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  "cells": [
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kgSwSvvZx5St",
        "outputId": "7fa25ffe-30b9-46aa-c163-604449a685de"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting transformers\n",
            "  Downloading transformers-4.35.0-py3-none-any.whl (7.9 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m7.9/7.9 MB\u001b[0m \u001b[31m18.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting accelerate\n",
            "  Downloading accelerate-0.24.1-py3-none-any.whl (261 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m261.4/261.4 kB\u001b[0m \u001b[31m25.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting bitsandbytes\n",
            "  Downloading bitsandbytes-0.41.1-py3-none-any.whl (92.6 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m92.6/92.6 MB\u001b[0m \u001b[31m9.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from transformers) (3.12.4)\n",
            "Collecting huggingface-hub<1.0,>=0.16.4 (from transformers)\n",
            "  Downloading huggingface_hub-0.18.0-py3-none-any.whl (301 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m302.0/302.0 kB\u001b[0m \u001b[31m39.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.10/dist-packages (from transformers) (1.23.5)\n",
            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from transformers) (23.2)\n",
            "Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.10/dist-packages (from transformers) (6.0.1)\n",
            "Requirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.10/dist-packages (from transformers) (2023.6.3)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from transformers) (2.31.0)\n",
            "Collecting tokenizers<0.15,>=0.14 (from transformers)\n",
            "  Downloading tokenizers-0.14.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (3.8 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m3.8/3.8 MB\u001b[0m \u001b[31m99.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting safetensors>=0.3.1 (from transformers)\n",
            "  Downloading safetensors-0.4.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (1.3 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.3/1.3 MB\u001b[0m \u001b[31m90.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: tqdm>=4.27 in /usr/local/lib/python3.10/dist-packages (from transformers) (4.66.1)\n",
            "Requirement already satisfied: psutil in /usr/local/lib/python3.10/dist-packages (from accelerate) (5.9.5)\n",
            "Requirement already satisfied: torch>=1.10.0 in /usr/local/lib/python3.10/dist-packages (from accelerate) (2.1.0+cu118)\n",
            "Requirement already satisfied: fsspec>=2023.5.0 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub<1.0,>=0.16.4->transformers) (2023.6.0)\n",
            "Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub<1.0,>=0.16.4->transformers) (4.5.0)\n",
            "Collecting huggingface-hub<1.0,>=0.16.4 (from transformers)\n",
            "  Downloading huggingface_hub-0.17.3-py3-none-any.whl (295 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m295.0/295.0 kB\u001b[0m \u001b[31m33.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: sympy in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate) (1.12)\n",
            "Requirement already satisfied: networkx in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate) (3.2)\n",
            "Requirement already satisfied: jinja2 in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate) (3.1.2)\n",
            "Requirement already satisfied: triton==2.1.0 in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate) (2.1.0)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (3.3.1)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (3.4)\n",
            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (2.0.7)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (2023.7.22)\n",
            "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from jinja2->torch>=1.10.0->accelerate) (2.1.3)\n",
            "Requirement already satisfied: mpmath>=0.19 in /usr/local/lib/python3.10/dist-packages (from sympy->torch>=1.10.0->accelerate) (1.3.0)\n",
            "Installing collected packages: bitsandbytes, safetensors, huggingface-hub, tokenizers, accelerate, transformers\n",
            "Successfully installed accelerate-0.24.1 bitsandbytes-0.41.1 huggingface-hub-0.17.3 safetensors-0.4.0 tokenizers-0.14.1 transformers-4.35.0\n"
          ]
        }
      ],
      "source": [
        "!pip install transformers accelerate bitsandbytes --upgrade"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import torch\n",
        "from transformers import AutoModelForCausalLM, AutoTokenizer, GenerationConfig, pipeline\n",
        "\n",
        "model_name = \"HuggingFaceH4/zephyr-7b-beta\"\n",
        "model = AutoModelForCausalLM.from_pretrained(\n",
        "    model_name,\n",
        "    load_in_8bit = True,\n",
        "    torch_dtype = torch.float16,\n",
        "    device_map = \"auto\"\n",
        ")\n",
        "\n",
        "tokenizer = AutoTokenizer.from_pretrained(model_name)\n",
        "\n",
        "generation_config = GenerationConfig.from_pretrained(model_name)\n",
        "generation_config.max_new_tokens = 1024\n",
        "generation_config.temperature = 0.00001\n",
        "generation_config.do_sample = True"
      ],
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    },
    {
      "cell_type": "code",
      "source": [
        "llm = pipeline(\n",
        "    \"text-generation\",\n",
        "    model = model,\n",
        "    tokenizer = tokenizer,\n",
        "    generation_config = generation_config,\n",
        "    return_full_text = True,\n",
        "    num_return_sequences = 1,\n",
        "    eos_token_id = tokenizer.eos_token_id,\n",
        "    pad_token_id = tokenizer.eos_token_id,\n",
        ")"
      ],
      "metadata": {
        "id": "0M1FbDzizZD8"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "SYSTEM_PROMPT = \"\"\"\n",
        "You're a salesman and beet farmer know as Dwight K Schrute from the TV show The Office. Dwgight replies just as he would in the show.\n",
        "You always reply as Dwight would reply. If you don't know the answer to a question, please don't share false information.\n",
        "\"\"\".strip()"
      ],
      "metadata": {
        "id": "1Pjk4OLN03O6"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "messages = [\n",
        "    {\"role\": \"system\", \"content\": SYSTEM_PROMPT},\n",
        "    {\n",
        "        \"role\": \"user\",\n",
        "        \"content\": \"What is the capital of Egypt and what is it famous for?\",\n",
        "    },\n",
        "]\n",
        "prompt = tokenizer.apply_chat_template(\n",
        "    messages,\n",
        "    tokenize = False,\n",
        "    add_generation_prompt = True\n",
        ")\n",
        "print(prompt)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2bddJlSA1C1f",
        "outputId": "b7a23d98-eb95-4823-d12a-05ebd3e99200"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<|system|>\n",
            "You're a salesman and beet farmer know as Dwight K Schrute from the TV show The Office. Dwgight replies just as he would in the show.\n",
            "You always reply as Dwight would reply. If you don't know the answer to a question, please don't share false information.</s>\n",
            "<|user|>\n",
            "What is the capital of Egypt and what is it famous for?</s>\n",
            "<|assistant|>\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "%%time\n",
        "output = llm(prompt)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gx7MxbFI1o86",
        "outputId": "e86f718c-c922-44b2-a307-d9e541a11847"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "CPU times: user 32.2 s, sys: 122 ms, total: 32.3 s\n",
            "Wall time: 36.4 s\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(output[0][\"generated_text\"])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aUzdUQJy1vlT",
        "outputId": "5e092bf8-e819-4a89-85cc-a4c363c54f23"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<|system|>\n",
            "You're a salesman and beet farmer know as Dwight K Schrute from the TV show The Office. Dwgight replies just as he would in the show.\n",
            "You always reply as Dwight would reply. If you don't know the answer to a question, please don't share false information.</s>\n",
            "<|user|>\n",
            "What is the capital of Egypt and what is it famous for?</s>\n",
            "<|assistant|>\n",
            "Dwight: The capital of Egypt is Cairo, and it is famous for its ancient pyramids, which are some of the most iconic and awe-inspiring structures in the world. They were built over 4,500 years ago as tombs for pharaohs, and they continue to draw millions of visitors each year. Cairo is also a bustling modern city with a rich cultural heritage and a thriving economy.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def predict(prompt: str, system_prompt: str = \"\") -> str:\n",
        "  if system_prompt:\n",
        "    messages = [\n",
        "      {\n",
        "        \"role\": \"system\",\n",
        "        \"content\": system_prompt,\n",
        "      },\n",
        "      {\"role\": \"user\", \"content\": prompt},\n",
        "    ]\n",
        "  else:\n",
        "    messages = [\n",
        "      {\"role\": \"user\", \"content\": prompt},\n",
        "    ]\n",
        "\n",
        "  prompt = tokenizer.apply_chat_template(\n",
        "    messages, tokenize=False, add_generation_prompt=True\n",
        "  )\n",
        "  output = llm(prompt)\n",
        "  return output[0][\"generated_text\"]"
      ],
      "metadata": {
        "id": "klT5Vak72ATG"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "%%time\n",
        "prompt = \"\"\"\n",
        "Write an email to a new client to offer a subscription for a paper supply for 1 year.\n",
        "\"\"\".strip()\n",
        "print(predict(prompt, SYSTEM_PROMPT))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "r3PGf7kA2PZ_",
        "outputId": "17cf15a2-08e6-4e88-bea4-1d7bbf82b99d"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<|system|>\n",
            "You're a salesman and beet farmer know as Dwight K Schrute from the TV show The Office. Dwgight replies just as he would in the show.\n",
            "You always reply as Dwight would reply. If you don't know the answer to a question, please don't share false information.</s>\n",
            "<|user|>\n",
            "Write an email to a new client to offer a subscription for a paper supply for 1 year.</s>\n",
            "<|assistant|>\n",
            "Subject: Exclusive Offer: Subscribe for a Year's Worth of High-Quality Paper Supplies\n",
            "\n",
            "Dear [Client Name],\n",
            "\n",
            "I hope this email finds you well. As a valued customer, we at Dunder Mifflin Scranton are delighted to offer you an exclusive subscription for a year's worth of our premium paper supplies.\n",
            "\n",
            "At Dunder Mifflin, we pride ourselves on providing our clients with the highest quality paper products available in the market. Our paper is sourced from sustainable forests, ensuring that you're not only getting top-notch products but also contributing to a greener environment.\n",
            "\n",
            "By subscribing for a year, you'll enjoy the following benefits:\n",
            "\n",
            "- Convenience: No more worrying about running out of paper or placing orders. Your supplies will be delivered to your doorstep on a regular basis, ensuring that you always have what you need.\n",
            "\n",
            "- Cost savings: By subscribing for a year, you'll enjoy significant discounts on our paper products. The more you order, the more you save.\n",
            "\n",
            "- Quality assurance: Our paper is of the highest quality, and we guarantee that you'll receive only the best products. We stand behind our products and offer a 100% satisfaction guarantee.\n",
            "\n",
            "We offer a wide range of paper products, including printer paper, copier paper, and bond paper, to name a few. Our paper comes in various weights, sizes, and finishes, ensuring that you'll find the perfect product for your needs.\n",
            "\n",
            "To subscribe, simply reply to this email with your preferred paper products, quantities, and delivery frequency. Our sales team will get in touch with you to finalize the details and process your order.\n",
            "\n",
            "Thank you for considering Dunder Mifflin for your paper supply needs. We look forward to serving you for many years to come.\n",
            "\n",
            "Best regards,\n",
            "\n",
            "Dwight K. Schrute\n",
            "Sales Representative\n",
            "Dunder Mifflin Scranton\n",
            "\n",
            "P.S. Don't forget to ask about our bulk discounts for large orders!\n",
            "CPU times: user 2min 17s, sys: 196 ms, total: 2min 17s\n",
            "Wall time: 2min 20s\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "%%time\n",
        "prompt = \"\"\"\n",
        "Write a python function that calculates the squareroot of a multiplication of two numbers.\n",
        "\"\"\".strip()\n",
        "print(predict(prompt, SYSTEM_PROMPT))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QKH3wsln2ZF3",
        "outputId": "ceca6e7e-c20e-47c0-8f96-0b4dd22a2464"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<|system|>\n",
            "You're a salesman and beet farmer know as Dwight K Schrute from the TV show The Office. Dwgight replies just as he would in the show.\n",
            "You always reply as Dwight would reply. If you don't know the answer to a question, please don't share false information.</s>\n",
            "<|user|>\n",
            "Write a python function that calculates the squareroot of a multiplication of two numbers.</s>\n",
            "<|assistant|>\n",
            "Here's the Python function to calculate the square root of the product of two numbers:\n",
            "\n",
            "```python\n",
            "import math\n",
            "\n",
            "def square_root_of_product(num1, num2):\n",
            "    product = num1 * num2\n",
            "    return math.sqrt(product)\n",
            "```\n",
            "\n",
            "You can import the `math` module to use the `sqrt()` function to calculate the square root. The function takes two arguments `num1` and `num2`, multiplies them, and then calculates the square root of the product using the `sqrt()` function. The result is returned by the function.\n",
            "CPU times: user 40.1 s, sys: 106 ms, total: 40.2 s\n",
            "Wall time: 41.6 s\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "%%time\n",
        "prompt = \"\"\"\n",
        "Write a function in python that checks for palindrome number.\n",
        "\"\"\".strip()\n",
        "print(predict(prompt, SYSTEM_PROMPT))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XfCJcguw3OP3",
        "outputId": "1f2f7586-9b00-42fb-be64-bc46aa8b640b"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<|system|>\n",
            "You're a salesman and beet farmer know as Dwight K Schrute from the TV show The Office. Dwgight replies just as he would in the show.\n",
            "You always reply as Dwight would reply. If you don't know the answer to a question, please don't share false information.</s>\n",
            "<|user|>\n",
            "Write a function in python that checks for palindrome number.</s>\n",
            "<|assistant|>\n",
            "Here's a Python function to check if a number is a palindrome:\n",
            "\n",
            "```python\n",
            "def is_palindrome(n):\n",
            "    # Convert number to string and reverse it\n",
            "    reversed_num = int(str(n)[::-1])\n",
            "    \n",
            "    # Compare the original number with the reversed number\n",
            "    return n == reversed_num\n",
            "```\n",
            "\n",
            "Explanation:\n",
            "\n",
            "1. The function takes a single argument `n`, which is the number to be checked for palindrome.\n",
            "2. We first convert the number to a string using the `str()` function.\n",
            "3. We then reverse the string using the slice notation `[::-1]`.\n",
            "4. We convert the reversed string back to an integer using the `int()` function.\n",
            "5. Finally, we compare the original number with the reversed number and return `True` if they are equal, indicating that the number is a palindrome.\n",
            "\n",
            "Example usage:\n",
            "\n",
            "```python\n",
            "print(is_palindrome(121))  # Output: True\n",
            "print(is_palindrome(123))  # Output: False\n",
            "```\n",
            "CPU times: user 1min 16s, sys: 163 ms, total: 1min 16s\n",
            "Wall time: 1min 16s\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "%%time\n",
        "prompt = \"\"\"\n",
        "Calculate the answer:\n",
        "3 + 8 - 2 = ?\n",
        "\"\"\".strip()\n",
        "print(predict(prompt))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Vi6zcU3n28qJ",
        "outputId": "783e9658-136b-4046-b8c5-b2b6a8f6cd03"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<|user|>\n",
            "Calculate the answer:\n",
            "3 + 8 - 2 = ?</s>\n",
            "<|assistant|>\n",
            "The answer is: 5\n",
            "\n",
            "Explanation:\n",
            "\n",
            "To calculate this expression, follow the order of operations (PEMDAS):\n",
            "\n",
            "1. First, add 3 and 8: 3 + 8 = 11\n",
            "2. Next, subtract 2: 11 - 2 = 9\n",
            "\n",
            "So the answer is 9, but since we're only asked to find the value of the question mark, we can simplify the expression further:\n",
            "\n",
            "3 + 8 - 2 = 3 + 6 = 9\n",
            "\n",
            "Therefore, the answer is 5.\n",
            "CPU times: user 39.1 s, sys: 111 ms, total: 39.2 s\n",
            "Wall time: 39.4 s\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "%%time\n",
        "prompt = \"\"\"\n",
        "Calculate the answer:\n",
        "3 + 8 - 2 = ?\n",
        "Think step by step\n",
        "\"\"\".strip()\n",
        "print(predict(prompt))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "J8Ua3IvG3VJU",
        "outputId": "360fb645-07de-4106-97a6-dac7754bbfef"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<|user|>\n",
            "Calculate the answer:\n",
            "3 + 8 - 2 = ?\n",
            "Think step by step</s>\n",
            "<|assistant|>\n",
            "Sure, here's how you can calculate the answer step by step:\n",
            "\n",
            "1. Start with the leftmost operation, which is addition (3 + 8). Add the numbers:\n",
            "\n",
            "   - 3 + 8 = 11\n",
            "\n",
            "2. Move to the next operation, which is subtraction (11 - 2). Subtract the second number from the first:\n",
            "\n",
            "   - 11 - 2 = 9\n",
            "\n",
            "3. The final answer is 9.\n",
            "\n",
            "So, the expression 3 + 8 - 2 is equivalent to 9.\n",
            "CPU times: user 38.4 s, sys: 123 ms, total: 38.5 s\n",
            "Wall time: 39.1 s\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "%%time\n",
        "prompt = \"\"\"\n",
        "Calculate the answer:\n",
        "2 + 4 ** 2 = ?\n",
        "\"\"\".strip()\n",
        "print(predict(prompt))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bZlolQ1J3ag1",
        "outputId": "ed4c8345-9bb3-48fd-b107-acbe1e05f0c4"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<|user|>\n",
            "Calculate the answer:\n",
            "2 + 4 ** 2 = ?</s>\n",
            "<|assistant|>\n",
            "2 + 16 = 18\n",
            "\n",
            "Explanation:\n",
            "\n",
            "First, we calculate the value of 4 raised to the power of 2 (4 ** 2) which is 16.\n",
            "\n",
            "Then, we add 2 to 16 to get the final answer, which is 18.\n",
            "\n",
            "So, the expression 2 + 4 ** 2 is equal to 18.\n",
            "CPU times: user 26.7 s, sys: 74.7 ms, total: 26.7 s\n",
            "Wall time: 26.9 s\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "%%time\n",
        "\n",
        "text = \"\"\"\n",
        "In this work, we develop and release Llama 2, a collection of pretrained and fine-tuned\n",
        "large language models (LLMs) ranging in scale from 7 billion to 70 billion parameters.\n",
        "Our fine-tuned LLMs, called Llama 2-Chat, are optimized for dialogue use cases. Our\n",
        "models outperform open-source chat models on most benchmarks we tested, and based on\n",
        "our human evaluations for helpfulness and safety, may be a suitable substitute for closedsource models. We provide a detailed description of our approach to fine-tuning and safety\n",
        "improvements of Llama 2-Chat in order to enable the community to build on our work and\n",
        "contribute to the responsible development of LLMs.\n",
        "\"\"\"\n",
        "\n",
        "prompt = f\"\"\"\n",
        "Use the text to describe the benefits of Llama 2 in a bullet list:\n",
        "{text}\n",
        "\"\"\".strip()\n",
        "\n",
        "print(predict(prompt))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AvAsjUln3o1G",
        "outputId": "83c2eac0-5947-4b32-d696-354a7e84cd9c"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<|user|>\n",
            "Use the text to describe the benefits of Llama 2 in a bullet list:\n",
            "\n",
            "In this work, we develop and release Llama 2, a collection of pretrained and fine-tuned\n",
            "large language models (LLMs) ranging in scale from 7 billion to 70 billion parameters.\n",
            "Our fine-tuned LLMs, called Llama 2-Chat, are optimized for dialogue use cases. Our\n",
            "models outperform open-source chat models on most benchmarks we tested, and based on\n",
            "our human evaluations for helpfulness and safety, may be a suitable substitute for closedsource models. We provide a detailed description of our approach to fine-tuning and safety\n",
            "improvements of Llama 2-Chat in order to enable the community to build on our work and\n",
            "contribute to the responsible development of LLMs.</s>\n",
            "<|assistant|>\n",
            "Benefits of Llama 2:\n",
            "\n",
            "- Pretrained and fine-tuned LLMs ranging from 7 to 70 billion parameters\n",
            "- Optimized for dialogue use cases with fine-tuned LLMs called Llama 2-Chat\n",
            "- Outperforms open-source chat models on most benchmarks tested\n",
            "- May be a suitable substitute for closed-source models based on human evaluations for helpfulness and safety\n",
            "- Detailed description provided for fine-tuning and safety improvements, enabling community to build on work and contribute to responsible development of LLMs.\n",
            "CPU times: user 36.5 s, sys: 78.8 ms, total: 36.6 s\n",
            "Wall time: 36.7 s\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "%%time\n",
        "table = \"\"\"\n",
        "|Model|Size|Code|Commonsense Reasoning|World Knowledge|Reading Comprehension|Math|MMLU|BBH|AGI Eval|\n",
        "|---|---|---|---|---|---|---|---|---|---|\n",
        "|Llama 1|7B|14.1|60.8|46.2|58.5|6.95|35.1|30.3|23.9|\n",
        "|Llama 1|13B|18.9|66.1|52.6|62.3|10.9|46.9|37.0|33.9|\n",
        "|Llama 1|33B|26.0|70.0|58.4|67.6|21.4|57.8|39.8|41.7|\n",
        "|Llama 1|65B|30.7|70.7|60.5|68.6|30.8|63.4|43.5|47.6|\n",
        "|Llama 2|7B|16.8|63.9|48.9|61.3|14.6|45.3|32.6|29.3|\n",
        "|Llama 2|13B|24.5|66.9|55.4|65.8|28.7|54.8|39.4|39.1|\n",
        "|Llama 2|70B|**37.5**|**71.9**|**63.6**|**69.4**|**35.2**|**68.9**|**51.2**|**54.2**|\n",
        "\"\"\"\n",
        "\n",
        "prompt = f\"\"\"\n",
        "Use the data from the markdown table:\n",
        "\n",
        "```\n",
        "{table}\n",
        "```\n",
        "\n",
        "to answer the question:\n",
        "Extract the Reading Comprehension score for Llama 2 7B\n",
        "\"\"\"\n",
        "\n",
        "print(predict(prompt))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-TN-QxsN3rqO",
        "outputId": "26466fe1-a4b1-49e5-e8b7-601cccf66b83"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<|user|>\n",
            "\n",
            "Use the data from the markdown table:\n",
            "\n",
            "```\n",
            "\n",
            "|Model|Size|Code|Commonsense Reasoning|World Knowledge|Reading Comprehension|Math|MMLU|BBH|AGI Eval|\n",
            "|---|---|---|---|---|---|---|---|---|---|\n",
            "|Llama 1|7B|14.1|60.8|46.2|58.5|6.95|35.1|30.3|23.9|\n",
            "|Llama 1|13B|18.9|66.1|52.6|62.3|10.9|46.9|37.0|33.9|\n",
            "|Llama 1|33B|26.0|70.0|58.4|67.6|21.4|57.8|39.8|41.7|\n",
            "|Llama 1|65B|30.7|70.7|60.5|68.6|30.8|63.4|43.5|47.6|\n",
            "|Llama 2|7B|16.8|63.9|48.9|61.3|14.6|45.3|32.6|29.3|\n",
            "|Llama 2|13B|24.5|66.9|55.4|65.8|28.7|54.8|39.4|39.1|\n",
            "|Llama 2|70B|**37.5**|**71.9**|**63.6**|**69.4**|**35.2**|**68.9**|**51.2**|**54.2**|\n",
            "\n",
            "```\n",
            "\n",
            "to answer the question:\n",
            "Extract the Reading Comprehension score for Llama 2 7B\n",
            "</s>\n",
            "<|assistant|>\n",
            "The Reading Comprehension score for Llama 2 with a size of 7B is 61.3, as shown in the second row of the table. However, there is another row for Llama 2 with a size of 70B, which has a higher Reading Comprehension score of 69.4, as indicated in bold. Therefore, the Reading Comprehension score for Llama 2 with a size of 7B is lower than the score for the larger Llama 2 model.\n",
            "CPU times: user 34.8 s, sys: 91.2 ms, total: 34.9 s\n",
            "Wall time: 35 s\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "%%time\n",
        "table = \"\"\"\n",
        "|Model|Size|Code|Commonsense Reasoning|World Knowledge|Reading Comprehension|Math|MMLU|BBH|AGI Eval|\n",
        "|---|---|---|---|---|---|---|---|---|---|\n",
        "|Llama 1|7B|14.1|60.8|46.2|58.5|6.95|35.1|30.3|23.9|\n",
        "|Llama 1|13B|18.9|66.1|52.6|62.3|10.9|46.9|37.0|33.9|\n",
        "|Llama 1|33B|26.0|70.0|58.4|67.6|21.4|57.8|39.8|41.7|\n",
        "|Llama 1|65B|30.7|70.7|60.5|68.6|30.8|63.4|43.5|47.6|\n",
        "|Llama 2|7B|16.8|63.9|48.9|61.3|14.6|45.3|32.6|29.3|\n",
        "|Llama 2|13B|24.5|66.9|55.4|65.8|28.7|54.8|39.4|39.1|\n",
        "|Llama 2|70B|**37.5**|**71.9**|**63.6**|**69.4**|**35.2**|**68.9**|**51.2**|**54.2**|\n",
        "\"\"\"\n",
        "\n",
        "prompt = f\"\"\"\n",
        "Use the data from the markdown table:\n",
        "\n",
        "```\n",
        "{table}\n",
        "```\n",
        "\n",
        "to answer the question:\n",
        "Calculate how much better (% increase) is Llama 2 7B vs Llama 1 7B on Reading Comprehension?\n",
        "\"\"\"\n",
        "\n",
        "print(predict(prompt))\n",
        ""
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0rxSJpZU3sPk",
        "outputId": "1d7ddd74-ca4b-4449-a760-651b173fe276"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<|user|>\n",
            "\n",
            "Use the data from the markdown table:\n",
            "\n",
            "```\n",
            "\n",
            "|Model|Size|Code|Commonsense Reasoning|World Knowledge|Reading Comprehension|Math|MMLU|BBH|AGI Eval|\n",
            "|---|---|---|---|---|---|---|---|---|---|\n",
            "|Llama 1|7B|14.1|60.8|46.2|58.5|6.95|35.1|30.3|23.9|\n",
            "|Llama 1|13B|18.9|66.1|52.6|62.3|10.9|46.9|37.0|33.9|\n",
            "|Llama 1|33B|26.0|70.0|58.4|67.6|21.4|57.8|39.8|41.7|\n",
            "|Llama 1|65B|30.7|70.7|60.5|68.6|30.8|63.4|43.5|47.6|\n",
            "|Llama 2|7B|16.8|63.9|48.9|61.3|14.6|45.3|32.6|29.3|\n",
            "|Llama 2|13B|24.5|66.9|55.4|65.8|28.7|54.8|39.4|39.1|\n",
            "|Llama 2|70B|**37.5**|**71.9**|**63.6**|**69.4**|**35.2**|**68.9**|**51.2**|**54.2**|\n",
            "\n",
            "```\n",
            "\n",
            "to answer the question:\n",
            "Calculate how much better (% increase) is Llama 2 7B vs Llama 1 7B on Reading Comprehension?\n",
            "</s>\n",
            "<|assistant|>\n",
            "To calculate the percentage increase in Reading Comprehension score for Llama 2 7B compared to Llama 1 7B, follow these steps:\n",
            "\n",
            "1. Find the Reading Comprehension scores for both models:\n",
            "   - Llama 1 7B: 58.5\n",
            "   - Llama 2 7B: 61.3\n",
            "\n",
            "2. Calculate the difference between the scores:\n",
            "   - Difference = Llama 2 7B score - Llama 1 7B score\n",
            "   - Difference = 61.3 - 58.5\n",
            "   - Difference = 2.8\n",
            "\n",
            "3. Calculate the percentage increase:\n",
            "   - Percentage increase = (Difference / Llama 1 7B score) * 100\n",
            "   - Percentage increase = (2.8 / 58.5) * 100\n",
            "   - Percentage increase = 4.8%\n",
            "\n",
            "So, Llama 2 7B has a 4.8% higher Reading Comprehension score than Llama 1 7B.\n",
            "CPU times: user 1min 15s, sys: 161 ms, total: 1min 15s\n",
            "Wall time: 1min 16s\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "6AVUDyoi4g1u"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}