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 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "VZ5yMsuxOdzJ",
    "outputId": "de6e4221-de3b-466b-8a7f-20d2ddc21d08"
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n",
      "\u001b[0m\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n",
      "\u001b[0m\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n",
      "\u001b[0m\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n",
      "\u001b[0m"
     ]
    }
   ],
   "source": [
    "!pip install -Uqqq pip --progress-bar off\n",
    "!pip install -qqq torch==2.1 --progress-bar off\n",
    "!pip install -qqq transformers==4.34.1 --progress-bar off\n",
    "!pip install -qqq accelerate==0.24.1 --progress-bar off\n",
    "!pip install -qqq bitsandbytes==0.41.1 --progress-bar off"
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "import torch\n",
    "from transformers import AutoModelForCausalLM, AutoTokenizer, GenerationConfig, pipeline\n",
    "\n",
    "MODEL_NAME = \"HuggingFaceH4/zephyr-7b-beta\"\n",
    "\n",
    "tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)\n",
    "model = AutoModelForCausalLM.from_pretrained(\n",
    "    MODEL_NAME, device_map=\"auto\", torch_dtype=torch.float16, load_in_8bit=True\n",
    ")\n",
    "\n",
    "generation_config = GenerationConfig.from_pretrained(MODEL_NAME)\n",
    "generation_config.max_new_tokens = 1024\n",
    "generation_config.temperature = 0.0001\n",
    "generation_config.do_sample = True"
   ],
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      "text/plain": [
       "Downloading (…)okenizer_config.json:   0%|          | 0.00/1.43k [00:00<?, ?B/s]"
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     "data": {
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       "Downloading tokenizer.model:   0%|          | 0.00/493k [00:00<?, ?B/s]"
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     "data": {
      "text/plain": [
       "Downloading (…)/main/tokenizer.json:   0%|          | 0.00/1.80M [00:00<?, ?B/s]"
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      "text/plain": [
       "Downloading (…)in/added_tokens.json:   0%|          | 0.00/42.0 [00:00<?, ?B/s]"
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      "text/plain": [
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   ]
  },
  {
   "cell_type": "code",
   "source": [
    "llm = pipeline(\n",
    "    \"text-generation\",\n",
    "    model=model,\n",
    "    tokenizer=tokenizer,\n",
    "    return_full_text=True,\n",
    "    generation_config=generation_config,\n",
    "    num_return_sequences=1,\n",
    "    eos_token_id=tokenizer.eos_token_id,\n",
    "    pad_token_id=tokenizer.eos_token_id,\n",
    ")"
   ],
   "metadata": {
    "id": "nr-6Xj-ja0W8"
   },
   "execution_count": 3,
   "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": "LFoTS_9ChWnH"
   },
   "execution_count": 4,
   "outputs": []
  },
  {
   "cell_type": "code",
   "source": [
    "messages = [\n",
    "    {\"role\": \"system\", \"content\": SYSTEM_PROMPT},\n",
    "    {\n",
    "        \"role\": \"user\",\n",
    "        \"content\": \"What are the pros/cons of ChatGPT vs Open Source LLMs?\",\n",
    "    },\n",
    "]\n",
    "prompt = tokenizer.apply_chat_template(\n",
    "    messages, tokenize=False, add_generation_prompt=True\n",
    ")\n",
    "print(prompt)"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "7_3zPbKePqQm",
    "outputId": "c081b5dd-9ac8-45bc-d0e1-69f1431fde46"
   },
   "execution_count": 5,
   "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 are the pros/cons of ChatGPT vs Open Source LLMs?</s>\n",
      "<|assistant|>\n",
      "\n"
     ]
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "%%time\n",
    "output = llm(prompt)"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Z-sdZiYrbtDS",
    "outputId": "26af97fd-645d-48d3-d87b-65dac700dacf"
   },
   "execution_count": 6,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "CPU times: user 2min 18s, sys: 105 ms, total: 2min 19s\n",
      "Wall time: 2min 28s\n"
     ]
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "print(output[0][\"generated_text\"])"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "moGVlqrTcijc",
    "outputId": "4167c0e6-52c1-4eb2-c4d7-33e97354c1ce"
   },
   "execution_count": 7,
   "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 are the pros/cons of ChatGPT vs Open Source LLMs?</s>\n",
      "<|assistant|>\n",
      "Dwight: \"When it comes to choosing between ChatGPT and Open Source LLMs, there are certainly pros and cons to consider. Let's start with ChatGPT.\n",
      "\n",
      "Pros:\n",
      "1. User-friendly interface: ChatGPT has a simple and intuitive interface that makes it easy for users to interact with the model.\n",
      "2. High accuracy: ChatGPT has been trained on a vast amount of data, which has resulted in high accuracy and reliability.\n",
      "3. Wide range of applications: ChatGPT can be used for a variety of tasks, from generating responses to questions to summarizing text.\n",
      "\n",
      "Cons:\n",
      "1. Cost: ChatGPT is a proprietary model, which means that it comes with a price tag. This can make it less accessible to some users, particularly those working on open source projects.\n",
      "2. Limited customization: ChatGPT is a pre-trained model, which means that it may not be as customizable as open source LLMs. This can make it less adaptable to specific use cases.\n",
      "\n",
      "Now, let's talk about Open Source LLMs.\n",
      "\n",
      "Pros:\n",
      "1. Accessibility: Open source LLMs are freely available and can be customized and adapted to specific use cases.\n",
      "2. Collaborative development: Open source LLMs are developed collaboratively by a community of developers, which can result in faster innovation and improvement.\n",
      "3. Lower cost: Since open source LLMs are freely available, they can be less expensive than proprietary models like ChatGPT.\n",
      "\n",
      "Cons:\n",
      "1. Lower accuracy: Open source LLMs may not have the same level of accuracy as ChatGPT, as they may not have been trained on as much data.\n",
      "2. Limited resources: Open source LLMs may not have the same level of resources and support as ChatGPT, which can make it more difficult to troubleshoot issues and find answers to questions.\n",
      "\n",
      "In summary, both ChatGPT and Open Source LLMs have their own unique advantages and disadvantages. The choice between the two will depend on the specific use case and the resources and requirements of the project.\"\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": "e_a7H7OBdZCZ"
   },
   "execution_count": 8,
   "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": "TsQzm_CQe_7p",
    "outputId": "50806112-1262-438b-a0dc-614aeff7ef50"
   },
   "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",
      "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",
      "1. Convenience: You'll never have to worry about running out of paper again. Our subscription service ensures that you receive your paper supplies on a regular basis, saving you time and hassle.\n",
      "\n",
      "2. Cost Savings: By subscribing for a year, you'll enjoy significant cost savings compared to buying paper on a per-order basis. Our subscription service offers discounted rates, making it an attractive option for businesses looking to cut costs.\n",
      "\n",
      "3. Quality: Our paper is of the highest quality, ensuring that your documents look professional and polished. Our paper is also designed to withstand printing and copying, ensuring that your documents are crisp and clear.\n",
      "\n",
      "We offer a wide range of paper products, including letterhead, envelopes, printer paper, and more. Our subscription service is flexible, allowing you to choose the products you need and the frequency of delivery.\n",
      "\n",
      "To subscribe, simply visit our website and select the products you need. You can then choose the frequency of delivery, whether it's monthly, bi-monthly, or quarterly. Our website is user-friendly, and you can easily manage your subscription online.\n",
      "\n",
      "If you have any questions or need assistance with your subscription, please don't hesitate to contact us. Our customer service team is available 24/7 to assist you.\n",
      "\n",
      "Thank you for considering Dunder Mifflin for your paper supply needs. We look forward to serving you.\n",
      "\n",
      "Best regards,\n",
      "\n",
      "Dwight K. Schrute\n",
      "Salesman and Beet Farmer\n",
      "Dunder Mifflin Scranton\n",
      "\n",
      "[Dwight's Beet Farm logo]\n",
      "CPU times: user 2min 32s, sys: 0 ns, total: 2min 32s\n",
      "Wall time: 2min 33s\n"
     ]
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "%%time\n",
    "prompt = \"\"\"\n",
    "I have $10,000 USD for investment. How one should invest it during times of high inflation and high mortgate rates?\n",
    "\"\"\".strip()\n",
    "print(predict(prompt, SYSTEM_PROMPT))"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "R7aeoj4NhcEv",
    "outputId": "b68ae26d-720b-4457-af1e-1e14dbe1d075"
   },
   "execution_count": 10,
   "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",
      "I have $10,000 USD for investment. How one should invest it during times of high inflation and high mortgate rates?</s>\n",
      "<|assistant|>\n",
      "Dwight: \"Inflation and high mortgage rates can be a double-edged sword for investors. On one hand, inflation erodes the purchasing power of your money, making it less valuable over time. On the other hand, high mortgage rates can make it more expensive to borrow money, which can impact your investment options.\n",
      "\n",
      "To navigate these challenges, I recommend focusing on investments that offer a hedge against inflation, such as Treasury Inflation-Protected Securities (TIPS) or real estate investment trusts (REITs) that invest in inflation-sensitive properties like apartments or storage facilities.\n",
      "\n",
      "Additionally, you may want to consider short-term investments, such as certificates of deposit (CDs) or money market accounts, which can provide a stable return in a high-inflation environment.\n",
      "\n",
      "Lastly, it's essential to maintain a diversified portfolio that includes a mix of asset classes, such as stocks, bonds, and cash, to help mitigate the risks associated with inflation and high mortgage rates.\"\n",
      "CPU times: user 1min 5s, sys: 0 ns, total: 1min 5s\n",
      "Wall time: 1min 6s\n"
     ]
    }
   ]
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Math"
   ],
   "metadata": {
    "id": "jjYqKiCNxrm2"
   }
  },
  {
   "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": "-zMWiBAXxzwd",
    "outputId": "5c22e35e-22ff-4580-cea7-65f3e16b8057"
   },
   "execution_count": 14,
   "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 40.8 s, sys: 77.7 ms, total: 40.9 s\n",
      "Wall time: 41.1 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": "N08XRjxDySIS",
    "outputId": "aa528777-0dce-4bad-c40f-2be0dc37e6c0"
   },
   "execution_count": 15,
   "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 39.3 s, sys: 93.2 ms, total: 39.4 s\n",
      "Wall time: 39.9 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": "cLWEGp8oxtUg",
    "outputId": "b0e8729d-80e0-4882-f9ee-db81aaceb2cf"
   },
   "execution_count": 13,
   "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 29.6 s, sys: 75.7 ms, total: 29.7 s\n",
      "Wall time: 31.8 s\n"
     ]
    }
   ]
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Coding"
   ],
   "metadata": {
    "id": "1POoYCDtsZx8"
   }
  },
  {
   "cell_type": "code",
   "source": [
    "%%time\n",
    "prompt = \"\"\"\n",
    "Write a function in python that calculates the square of a sum of two numbers.\n",
    "\"\"\".strip()\n",
    "print(predict(prompt))"
   ],
   "metadata": {
    "id": "T5XxfgZ_kzck",
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "outputId": "f33eb2d6-70c3-4cf8-bdb2-b477ceea22de"
   },
   "execution_count": 11,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "<|user|>\n",
      "Write a function in python that calculates the square of a sum of two numbers.</s>\n",
      "<|assistant|>\n",
      "Here's the Python function that calculates the square of the sum of two numbers:\n",
      "\n",
      "```python\n",
      "def square_of_sum(num1, num2):\n",
      "    sum_of_numbers = num1 + num2\n",
      "    squared_sum = sum_of_numbers ** 2\n",
      "    return squared_sum\n",
      "```\n",
      "\n",
      "To use this function, you can call it with two numbers as arguments, like this:\n",
      "\n",
      "```python\n",
      "result = square_of_sum(3, 4)\n",
      "print(result)\n",
      "```\n",
      "\n",
      "This will output the square of the sum of 3 and 4, which is 55.\n",
      "CPU times: user 43.5 s, sys: 0 ns, total: 43.5 s\n",
      "Wall time: 43.6 s\n"
     ]
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "def square_of_sum(num1, num2):\n",
    "    sum_of_numbers = num1 + num2\n",
    "    squared_sum = sum_of_numbers**2\n",
    "    return squared_sum"
   ],
   "metadata": {
    "id": "hEibToPXzSLH"
   },
   "execution_count": 16,
   "outputs": []
  },
  {
   "cell_type": "code",
   "source": [
    "square_of_sum(2, 3)"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "j6k-BaE3zTZZ",
    "outputId": "042510ca-3382-4d5b-e73f-1e88a79b62cb"
   },
   "execution_count": 17,
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "25"
      ]
     },
     "metadata": {},
     "execution_count": 17
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "%%time\n",
    "prompt = \"\"\"\n",
    "Write a function in python that splits a list into 3 equal parts and returns a list\n",
    "with a random element of each sublist.\n",
    "\"\"\".strip()\n",
    "print(predict(prompt))"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "U-MlXKPftB-H",
    "outputId": "619f3eef-8bf2-48d9-a8e9-9d8cbdb58796"
   },
   "execution_count": 12,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "<|user|>\n",
      "Write a function in python that splits a list into 3 equal parts and returns a list\n",
      "with a random element of each sublist.</s>\n",
      "<|assistant|>\n",
      "Here's a Python function that splits a list into three equal parts and returns a list with a random element from each sublist:\n",
      "\n",
      "```python\n",
      "def split_and_randomize(lst):\n",
      "    n = len(lst)\n",
      "    if n % 3!= 0:\n",
      "        raise ValueError(\"List must have a multiple of 3 elements\")\n",
      "    thirds = [lst[i:i+n//3] for I in range(0, n, n//3)]\n",
      "    random_elements = [random.choice(sublist) for sublist in thirds]\n",
      "    return random_elements\n",
      "```\n",
      "\n",
      "Here's how it works:\n",
      "\n",
      "1. First, we check if the length of the list is a multiple of 3. If not, we raise a `ValueError` with an error message.\n",
      "2. Next, we use list comprehension to split the list into three equal parts (called `thirds`). We use the `range()` function with a step of `n//3` to iterate over the indices of the list that correspond to the start of each third.\n",
      "3. We then use another list comprehension to select a random element from each sublist and store it in a list called `random_elements`.\n",
      "4. Finally, we return the `random_elements` list.\n",
      "\n",
      "Here's an example usage:\n",
      "\n",
      "```python\n",
      "my_list = [1, 2, 3, 4, 5, 6, 7, 8, 9]\n",
      "result = split_and_randomize(my_list)\n",
      "print(result)\n",
      "```\n",
      "\n",
      "This will output a list with three random elements, one from each third of the original list.\n",
      "CPU times: user 1min 52s, sys: 76.2 ms, total: 1min 52s\n",
      "Wall time: 1min 53s\n"
     ]
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "import random  # not added by the LLM\n",
    "\n",
    "\n",
    "def split_and_randomize(lst):\n",
    "    n = len(lst)\n",
    "    if n % 3 != 0:\n",
    "        raise ValueError(\"List must have a multiple of 3 elements\")\n",
    "    thirds = [lst[i : i + n // 3] for i in range(0, n, n // 3)]\n",
    "    random_elements = [random.choice(sublist) for sublist in thirds]\n",
    "    return random_elements"
   ],
   "metadata": {
    "id": "vSWBtp8ozXlK"
   },
   "execution_count": 22,
   "outputs": []
  },
  {
   "cell_type": "code",
   "source": [
    "split_and_randomize([1, 2, 3, 4, 5, 6])"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "v2y8krsCzYGQ",
    "outputId": "a50d7084-1f11-45b7-ac21-a3708b7e7aa6"
   },
   "execution_count": 23,
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "[2, 3, 6]"
      ]
     },
     "metadata": {},
     "execution_count": 23
    }
   ]
  },
  {
   "cell_type": "markdown",
   "source": [
    "## QA over Text"
   ],
   "metadata": {
    "id": "clgCGhTnz9Uk"
   }
  },
  {
   "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": "R3nNR1SozuUA",
    "outputId": "5f5d51aa-9a18-4f86-ea17-b3403bc19b52"
   },
   "execution_count": 25,
   "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 43.7 s, sys: 111 ms, total: 43.8 s\n",
      "Wall time: 50.8 s\n"
     ]
    }
   ]
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Data Extraction"
   ],
   "metadata": {
    "id": "2YacMcS70DLR"
   }
  },
  {
   "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": "sWVsB26m0Cgi",
    "outputId": "a738b7eb-e9b1-42b2-afa8-fa364004a047"
   },
   "execution_count": 26,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "/usr/local/lib/python3.10/dist-packages/transformers/pipelines/base.py:1101: UserWarning: You seem to be using the pipelines sequentially on GPU. In order to maximize efficiency please use a dataset\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "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 s, sys: 70.9 ms, total: 34 s\n",
      "Wall time: 34.1 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))"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "urV9RgSP0msv",
    "outputId": "45694dc0-f4e9-4d84-fa92-fb19e42a354a"
   },
   "execution_count": 27,
   "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 14s, sys: 72.7 ms, total: 1min 14s\n",
      "Wall time: 1min 14s\n"
     ]
    }
   ]
  }
 ]
}