement already satisfied: attrs>=17.3.0 in /usr/local/lib/python3.10/dist-packages (from aiohttp->openai) (23.1.0)\n",
            "Requirement already satisfied: multidict<7.0,>=4.5 in /usr/local/lib/python3.10/dist-packages (from aiohttp->openai) (6.0.4)\n",
            "Requirement already satisfied: async-timeout<5.0,>=4.0.0a3 in /usr/local/lib/python3.10/dist-packages (from aiohttp->openai) (4.0.3)\n",
            "Requirement already satisfied: yarl<2.0,>=1.0 in /usr/local/lib/python3.10/dist-packages (from aiohttp->openai) (1.9.2)\n",
            "Requirement already satisfied: frozenlist>=1.1.1 in /usr/local/lib/python3.10/dist-packages (from aiohttp->openai) (1.4.0)\n",
            "Requirement already satisfied: aiosignal>=1.1.2 in /usr/local/lib/python3.10/dist-packages (from aiohttp->openai) (1.3.1)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "import openai\n",
        "import random\n",
        "\n",
        "openai.api_key = \"xxx\"\n",
        "\n",
        "def generate_example(prompt, prev_examples, temperature=.5):\n",
        "    messages=[\n",
        "        {\n",
        "            \"role\": \"system\",\n",
        "            \"content\": f\"You are generating data which will be used to train a machine learning model.\\n\\nYou will be given a high-level description of the model we want to train, and from that, you will generate data samples, each with a prompt/response pair.\\n\\nYou will do so in this format:\\n```\\nprompt\\n-----------\\n$prompt_goes_here\\n-----------\\n\\nresponse\\n-----------\\n$response_goes_here\\n-----------\\n```\\n\\nOnly one prompt/response pair should be generated per turn.\\n\\nFor each turn, make the example slightly more complex than the last, while ensuring diversity.\\n\\nMake sure your samples are unique and diverse, yet high-quality and complex enough to train a well-performing model.\\n\\nHere is the type of model we want to train:\\n`{prompt}`\"\n",
        "        }\n",
        "    ]\n",
        "\n",
        "    if len(prev_examples) > 0:\n",
        "        if len(prev_examples) > 10:\n",
        "            prev_examples = random.sample(prev_examples, 10)\n",
        "        for example in prev_examples:\n",
        "            messages.append({\n",
        "                \"role\": \"assistant\",\n",
        "                \"content\": example\n",
        "            })\n",
        "\n",
        "    response = openai.ChatCompletion.create(\n",
        "        model=\"gpt-4\",\n",
        "        messages=messages,\n",
        "        temperature=temperature,\n",
        "        max_tokens=1354,\n",
        "    )\n",
        "\n",
        "    return response.choices[0].message['content']\n",
        "\n",
        "# Generate examples\n",
        "prev_examples = []\n",
        "for i in range(number_of_examples):\n",
        "    print(f'Generating example {i}')\n",
        "    example = generate_example(prompt, prev_examples, temperature)\n",
        "    prev_examples.append(example)\n",
        "\n",
        "print(prev_examples)"
      ],
      "metadata": {
        "id": "Rdsd82ngpHCG",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "64739d27-4638-4fae-b0ad-4927d6d33a14"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Generating example 0\n",
            "Generating example 1\n",
            "Generating example 2\n",
            "Generating example 3\n",
            "Generating example 4\n",
            "Generating example 5\n",
            "Generating example 6\n",
            "Generating example 7\n",
            "Generating example 8\n",
            "Generating example 9\n",
            "['prompt\\n-----------\\nIf a train leaves the station at 10am and travels at a speed of 60 miles per hour, and another train leaves the same station at 11am and travels at a speed of 75 miles per hour, at what time will the second train catch up to the first?\\n-----------\\nresponse\\n-----------\\nPrimero, necesitamos calcular cuánto tiempo le tomará al segundo tren alcanzar al primero. Para hacer esto, necesitamos saber cuánta distancia ha recorrido el primer tren antes de que el segundo tren comience a moverse. \\n\\nEl primer tren comienza a moverse a las 10 am y el segundo tren comienza a moverse a las 11 am, por lo que el primer tren ha estado en movimiento durante una hora. Dado que el primer tren se mueve a 60 millas por hora, ha recorrido 60 millas en esa hora.\\n\\nAhora, necesitamos calcular cuánto tiempo le tomará al segundo tren recorrer esa distancia de 60 millas. El segundo tren se mueve a 75 millas por hora, por lo que dividimos la distancia total que necesita recorrer (60 millas) por su velocidad (75 millas por hora) para obtener el tiempo que le tomará alcanzar al primer tren. \\n\\n60 millas / 75 millas por hora = 0.8 horas \\n\\nPor lo tanto, el segundo tren alcanzará al primer tren 0.8 horas después de las 11 am, que es aproximadamente a las 11:48 am.\\n-----------', \"prompt\\n-----------\\nA rectangle's length is three times its width. If the perimeter of the rectangle is 80 cm, what are the dimensions of the rectangle?\\n-----------\\nresponse\\n-----------\\nPrimero, necesitamos entender la fórmula para el perímetro de un rectángulo, que es 2*(longitud + ancho). En este caso, sabemos que el perímetro es 80 cm.\\n\\nAdemás, sabemos que la longitud del rectángulo es tres veces su ancho. Podemos expresar esto como longitud = 3 * ancho.\\n\\nPodemos sustituir esta información en nuestra fórmula del perímetro para obtener 2*(3*ancho + ancho) = 80. Esto se simplifica a 8*ancho = 80.\\n\\nPara encontrar el ancho, dividimos ambos lados de la ecuación por 8, lo que nos da ancho = 10 cm.\\n\\nAhora que tenemos el ancho, podemos encontrar la longitud multiplicando el ancho por 3, lo que nos da longitud = 30 cm.\\n\\nPor lo tanto, las dimensiones del rectángulo son 30 cm de longitud y 10 cm de ancho.\\n-----------\", 'prompt\\n-----------\\nA tank can be filled by one tap in 10 hours and by another in 12 hours. Both the taps are kept open. In how many hours will the tank be filled?\\n-----------\\nresponse\\n-----------\\nPara resolver este problema, primero necesitamos entender cuánto de la cisterna puede llenar cada grifo en una hora. \\n\\nEl primer grifo puede llenar 1/10 de la cisterna en una hora, y el segundo grifo puede llenar 1/12 de la cisterna en una hora.\\n\\nSi ambos grifos están abiertos al mismo tiempo, entonces en una hora, juntos pueden llenar 1/10 + 1/12 de la cisterna. Para sumar estas dos fracciones, necesitamos un denominador común. El mínimo común múltiplo de 10 y 12 es 60, por lo que convertimos ambas fracciones a tener este denominador común.\\n\\nEsto nos da 6/60 + 5/60 = 11/60. Por lo tanto, juntos, los grifos pueden llenar 11/60 de la cisterna en una hora.\\n\\nPara encontrar cuánto tiempo tomará llenar toda la cisterna, dividimos 1 (la cisterna completa) por 11/60. Esto nos da aproximadamente 5.45 horas.\\n\\nPor lo tanto, si ambos grifos están abiertos, la cisterna se llenará en aproximadamente 5.45 horas.\\n-----------', 'prompt\\n-----------\\nA man has 53 socks in his drawer: 21 identical blue, 15 identical black and 17 identical red. The room is dark and he wants to get his socks. What is the minimum number of socks that he has to take to be sure he has a pair of the same color?\\n-----------\\nresponse\\n-----------\\nPara asegurarse de tener un par del mismo color, el hombre tendría que sacar un calcetín de cada color y luego un calcetín adicional. \\n\\nEsto se debe a que, después de sacar un calcetín de cada color (azul, negro y rojo), el próximo calcetín que saque, sin importar el color, garantizará que tenga al menos un par del mismo color.\\n\\nPor lo tanto, el número mínimo de calcetines que tendría que sacar para asegurarse de tener un par del mismo color sería 4.\\n-----------', \"prompt\\n-----------\\nA car travels from Town A to Town B at a speed of 60 km/h, and returns to Town A at a speed of 40 km/h. If the total journey took 10 hours, what is the distance between Town A and Town B?\\n-----------\\nresponse\\n-----------\\nPara resolver este problema, primero necesitamos entender que el tiempo total de viaje es la suma del tiempo que se tarda en ir de la ciudad A a la ciudad B y el tiempo que se tarda en regresar.\\n\\nPodemos expresar el tiempo de viaje como la distancia dividida por la velocidad. Por lo tanto, el tiempo para ir de la ciudad A a la ciudad B es la distancia (que llamaremos 'd') dividida por 60 km/h, y el tiempo para regresar es la misma distancia 'd' dividida por 40 km/h.\\n\\nPor lo tanto, la ecuación que tenemos es: d/60 + d/40 = 10.\\n\\nPara resolver esta ecuación para 'd', primero necesitamos encontrar un denominador común para las fracciones, que es 240. Esto nos da 4d/240 + 6d/240 = 10, que se simplifica a 10d/240 = 10.\\n\\nSi multiplicamos ambos lados de la ecuación por 240, obtenemos 10d = 2400, y si dividimos ambos lados de la ecuación por 10, obtenemos d = 240.\\n\\nPor lo tanto, la distancia entre la ciudad A y la ciudad B es de 240 km.\\n-----------\", 'prompt\\n-----------\\nA group of friends decided to go on a trip. If each friend can drive a car that can fit 5 people and the group is made up of 27 people, how many cars will they need?\\n-----------\\nresponse\\n-----------\\nPara resolver este problema, primero necesitamos entender cuántas personas puede llevar cada coche. Cada coche puede llevar a 5 personas.\\n\\nEntonces, necesitamos dividir el número total de personas en el grupo (27) por la cantidad de personas que puede llevar cada coche (5). \\n\\n27 dividido por 5 es 5.4. Como no podemos tener un número fraccionario de coches, necesitamos redondear este número al siguiente número entero más alto, que es 6.\\n\\nPor lo tanto, necesitarán 6 coches para llevar a las 27 personas.\\n-----------', 'prompt\\n-----------\\nA man buys an article for $600 and sells it at a gain of 20%. What is the selling price of the article?\\n-----------\\nresponse\\n-----------\\nPara resolver este problema, primero necesitamos calcular cuánto es el 20% del precio de compra del artículo, que es $600.\\n\\nEl 20% de $600 es $120.\\n\\nLuego, para encontrar el precio de venta del artículo, sumamos este beneficio al precio de compra original del artículo.\\n\\nPor lo tanto, el precio de venta del artículo es $600 + $120 = $720.\\n-----------', 'prompt\\n-----------\\nA snail climbs up a 10m wall. Every day the snail climbs 3m but at night, while sleeping, it slides down 2m. How many days does it take for the snail to get out of the wall?\\n-----------\\nresponse\\n-----------\\nPara resolver este problema, primero necesitamos entender cuánto avanza el caracol cada día. Durante el día, el caracol sube 3m, pero por la noche, mientras duerme, se desliza 2m. Por lo tanto, el caracol avanza 1m (3m - 2m) cada día.\\n\\nSin embargo, debemos tener en cuenta que en el último día, cuando el caracol llega a la cima de la pared de 10m, no se deslizará hacia abajo por la noche. Por lo tanto, en el último día, el caracol subirá 3m en lugar de 1m.\\n\\nPor lo tanto, el caracol tardará 7 días (6 días para subir los primeros 7m a una tasa de 1m por día, y luego 1 día adicional para subir los últimos 3m a una tasa de 3m por día) para salir de la pared.\\n\\nPor lo tanto, el caracol tardará 7 días en total para salir de la pared.\\n-----------', 'prompt\\n-----------\\nA man sells two cows for $400 each. On one, he gains 20% and on the other, he loses 20%. What is his overall gain or loss?\\n-----------\\nresponse\\n-----------\\nPara resolver este problema, primero necesitamos calcular cuánto pagó originalmente por cada vaca. \\n\\nPara la vaca en la que ganó un 20%, si la vendió por $400 y eso representa un 120% del costo original (100% del costo original + 20% de ganancia), entonces el costo original fue $400 / 1.2 = $333.33.\\n\\nPara la vaca en la que perdió un 20%, si la vendió por $400 y eso representa un 80% del costo original (100% del costo original - 20% de pérdida), entonces el costo original fue $400 / 0.8 = $500.\\n\\nPor lo tanto, el hombre pagó $333.33 + $500 = $833.33 por las dos vacas.\\n\\nLuego, vendió las dos vacas por un total de $400 + $400 = $800.\\n\\nPor lo tanto, su pérdida total fue de $833.33 - $800 = $33.33.\\n-----------', \"prompt\\n-----------\\nA man has 7 friends. He wants to invite 3 of them to a party. In how many ways can he invite them?\\n-----------\\nresponse\\n-----------\\nPara resolver este problema, necesitamos usar la fórmula de combinaciones, que es nCr = n! / [(n-r)! * r!], donde n es el número total de elementos, r es el número de elementos a seleccionar, y '!' denota el factorial.\\n\\nEn este caso, el hombre tiene 7 amigos (n=7) y quiere invitar a 3 de ellos (r=3).\\n\\nPor lo tanto, el número de formas en que puede invitar a 3 amigos es 7C3 = 7! / [(7-3)! * 3!] = 7*6*5 / 3*2*1 = 35.\\n\\nPor lo tanto, hay 35 formas en que el hombre puede invitar a 3 de sus 7 amigos a la fiesta.\\n-----------\"]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "We also need to generate a system message."
      ],
      "metadata": {
        "id": "KC6iJzXjugJ-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def generate_system_message(prompt):\n",
        "\n",
        "    response = openai.ChatCompletion.create(\n",
        "        model=\"gpt-4\",\n",
        "        messages=[\n",
        "          {\n",
        "            \"role\": \"system\",\n",
        "            \"content\": \"You will be given a high-level description of the model we are training, and from that, you will generate a simple system prompt for that model to use. Remember, you are not generating the system message for data generation -- you are generating the system message to use for inference. A good format to follow is `Given $INPUT_DATA, you will $WHAT_THE_MODEL_SHOULD_DO.`.\\n\\nMake it as concise as possible. Include nothing but the system prompt in your response.\\n\\nFor example, never write: `\\\"$SYSTEM_PROMPT_HERE\\\"`.\\n\\nIt should be like: `$SYSTEM_PROMPT_HERE`.\"\n",
        "          },\n",
        "          {\n",
        "              \"role\": \"user\",\n",
        "              \"content\": prompt.strip(),\n",
        "          }\n",
        "        ],\n",
        "        temperature=temperature,\n",
        "        max_tokens=500,\n",
        "    )\n",
        "\n",
        "    return response.choices[0].message['content']\n",
        "\n",
        "system_message = generate_system_message(prompt)\n",
        "\n",
        "print(f'The system message is: `{system_message}`. Feel free to re-run this cell if you want a better result.')"
      ],
      "metadata": {
        "id": "xMcfhW6Guh2E",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "21cbd0ba-2480-47e2-9868-84de96751a8c"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The system message is: `Given an English puzzle-like reasoning-heavy question, you will provide a well-reasoned, step-by-step response in Spanish.`. Feel free to re-run this cell if you want a better result.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Now let's put our examples into a dataframe and turn them into a final pair of datasets."
      ],
      "metadata": {
        "id": "G6BqZ-hjseBF"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "\n",
        "# Initialize lists to store prompts and responses\n",
        "prompts = []\n",
        "responses = []\n",
        "\n",
        "# Parse out prompts and responses from examples\n",
        "for example in prev_examples:\n",
        "  try:\n",
        "    split_example = example.split('-----------')\n",
        "    prompts.append(split_example[1].strip())\n",
        "    responses.append(split_example[3].strip())\n",
        "  except:\n",
        "    pass\n",
        "\n",
        "# Create a DataFrame\n",
        "df = pd.DataFrame({\n",
        "    'prompt': prompts,\n",
        "    'response': responses\n",
        "})\n",
        "\n",
        "# Remove duplicates\n",
        "df = df.drop_duplicates()\n",
        "\n",
        "print('There are ' + str(len(df)) + ' successfully-generated examples. Here are the first few:')\n",
        "\n",
        "df.head(10)"
      ],
      "metadata": {
        "id": "7CEdkYeRsdmB",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 380
        },
        "outputId": "7f021770-18c2-4af1-b0c4-f45b834f3964"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "There are 10 successfully-generated examples. Here are the first few:\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                              prompt  \\\n",
              "0  If a train leaves the station at 10am and trav...   \n",
              "1  A rectangle's length is three times its width....   \n",
              "2  A tank can be filled by one tap in 10 hours an...   \n",
              "3  A man has 53 socks in his drawer: 21 identical...   \n",
              "4  A car travels from Town A to Town B at a speed...   \n",
              "5  A group of friends decided to go on a trip. If...   \n",
              "6  A man buys an article for $600 and sells it at...   \n",
              "7  A snail climbs up a 10m wall. Every day the sn...   \n",
              "8  A man sells two cows for $400 each. On one, he...   \n",
              "9  A man has 7 friends. He wants to invite 3 of t...   \n",
              "\n",
              "                                            response  \n",
              "0  Primero, necesitamos calcular cuánto tiempo le...  \n",
              "1  Primero, necesitamos entender la fórmula para ...  \n",
              "2  Para resolver este problema, primero necesitam...  \n",
              "3  Para asegurarse de tener un par del mismo colo...  \n",
              "4  Para resolver este problema, primero necesitam...  \n",
              "5  Para resolver este problema, primero necesitam...  \n",
              "6  Para resolver este problema, primero necesitam...  \n",
              "7  Para resolver este problema, primero necesitam...  \n",
              "8  Para resolver este problema, primero necesitam...  \n",
              "9  Para resolver este problema, necesitamos usar ...  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-17e2837c-81cf-488a-bdca-46378461e923\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>prompt</th>\n",
              "      <th>response</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>If a train leaves the station at 10am and trav...</td>\n",
              "      <td>Primero, necesitamos calcular cuánto tiempo le...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>A rectangle's length is three times its width....</td>\n",
              "      <td>Primero, necesitamos entender la fórmula para ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>A tank can be filled by one tap in 10 hours an...</td>\n",
              "      <td>Para resolver este problema, primero necesitam...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>A man has 53 socks in his drawer: 21 identical...</td>\n",
              "      <td>Para asegurarse de tener un par del mismo colo...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>A car travels from Town A to Town B at a speed...</td>\n",
              "      <td>Para resolver este problema, primero necesitam...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>A group of friends decided to go on a trip. If...</td>\n",
              "      <td>Para resolver este problema, primero necesitam...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>A man buys an article for $600 and sells it at...</td>\n",
              "      <td>Para resolver este problema, primero necesitam...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>A snail climbs up a 10m wall. Every day the sn...</td>\n",
              "      <td>Para resolver este problema, primero necesitam...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>A man sells two cows for $400 each. On one, he...</td>\n",
              "      <td>Para resolver este problema, primero necesitam...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>A man has 7 friends. He wants to invite 3 of t...</td>\n",
              "      <td>Para resolver este problema, necesitamos usar ...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-17e2837c-81cf-488a-bdca-46378461e923')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-17e2837c-81cf-488a-bdca-46378461e923 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-17e2837c-81cf-488a-bdca-46378461e923');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-26276798-b6b9-476f-b5d1-c21948415104\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-26276798-b6b9-476f-b5d1-c21948415104')\"\n",
              "            title=\"Suggest charts.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-26276798-b6b9-476f-b5d1-c21948415104 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Split into train and test sets."
      ],
      "metadata": {
        "id": "A-8dt5qqtpgM"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Split the data into train and test sets, with 90% in the train set\n",
        "train_df = df.sample(frac=0.9, random_state=42)\n",
        "test_df = df.drop(train_df.index)\n",
        "\n",
        "# Save the dataframes to .jsonl files\n",
        "train_df.to_json('train.jsonl', orient='records', lines=True)\n",
        "test_df.to_json('test.jsonl', orient='records', lines=True)"
      ],
      "metadata": {
        "id": "GFPEn1omtrXM"
      },
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Install necessary libraries"
      ],
      "metadata": {
        "id": "AbrFgrhG_xYi"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install -q accelerate==0.21.0 peft==0.4.0 bitsandbytes==0.40.2 transformers==4.31.0 trl==0.4.7\n",
        "\n",
        "import os\n",
        "import torch\n",
        "from datasets import load_dataset\n",
        "from transformers import (\n",
        "    AutoModelForCausalLM,\n",
        "    AutoTokenizer,\n",
        "    BitsAndBytesConfig,\n",
        "    HfArgumentParser,\n",
        "    TrainingArguments,\n",
        "    pipeline,\n",
        "    logging,\n",
        ")\n",
        "from peft import LoraConfig, PeftModel\n",
        "from trl import SFTTrainer"
      ],
      "metadata": {
        "id": "lPG7wEPetFx2",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "0ec2c2ed-5297-44d8-84f4-750715be3a0d"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[?25l     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/244.2 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K     \u001b[91m━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[91m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━\u001b[0m \u001b[32m153.6/244.2 kB\u001b[0m \u001b[31m4.4 MB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0m\r\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m244.2/244.2 kB\u001b[0m \u001b[31m5.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m72.9/72.9 kB\u001b[0m \u001b[31m8.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m92.5/92.5 MB\u001b[0m \u001b[31m9.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m7.4/7.4 MB\u001b[0m \u001b[31m76.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m77.4/77.4 kB\u001b[0m \u001b[31m9.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.3/1.3 MB\u001b[0m \u001b[31m78.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m294.9/294.9 kB\u001b[0m \u001b[31m34.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m7.8/7.8 MB\u001b[0m \u001b[31m114.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m519.6/519.6 kB\u001b[0m \u001b[31m43.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m115.3/115.3 kB\u001b[0m \u001b[31m16.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m194.1/194.1 kB\u001b[0m \u001b[31m23.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m134.8/134.8 kB\u001b[0m \u001b[31m17.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Define Hyperparameters"
      ],
      "metadata": {
        "id": "moVo0led-6tu"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model_name = \"NousResearch/llama-2-7b-chat-hf\" # use this if you have access to the official LLaMA 2 model \"meta-llama/Llama-2-7b-chat-hf\", though keep in mind you'll need to pass a Hugging Face key argument\n",
        "dataset_name = \"/content/train.jsonl\"\n",
        "new_model = \"llama-2-7b-custom\"\n",
        "lora_r = 64\n",
        "lora_alpha = 16\n",
        "lora_dropout = 0.1\n",
        "use_4bit = True\n",
        "bnb_4bit_compute_dtype = \"float16\"\n",
        "bnb_4bit_quant_type = \"nf4\"\n",
        "use_nested_quant = False\n",
        "output_dir = \"./results\"\n",
        "num_train_epochs = 1\n",
        "fp16 = False\n",
        "bf16 = False\n",
        "per_device_train_batch_size = 4\n",
        "per_device_eval_batch_size = 4\n",
        "gradient_accumulation_steps = 1\n",
        "gradient_checkpointing = True\n",
        "max_grad_norm = 0.3\n",
        "learning_rate = 2e-4\n",
        "weight_decay = 0.001\n",
        "optim = \"paged_adamw_32bit\"\n",
        "lr_scheduler_type = \"constant\"\n",
        "max_steps = -1\n",
        "warmup_ratio = 0.03\n",
        "group_by_length = True\n",
        "save_steps = 25\n",
        "logging_steps = 5\n",
        "max_seq_length = None\n",
        "packing = False\n",
        "device_map = {\"\": 0}"
      ],
      "metadata": {
        "id": "bqfbhUZI-4c_"
      },
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Load Datasets and Train"
      ],
      "metadata": {
        "id": "F-J5p5KS_MZY"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Load datasets\n",
        "train_dataset = load_dataset('json', data_files='/content/train.jsonl', split=\"train\")\n",
        "valid_dataset = load_dataset('json', data_files='/content/test.jsonl', split=\"train\")\n",
        "\n",
        "# Preprocess datasets\n",
        "train_dataset_mapped = train_dataset.map(lambda examples: {'text': [f'[INST] <<SYS>>\\n{system_message.strip()}\\n<</SYS>>\\n\\n' + prompt + ' [/INST] ' + response for prompt, response in zip(examples['prompt'], examples['response'])]}, batched=True)\n",
        "valid_dataset_mapped = valid_dataset.map(lambda examples: {'text': [f'[INST] <<SYS>>\\n{system_message.strip()}\\n<</SYS>>\\n\\n' + prompt + ' [/INST] ' + response for prompt, response in zip(examples['prompt'], examples['response'])]}, batched=True)\n"
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        {
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              "Downloading data files:   0%|          | 0/1 [00:00<?, ?it/s]"
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    },
    {
      "cell_type": "code",
      "source": [
        "train_dataset_mapped[0][\"text\"]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 174
        },
        "id": "K0Hdo8V3ycwc",
        "outputId": "e7f3b9fb-7779-4eb2-b42f-d2d76d840b52"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'[INST] <<SYS>>\\nGiven an English puzzle-like reasoning-heavy question, you will provide a well-reasoned, step-by-step response in Spanish.\\n<</SYS>>\\n\\nA man sells two cows for $400 each. On one, he gains 20% and on the other, he loses 20%. What is his overall gain or loss? [/INST] Para resolver este problema, primero necesitamos calcular cuánto pagó originalmente por cada vaca. \\n\\nPara la vaca en la que ganó un 20%, si la vendió por $400 y eso representa un 120% del costo original (100% del costo original + 20% de ganancia), entonces el costo original fue $400 / 1.2 = $333.33.\\n\\nPara la vaca en la que perdió un 20%, si la vendió por $400 y eso representa un 80% del costo original (100% del costo original - 20% de pérdida), entonces el costo original fue $400 / 0.8 = $500.\\n\\nPor lo tanto, el hombre pagó $333.33 + $500 = $833.33 por las dos vacas.\\n\\nLuego, vendió las dos vacas por un total de $400 + $400 = $800.\\n\\nPor lo tanto, su pérdida total fue de $833.33 - $800 = $33.33.'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "compute_dtype = getattr(torch, bnb_4bit_compute_dtype)\n",
        "bnb_config = BitsAndBytesConfig(\n",
        "    load_in_4bit=use_4bit,\n",
        "    bnb_4bit_quant_type=bnb_4bit_quant_type,\n",
        "    bnb_4bit_compute_dtype=compute_dtype,\n",
        "    bnb_4bit_use_double_quant=use_nested_quant,\n",
        ")\n",
        "model = AutoModelForCausalLM.from_pretrained(\n",
        "    model_name,\n",
        "    quantization_config=bnb_config,\n",
        "    device_map=device_map\n",
        ")\n",
        "model.config.use_cache = False\n",
        "model.config.pretraining_tp = 1\n",
        "tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)\n",
        "tokenizer.pad_token = tokenizer.eos_token\n",
        "tokenizer.padding_side = \"right\"\n",
        "peft_config = LoraConfig(\n",
        "    lora_alpha=lora_alpha,\n",
        "    lora_dropout=lora_dropout,\n",
        "    r=lora_r,\n",
        "    bias=\"none\",\n",
        "    task_type=\"CAUSAL_LM\",\n",
        ")\n",
        "# Set training parameters\n",
        "training_arguments = TrainingArguments(\n",
        "    output_dir=output_dir,\n",
        "    num_train_epochs=num_train_epochs,\n",
        "    per_device_train_batch_size=per_device_train_batch_size,\n",
        "    gradient_accumulation_steps=gradient_accumulation_steps,\n",
        "    optim=optim,\n",
        "    save_steps=save_steps,\n",
        "    logging_steps=logging_steps,\n",
        "    learning_rate=learning_rate,\n",
        "    weight_decay=weight_decay,\n",
        "    fp16=fp16,\n",
        "    bf16=bf16,\n",
        "    max_grad_norm=max_grad_norm,\n",
        "    max_steps=max_steps,\n",
        "    warmup_ratio=warmup_ratio,\n",
        "    group_by_length=group_by_length,\n",
        "    lr_scheduler_type=lr_scheduler_type,\n",
        "    report_to=\"all\",\n",
        "    evaluation_strategy=\"steps\",\n",
        "    eval_steps=5  # Evaluate every 20 steps\n",
        ")\n",
        "# Set supervised fine-tuning parameters\n",
        "trainer = SFTTrainer(\n",
        "    model=model,\n",
        "    train_dataset=train_dataset_mapped,\n",
        "    eval_dataset=valid_dataset_mapped,  # Pass validation dataset here\n",
        "    peft_config=peft_config,\n",
        "    dataset_text_field=\"text\",\n",
        "    max_seq_length=max_seq_length,\n",
        "    tokenizer=tokenizer,\n",
        "    args=training_arguments,\n",
        "    packing=packing,\n",
        ")\n",
        "trainer.train()\n",
        "trainer.model.save_pretrained(new_model)\n",
        "\n",
        "# Cell 4: Test the model\n",
        "logging.set_verbosity(logging.CRITICAL)\n",
        "prompt = f\"[INST] <<SYS>>\\n{system_message}\\n<</SYS>>\\n\\nWrite a function that reverses a string. [/INST]\" # replace the command here with something relevant to your task\n",
        "pipe = pipeline(task=\"text-generation\", model=model, tokenizer=tokenizer, max_length=200)\n",
        "result = pipe(prompt)\n",
        "print(result[0]['generated_text'])"
      ],
      "metadata": {
        "id": "qf1qxbiF-x6p"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "7Ov8FW46yVH4"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Run Inference"
      ],
      "metadata": {
        "id": "F6fux9om_c4-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import pipeline\n",
        "\n",
        "prompt = f\"[INST] <<SYS>>\\n{system_message}\\n<</SYS>>\\n\\nWrite a function that reverses a string. [/INST]\" # replace the command here with something relevant to your task\n",
        "num_new_tokens = 100  # change to the number of new tokens you want to generate\n",
        "\n",
        "# Count the number of tokens in the prompt\n",
        "num_prompt_tokens = len(tokenizer(prompt)['input_ids'])\n",
        "\n",
        "# Calculate the maximum length for the generation\n",
        "max_length = num_prompt_tokens + num_new_tokens\n",
        "\n",
        "gen = pipeline('text-generation', model=model, tokenizer=tokenizer, max_length=max_length)\n",
        "result = gen(prompt)\n",
        "print(result[0]['generated_text'].replace(prompt, ''))"
      ],
      "metadata": {
        "id": "7hxQ_Ero2IJe"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Merge the model and store in Google Drive"
      ],
      "metadata": {
        "id": "Ko6UkINu_qSx"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Merge and save the fine-tuned model\n",
        "from google.colab import drive\n",
        "drive.mount('/content/drive')\n",
        "\n",
        "model_path = \"/content/drive/MyDrive/llama-2-7b-custom\"  # change to your preferred path\n",
        "\n",
        "# Reload model in FP16 and merge it with LoRA weights\n",
        "base_model = AutoModelForCausalLM.from_pretrained(\n",
        "    model_name,\n",
        "    low_cpu_mem_usage=True,\n",
        "    return_dict=True,\n",
        "    torch_dtype=torch.float16,\n",
        "    device_map=device_map,\n",
        ")\n",
        "model = PeftModel.from_pretrained(base_model, new_model)\n",
        "model = model.merge_and_unload()\n",
        "\n",
        "# Reload tokenizer to save it\n",
        "tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)\n",
        "tokenizer.pad_token = tokenizer.eos_token\n",
        "tokenizer.padding_side = \"right\"\n",
        "\n",
        "# Save the merged model\n",
        "model.save_pretrained(model_path)\n",
        "tokenizer.save_pretrained(model_path)"
      ],
      "metadata": {
        "id": "AgKCL7fTyp9u"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Load a fine-tuned model from Drive and run inference"
      ],
      "metadata": {
        "id": "do-dFdE5zWGO"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "from transformers import AutoModelForCausalLM, AutoTokenizer\n",
        "\n",
        "drive.mount('/content/drive')\n",
        "\n",
        "model_path = \"/content/drive/MyDrive/llama-2-7b-custom\"  # change to the path where your model is saved\n",
        "\n",
        "model = AutoModelForCausalLM.from_pretrained(model_path)\n",
        "tokenizer = AutoTokenizer.from_pretrained(model_path)"
      ],
      "metadata": {
        "id": "xg6nHPsLzMw-"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import pipeline\n",
        "\n",
        "prompt = \"What is 2 + 2?\"  # change to your desired prompt\n",
        "gen = pipeline('text-generation', model=model, tokenizer=tokenizer)\n",
        "result = gen(prompt)\n",
        "print(result[0]['generated_text'])"
      ],
      "metadata": {
        "id": "fBK2aE2KzZ05"
      },
      "execution_count": null,
      "outputs": []
    }
  ],
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    "accelerator": "GPU",
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      "gpuType": "T4",
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