Llama_3_Prompt_Engineering.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/amitsangani/Llama/blob/main/Llama_3_Prompt_Engineering.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RJSnI0Xy-kCm"
      },
      "source": [
        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)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LERqQn5v8-ak"
      },
      "source": [
        "# **Prompt Engineering with Llama 3**\n",
        "Our goal in this session is to provide a guided tour of Llama 3, including understanding different Llama 3 models, how and where to access them, Generative AI and Chatbot architectures, and Prompt Engineering. We'll present comparison examples of Llama 2 and Llama 3, and also cover resources for building more advanced Llama apps using RAG (Retrieval Augmented Generation), Fine-tuning, and Agents."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ioVMNcTesSEk"
      },
      "source": [
        "##**Prerequisites**\n",
        "* Basic understanding of Large Language Models\n",
        "\n",
        "* Basic understanding of Python\n",
        "\n",
        "* Access to Github, GColab and Replicate"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 61,
      "metadata": {
        "id": "ktEA7qXmwdUM"
      },
      "outputs": [],
      "source": [
        "# presentation layer code\n",
        "\n",
        "import base64\n",
        "from IPython.display import Image, display\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "def mm(graph):\n",
        "  graphbytes = graph.encode(\"ascii\")\n",
        "  base64_bytes = base64.b64encode(graphbytes)\n",
        "  base64_string = base64_bytes.decode(\"ascii\")\n",
        "  display(Image(url=\"https://mermaid.ink/img/\" + base64_string))\n",
        "\n",
        "def genai_app_arch():\n",
        "  mm(\"\"\"\n",
        "  flowchart TD\n",
        "    A[Users] --> B(Applications e.g. mobile, web)\n",
        "    B <--> |Hosted API|C(Platforms e.g. Custom, HuggingFace, Replicate, Together, Anyscale)\n",
        "    B -- optional --> E(Frameworks e.g. LangChain or LlamaIndex)\n",
        "    C-->|User Input|D[Llama 3]\n",
        "    D-->|Model Output|C\n",
        "    E --> C\n",
        "    classDef default fill:#CCE6FF,stroke:#84BCF5,textColor:#1C2B33,fontFamily:trebuchet ms;\n",
        "  \"\"\")\n",
        "\n",
        "def rag_arch():\n",
        "  mm(\"\"\"\n",
        "  flowchart TD\n",
        "    A[User Prompts] --> B(Frameworks e.g. LangChain)\n",
        "    B <--> |Database, Docs, XLS|C[fa:fa-database External Data]\n",
        "    B -->|API|D[Llama 3]\n",
        "    classDef default fill:#CCE6FF,stroke:#84BCF5,textColor:#1C2B33,fontFamily:trebuchet ms;\n",
        "  \"\"\")\n",
        "\n",
        "def llama3_family():\n",
        "  mm(\"\"\"\n",
        "  graph LR;\n",
        "      llama-3 --> llama-3-8b\n",
        "      llama-3 --> llama-3-70b\n",
        "      llama-3-8b --> llama-3-8b-base\n",
        "      llama-3-8b --> llama-3-8b-instruct\n",
        "      llama-3-70b --> llama-3-70b-base\n",
        "      llama-3-70b --> llama-3-70b-instruct\n",
        "      classDef default fill:#CCE6FF,stroke:#84BCF5,textColor:#1C2B33,fontFamily:trebuchet ms;\n",
        "  \"\"\")\n",
        "\n",
        "def apps_and_llms():\n",
        "  mm(\"\"\"\n",
        "  graph LR;\n",
        "    users --> apps\n",
        "    apps --> frameworks\n",
        "    frameworks --> platforms\n",
        "    platforms --> Llama 2\n",
        "    classDef default fill:#CCE6FF,stroke:#84BCF5,textColor:#1C2B33,fontFamily:trebuchet ms;\n",
        "  \"\"\")\n",
        "\n",
        "import ipywidgets as widgets\n",
        "from IPython.display import display, Markdown\n",
        "\n",
        "# Create a text widget\n",
        "API_KEY = widgets.Password(\n",
        "    value='',\n",
        "    placeholder='',\n",
        "    description='API_KEY:',\n",
        "    disabled=False\n",
        ")\n",
        "\n",
        "def md(t):\n",
        "  display(Markdown(t))\n",
        "\n",
        "def bot_arch():\n",
        "  mm(\"\"\"\n",
        "  graph LR;\n",
        "  user --> prompt\n",
        "  prompt --> i_safety\n",
        "  i_safety --> context\n",
        "  context --> Llama_3\n",
        "  Llama_3 --> output\n",
        "  output --> o_safety\n",
        "  i_safety --> memory\n",
        "  o_safety --> memory\n",
        "  memory --> context\n",
        "  o_safety --> user\n",
        "  classDef default fill:#CCE6FF,stroke:#84BCF5,textColor:#1C2B33,fontFamily:trebuchet ms;\n",
        "  \"\"\")\n",
        "\n",
        "def fine_tuned_arch():\n",
        "  mm(\"\"\"\n",
        "  graph LR;\n",
        "      Custom_Dataset --> Pre-trained_Llama\n",
        "      Pre-trained_Llama --> Fine-tuned_Llama\n",
        "      Fine-tuned_Llama --> RLHF\n",
        "      RLHF --> |Loss:Cross-Entropy|Fine-tuned_Llama\n",
        "      classDef default fill:#CCE6FF,stroke:#84BCF5,textColor:#1C2B33,fontFamily:trebuchet ms;\n",
        "  \"\"\")\n",
        "\n",
        "def load_data_faiss_arch():\n",
        "  mm(\"\"\"\n",
        "  graph LR;\n",
        "      documents --> textsplitter\n",
        "      textsplitter --> embeddings\n",
        "      embeddings --> vectorstore\n",
        "      classDef default fill:#CCE6FF,stroke:#84BCF5,textColor:#1C2B33,fontFamily:trebuchet ms;\n",
        "  \"\"\")\n",
        "\n",
        "def mem_context():\n",
        "  mm(\"\"\"\n",
        "      graph LR\n",
        "      context(text)\n",
        "      user_prompt --> context\n",
        "      instruction --> context\n",
        "      examples --> context\n",
        "      memory --> context\n",
        "      context --> tokenizer\n",
        "      tokenizer --> embeddings\n",
        "      embeddings --> LLM\n",
        "      classDef default fill:#CCE6FF,stroke:#84BCF5,textColor:#1C2B33,fontFamily:trebuchet ms;\n",
        "  \"\"\")\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "i4Np_l_KtIno"
      },
      "source": [
        "##**1 - Understanding Llama 3**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PGPSI3M5PGTi"
      },
      "source": [
        "### **1.1 - What is Llama 3?**\n",
        "\n",
        "* State of the art (SOTA), Open Source LLM\n",
        "* 8B, 70B\n",
        "* Choosing model: Size, Quality, Cost, Speed\n",
        "* Pretrained + Chat\n",
        "* [Meta Llama 3 Blog](https://ai.meta.com/blog/meta-llama-3/)\n",
        "* [Getting Started with Meta Llama](https://llama.meta.com/docs/get-started)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "OXRCC7wexZXd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 323
        },
        "outputId": "5d67c821-6c5e-47a8-dd62-99e6470d8e33"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<img src=\"https://mermaid.ink/img/CiAgZ3JhcGggTFI7CiAgICAgIGxsYW1hLTMgLS0+IGxsYW1hLTMtOGIKICAgICAgbGxhbWEtMyAtLT4gbGxhbWEtMy03MGIKICAgICAgbGxhbWEtMy04YiAtLT4gbGxhbWEtMy04Yi1iYXNlCiAgICAgIGxsYW1hLTMtOGIgLS0+IGxsYW1hLTMtOGItaW5zdHJ1Y3QKICAgICAgbGxhbWEtMy03MGIgLS0+IGxsYW1hLTMtNzBiLWJhc2UKICAgICAgbGxhbWEtMy03MGIgLS0+IGxsYW1hLTMtNzBiLWluc3RydWN0CiAgICAgIGNsYXNzRGVmIGRlZmF1bHQgZmlsbDojQ0NFNkZGLHN0cm9rZTojODRCQ0Y1LHRleHRDb2xvcjojMUMyQjMzLGZvbnRGYW1pbHk6dHJlYnVjaGV0IG1zOwogIA==\"/>"
            ],
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {}
        }
      ],
      "source": [
        "llama3_family()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aYeHVVh45bdT"
      },
      "source": [
        "### **1.2 - Accessing Llama 3**\n",
        "* Download + Self Host (i.e. [download Llama](https://ai.meta.com/resources/models-and-libraries/llama-downloads))\n",
        "* Hosted API Platform (e.g. [Replicate](https://replicate.com/meta/meta-llama-3-8b-instruct), [Together](https://api.together.xyz/playground/language/meta-llama/Llama-3-8b-hf), [Anyscale](https://app.endpoints.anyscale.com/playground))\n",
        "\n",
        "* Hosted Container Platform (e.g. [Azure](https://techcommunity.microsoft.com/t5/ai-machine-learning-blog/introducing-llama-2-on-azure/ba-p/3881233), [AWS](https://aws.amazon.com/blogs/machine-learning/llama-2-foundation-models-from-meta-are-now-available-in-amazon-sagemaker-jumpstart/), [GCP](https://console.cloud.google.com/vertex-ai/publishers/google/model-garden/139))\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kBuSay8vtzL4"
      },
      "source": [
        "### **1.3 - Use Cases of Llama 3**\n",
        "* Content Generation\n",
        "* Summarization\n",
        "* General Chatbots\n",
        "* RAG (Retrieval Augmented Generation): Chat about Your Own Data\n",
        "* Fine-tuning\n",
        "* Agents"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sd54g0OHuqBY"
      },
      "source": [
        "## **2 - Using and Comparing Llama 3 and Llama 2**\n",
        "\n",
        "In this notebook, we will use the Llama 2 7b chat and Llama 3 8b instruct models hosted on [Replicate](https://replicate.com/blog/run-llama-3-with-an-api?input=python). You'll need to first [sign in](https://replicate.com/signin) with your github account, then get an [API token](https://replicate.com/account/api-tokens) to try Replicate out for free.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "h3YGMDJidHtH"
      },
      "source": [
        "### **2.1 - Install dependencies**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 62,
      "metadata": {
        "id": "VhN6hXwx7FCp",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4b29a123-103f-4d83-9adc-64dcfd4024ca"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: replicate in /usr/local/lib/python3.10/dist-packages (0.25.2)\n",
            "Requirement already satisfied: httpx<1,>=0.21.0 in /usr/local/lib/python3.10/dist-packages (from replicate) (0.27.0)\n",
            "Requirement already satisfied: packaging in /usr/local/lib/python3.10/dist-packages (from replicate) (23.2)\n",
            "Requirement already satisfied: pydantic>1.10.7 in /usr/local/lib/python3.10/dist-packages (from replicate) (2.7.0)\n",
            "Requirement already satisfied: typing-extensions>=4.5.0 in /usr/local/lib/python3.10/dist-packages (from replicate) (4.11.0)\n",
            "Requirement already satisfied: anyio in /usr/local/lib/python3.10/dist-packages (from httpx<1,>=0.21.0->replicate) (3.7.1)\n",
            "Requirement already satisfied: certifi in /usr/local/lib/python3.10/dist-packages (from httpx<1,>=0.21.0->replicate) (2024.2.2)\n",
            "Requirement already satisfied: httpcore==1.* in /usr/local/lib/python3.10/dist-packages (from httpx<1,>=0.21.0->replicate) (1.0.5)\n",
            "Requirement already satisfied: idna in /usr/local/lib/python3.10/dist-packages (from httpx<1,>=0.21.0->replicate) (3.7)\n",
            "Requirement already satisfied: sniffio in /usr/local/lib/python3.10/dist-packages (from httpx<1,>=0.21.0->replicate) (1.3.1)\n",
            "Requirement already satisfied: h11<0.15,>=0.13 in /usr/local/lib/python3.10/dist-packages (from httpcore==1.*->httpx<1,>=0.21.0->replicate) (0.14.0)\n",
            "Requirement already satisfied: annotated-types>=0.4.0 in /usr/local/lib/python3.10/dist-packages (from pydantic>1.10.7->replicate) (0.6.0)\n",
            "Requirement already satisfied: pydantic-core==2.18.1 in /usr/local/lib/python3.10/dist-packages (from pydantic>1.10.7->replicate) (2.18.1)\n",
            "Requirement already satisfied: exceptiongroup in /usr/local/lib/python3.10/dist-packages (from anyio->httpx<1,>=0.21.0->replicate) (1.2.1)\n"
          ]
        }
      ],
      "source": [
        "!pip install replicate"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fQmzrWakcyd2"
      },
      "source": [
        "### **2.2 - Create helpers for Llama 2 and Llama 3**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "C4P-b0eQcyd2"
      },
      "source": [
        "First, set your Replicate API token as environment variables.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 63,
      "metadata": {
        "id": "yE3sPjS-cyd2",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6d88c481-3009-4678-e595-91b8e167a9a5"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "··········\n"
          ]
        }
      ],
      "source": [
        "import os\n",
        "from getpass import getpass\n",
        "\n",
        "REPLICATE_API_TOKEN = getpass()\n",
        "\n",
        "os.environ[\"REPLICATE_API_TOKEN\"] = REPLICATE_API_TOKEN"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jMiJESNRcyd2"
      },
      "source": [
        "Create Llama 2 and Llama 3 helper functions - for chatbot type of apps, we'll use Llama 3 8b/70b instruct models, not the base models."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 64,
      "metadata": {
        "id": "o7KQ1wp-cyd2"
      },
      "outputs": [],
      "source": [
        "import replicate\n",
        "\n",
        "def llama2(prompt, temperature=0.0, input_print=True):\n",
        "  output = replicate.run(\n",
        "    \"meta/llama-2-7b-chat\",\n",
        "    input={\n",
        "        \"prompt\": prompt,\n",
        "        \"max_tokens\": 2048,\n",
        "        \"temperature\": temperature})\n",
        "  return \"\".join(output)\n",
        "\n",
        "def llama3_8b(prompt, temperature=0.0):\n",
        "  output = replicate.run(\n",
        "    \"meta/meta-llama-3-8b-instruct\",\n",
        "    input={\n",
        "        \"prompt\": prompt,\n",
        "        \"max_tokens\": 2048,\n",
        "        \"temperature\": temperature})\n",
        "  return \"\".join(output)\n",
        "\n",
        "def llama3_70b(prompt, temperature=0.0):\n",
        "  output = replicate.run(\n",
        "    \"meta/meta-llama-3-70b-instruct\",\n",
        "    input={\n",
        "        \"prompt\": prompt,\n",
        "        \"max_tokens\": 2048,\n",
        "        \"temperature\": temperature})\n",
        "  return \"\".join(output)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5Jxq0pmf6L73"
      },
      "source": [
        "### **2.3 - Basic QA with Llama 2 and 3**"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "prompt = \"The typical color of Llama is: \"\n",
        "output = llama2(prompt)\n",
        "md(output)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 81
        },
        "id": "UtPLbKcZHLQJ",
        "outputId": "a0a5b427-cc6f-4350-cc79-8aa4e532c7e2"
      },
      "execution_count": 65,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Markdown object>"
            ],
            "text/markdown": " Hello! I'm here to help you with your question. However, I must inform you that the typical color of a llama is not a factual or coherent question. Llamas can come in a variety of colors, including white, gray, brown, and black, among others. So, I'm afraid I cannot provide a definitive answer to this question. Is there anything else I can help you with?"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "output = llama3_8b(prompt)\n",
        "md(output)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 46
        },
        "id": "YgGbIYw6Yg71",
        "outputId": "a9d318e2-2d03-4eb7-a48b-b7e3753a1a0b"
      },
      "execution_count": 66,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Markdown object>"
            ],
            "text/markdown": "The typical color of a llama is white! However, llamas can also come in a variety of other colors, including light brown, dark brown, gray, and even black!"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Note: Llama 3 in not as chatty as Llama 2.**"
      ],
      "metadata": {
        "id": "tfn8LRwZYo4q"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **2.4 - Changing temperature when calling Llama 3**\n",
        "\n",
        "Temperature controls the randomness of LLM's output: with the values range from 0.0 for the most determinstic output to 1.0 for the most random output."
      ],
      "metadata": {
        "id": "GioJ0XV-lhzL"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "output = llama3_8b(prompt, temperature=0.0)\n",
        "md(output)"
      ],
      "metadata": {
        "id": "xml21thkkYVG",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 46
        },
        "outputId": "94301048-3497-4128-febe-dc196af8b2d0"
      },
      "execution_count": 69,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Markdown object>"
            ],
            "text/markdown": "The typical color of a llama is white! However, llamas can also come in a variety of other colors, including light brown, dark brown, gray, and even black!"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "output = llama3_8b(prompt, temperature=0.0)\n",
        "md(output)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 46
        },
        "id": "P5IBmhgFtJcB",
        "outputId": "457aa632-561e-4d8d-cb0e-ca9fdcd9b9e6"
      },
      "execution_count": 73,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Markdown object>"
            ],
            "text/markdown": "The typical color of a llama is white! However, llamas can also come in a variety of other colors, including light brown, dark brown, gray, and even black!"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "output = llama3_8b(prompt, temperature=1.0)\n",
        "md(output)"
      ],
      "metadata": {
        "id": "9zL89Ca_kYXy",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 46
        },
        "outputId": "af5c46aa-e1ee-4be6-b414-d4f3b5560039"
      },
      "execution_count": 71,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Markdown object>"
            ],
            "text/markdown": "The typical color of a llama is a soft, fleecy brown color, often with a slightly darker stripe running along their back."
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "output = llama3_8b(prompt, temperature=1.0)\n",
        "md(output)"
      ],
      "metadata": {
        "id": "7jFGMwQ1kYaM",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 46
        },
        "outputId": "49262874-6ac1-4f5b-951a-d7b3cd888808"
      },
      "execution_count": 72,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Markdown object>"
            ],
            "text/markdown": "That's an easy one! The typical color of a llama is white. However, some llamas can have a variety of colors, including light brown, dark brown, and black."
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Note: Two calls with temperature 0.0 give the exactly same results. Two calls with temperature 1.0 give wildly different answers.**"
      ],
      "metadata": {
        "id": "a_s1vLGhoKdy"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "r4DyTLD5ys6t"
      },
      "source": [
        "## **3 - Chat conversation**\n",
        "* LLMs are stateless\n",
        "* Single Turn\n",
        "* Multi Turn (Memory)\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "bot_arch()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 177
        },
        "id": "HWgraOFxgtIk",
        "outputId": "c5401e40-d2f1-4a11-c7be-49f25a0918bc"
      },
      "execution_count": 74,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<img src=\"https://mermaid.ink/img/CiAgZ3JhcGggTFI7CiAgdXNlciAtLT4gcHJvbXB0CiAgcHJvbXB0IC0tPiBpX3NhZmV0eQogIGlfc2FmZXR5IC0tPiBjb250ZXh0CiAgY29udGV4dCAtLT4gTGxhbWFfMwogIExsYW1hXzMgLS0+IG91dHB1dAogIG91dHB1dCAtLT4gb19zYWZldHkKICBpX3NhZmV0eSAtLT4gbWVtb3J5CiAgb19zYWZldHkgLS0+IG1lbW9yeQogIG1lbW9yeSAtLT4gY29udGV4dAogIG9fc2FmZXR5IC0tPiB1c2VyCiAgY2xhc3NEZWYgZGVmYXVsdCBmaWxsOiNDQ0U2RkYsc3Ryb2tlOiM4NEJDRjUsdGV4dENvbG9yOiMxQzJCMzMsZm9udEZhbWlseTp0cmVidWNoZXQgbXM7CiAg\"/>"
            ],
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **3.1 - Single-turn chat**"
      ],
      "metadata": {
        "id": "P6mTEV_u29j7"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 77,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 81
        },
        "id": "EMM_egWMys6u",
        "outputId": "cbf2743a-54ef-4161-c3a8-53d1fabc6e1b"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Markdown object>"
            ],
            "text/markdown": " Thank you for your question! The average lifespan of a llama is around 15-20 years. However, it's important to note that the lifespan of a llama can vary depending on several factors such as breed, living conditions, and health. Some llamas have been known to live up to 30 years or more with proper care and attention. It's always best to consult with a veterinarian or a llama breeder for more specific information. Is there anything else I can help you with?"
          },
          "metadata": {}
        }
      ],
      "source": [
        "prompt_chat = \"What is the average lifespan of a Llama? Answer the question in few words.\"\n",
        "output = llama2(prompt_chat)\n",
        "md(output)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "output = llama3_8b(prompt_chat)\n",
        "md(output)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 64
        },
        "id": "IEFB5DQgYyYb",
        "outputId": "02cf6dd1-5f26-4b82-a7a0-50f04efa4836"
      },
      "execution_count": 78,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Markdown object>"
            ],
            "text/markdown": "According to the American Llama Association, the average lifespan of a llama is between 20 to 30 years. However, with proper care and management, some llamas have been known to live into their 40s."
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 79,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 46
        },
        "id": "sZ7uVKDYucgi",
        "outputId": "31d8c228-fbe6-4193-817c-c7d66b8c5a1f"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Markdown object>"
            ],
            "text/markdown": " Sure, I'd be happy to help! The animal family that the question is referring to is the \"Carnivora\" family, which includes mammals that primarily feed on meat."
          },
          "metadata": {}
        }
      ],
      "source": [
        "# example without previous context. LLM's are stateless and cannot understand \"they\" without previous context\n",
        "prompt_chat = \"What animal family are they? Answer the question in few words.\"\n",
        "output = llama2(prompt_chat)\n",
        "md(output)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "output = llama3_8b(prompt_chat)\n",
        "md(output)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 46
        },
        "id": "uM0QivYnY39B",
        "outputId": "1794fcce-010b-4b7b-bd08-de037d5eacf2"
      },
      "execution_count": 81,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Markdown object>"
            ],
            "text/markdown": "Mammal."
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "output = llama3_70b(prompt_chat)\n",
        "md(output)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 46
        },
        "id": "jYe6rPgIZQuC",
        "outputId": "c1905c46-ee0a-4e1f-db31-b4ca74e4746a"
      },
      "execution_count": 80,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Markdown object>"
            ],
            "text/markdown": "Please provide the animal's name, and I'll respond with its family in a few words."
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Note: Llama 3 70b doesn't hallucinate.**"
      ],
      "metadata": {
        "id": "SlUhajKvY9r4"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **3.2 - Multi-turn chat**\n",
        "Chat app requires us to send in previous context to LLM to get in valid responses. Below is an example of Multi-turn chat."
      ],
      "metadata": {
        "id": "5L_8y9Rh3Ym-"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 82,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 64
        },
        "id": "t7SZe5fT3HG3",
        "outputId": "82dc478e-8b5f-4230-d137-9c42bb2d69f1"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Markdown object>"
            ],
            "text/markdown": " Assistant: Thank you for your question! Llamas are actually members of the camel family, not an animal family. They are part of the Camelidae family, which includes camels, llamas, and alpacas. The average lifespan of a llama is around 15-20 years. Is there anything else I can help you with?"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# example of multi-turn chat, with storing previous context\n",
        "prompt_chat = \"\"\"\n",
        "User: What is the average lifespan of a Llama?\n",
        "Assistant: 15-20 years.\n",
        "User: What animal family are they?\n",
        "\"\"\"\n",
        "output = llama2(prompt_chat)\n",
        "md(output)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "output = llama3_8b(prompt_chat)\n",
        "md(output)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 46
        },
        "id": "xqj2uLQnZq7O",
        "outputId": "3a1092c3-ee12-4475-8b13-320cd942911d"
      },
      "execution_count": 83,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Markdown object>"
            ],
            "text/markdown": "Llamas are members of the camelid family, which also includes camels, alpacas, and guanacos."
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Note: Llama 2 and 3 all behave well for using the chat history for follow up questions.**"
      ],
      "metadata": {
        "id": "HWJ9GBkg31Y3"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **3.3 - Multi-turn chat with more instruction**\n",
        "Adding the instructon \"Answer the question with one word\" to see the difference of Llama 2 and 3."
      ],
      "metadata": {
        "id": "x-l3tf5n4P7P"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 46
        },
        "id": "CPknNap5cyd3",
        "outputId": "ed80bc4c-5858-48f4-a408-f9c003c56de4"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Markdown object>"
            ],
            "text/markdown": " Sure! Llamas are part of the Camelidae family."
          },
          "metadata": {}
        }
      ],
      "source": [
        "# example of multi-turn chat, with storing previous context\n",
        "prompt_chat = \"\"\"\n",
        "User: What is the average lifespan of a Llama?\n",
        "Assistant: Sure! The average lifespan of a llama is around 20-30 years.\n",
        "User: What animal family are they?\n",
        "\n",
        "Answer the question with one word.\n",
        "\"\"\"\n",
        "output = llama2(prompt_chat)\n",
        "md(output)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "output = llama3_8b(prompt_chat)\n",
        "md(output)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 46
        },
        "id": "ncXJDhtlaCDK",
        "outputId": "c33d75fe-b1e3-46f2-b0bd-ec8b23715a29"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Markdown object>"
            ],
            "text/markdown": "Camelid."
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Llama 3 follows instructions (e.g. \"Answer the question one word\") better!**"
      ],
      "metadata": {
        "id": "VS2j0tQRaIDw"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "moXnmJ_xyD10"
      },
      "source": [
        "## **4 - Prompt Engineering**\n",
        "* Prompt engineering refers to the science of designing effective prompts to get desired responses\n",
        "\n",
        "* Helps reduce hallucination\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "t-v-FeZ4ztTB"
      },
      "source": [
        "#### **4.1 - In-Context Learning (e.g. Zero-shot, Few-shot)**\n",
        " * In-context learning - specific method of prompt engineering where demonstration of task are provided as part of prompt.\n",
        "  1. Zero-shot learning - model is performing tasks without any\n",
        "input examples.\n",
        "  2. Few or “N-Shot” Learning - model is performing and behaving based on input examples in user's prompt."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 46
        },
        "id": "6W71MFNZyRkQ",
        "outputId": "2ab17f57-bb8e-474a-fb47-c1872e12349c"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Markdown object>"
            ],
            "text/markdown": " Interesting!"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Zero-shot example. To get positive/negative/neutral sentiment, we need to give examples in the prompt\n",
        "prompt = '''\n",
        "Classify: I saw a Gecko.\n",
        "Sentiment: ?\n",
        "\n",
        "Give one word response.\n",
        "'''\n",
        "output = llama2(prompt)\n",
        "md(output)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "output = llama3_8b(prompt)\n",
        "md(output)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 46
        },
        "id": "Bdm3YHracMWt",
        "outputId": "87cd6da6-4e34-40a3-e967-6a27f687d01d"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Markdown object>"
            ],
            "text/markdown": "Neutral"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Note: Llama 3 has different opinions than Llama 2.**"
      ],
      "metadata": {
        "id": "PtTKtMnP48aR"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 116
        },
        "id": "MCQRjf1Y1RYJ",
        "outputId": "010a128e-7fc6-4ff0-f5e1-3d711ae542a9"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Markdown object>"
            ],
            "text/markdown": " Sure, I'd be happy to help! Here are the classifications and sentiments for the three statements you provided:\n\nClassify: I love Llamas!\nSentiment: Positive\n\nClassify: I dont like Snakes.\nSentiment: Negative\n\nClassify: I saw a Gecko.\nSentiment: Neutral"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# By giving examples to Llama, it understands the expected output format.\n",
        "\n",
        "prompt = '''\n",
        "Classify: I love Llamas!\n",
        "Sentiment: Positive\n",
        "Classify: I dont like Snakes.\n",
        "Sentiment: Negative\n",
        "Classify: I saw a Gecko.\n",
        "Sentiment:\n",
        "\n",
        "Give one word response.\n",
        "'''\n",
        "\n",
        "output = llama2(prompt)\n",
        "md(output)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "output = llama3_8b(prompt)\n",
        "md(output)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 46
        },
        "id": "ahJodhNScXP4",
        "outputId": "ac309782-603a-4abd-d958-5ef5682c3a20"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Markdown object>"
            ],
            "text/markdown": "Neutral"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Note: Again, Llama 3 follows instructions better. Llama 2 now, with few shots, has the same output \"Neutral\" as Llama 3.**"
      ],
      "metadata": {
        "id": "GAHLuRFE5RJM"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 84,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 46
        },
        "id": "8UmdlTmpDZxA",
        "outputId": "2abfec15-4a5c-426b-b0e9-1c288b54b529"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Markdown object>"
            ],
            "text/markdown": "Fiber."
          },
          "metadata": {}
        }
      ],
      "source": [
        "# another zero-shot learning\n",
        "prompt = '''\n",
        "QUESTION: Vicuna?\n",
        "ANSWER:\n",
        "\n",
        "Answer with one word.\n",
        "'''\n",
        "\n",
        "output = llama3_8b(prompt)\n",
        "md(output)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "output = llama3_70b(prompt)\n",
        "md(output)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 46
        },
        "id": "6jKftdYHckEk",
        "outputId": "e64ae694-a1db-43ec-e72d-cc073507c110"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Markdown object>"
            ],
            "text/markdown": "Llama."
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 29,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 46
        },
        "id": "M_EcsUo1zqFD",
        "outputId": "7b3dac55-ee3a-4d49-d836-6ec7b0ad6d89"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Markdown object>"
            ],
            "text/markdown": "Yes"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Another few-shot learning example with formatted prompt.\n",
        "\n",
        "prompt = '''\n",
        "QUESTION: Llama?\n",
        "ANSWER: Yes\n",
        "QUESTION: Alpaca?\n",
        "ANSWER: Yes\n",
        "QUESTION: Rabbit?\n",
        "ANSWER: No\n",
        "QUESTION: Vicuna?\n",
        "ANSWER:\n",
        "'''\n",
        "\n",
        "output = llama3_8b(prompt)\n",
        "md(output)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "output = llama3_70b(prompt)\n",
        "md(output)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 110
        },
        "id": "arUlC1sNcsxt",
        "outputId": "cb1f784c-a039-489a-ccc5-5e6dc35dd2a9"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Markdown object>"
            ],
            "text/markdown": "I think I see a pattern!\n\nSince the answer is \"Yes\" for Llama and Alpaca, which are both camelids, I'm going to take a guess that the answer is also \"Yes\" for Vicuna, which is another type of camelid.\n\nSo, my answer would be: Yes"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Note: Llama 3 70b explains the answer.**"
      ],
      "metadata": {
        "id": "yWr0H0N_5y0R"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# let's see if Llama 3 70b can just give the answer without explanation.\n",
        "prompt = '''\n",
        "QUESTION: Llama?\n",
        "ANSWER: Yes\n",
        "QUESTION: Alpaca?\n",
        "ANSWER: Yes\n",
        "QUESTION: Rabbit?\n",
        "ANSWER: No\n",
        "QUESTION: Vicuna?\n",
        "ANSWER:\n",
        "\n",
        "Answer with one word.\n",
        "'''\n",
        "\n",
        "output = llama3_70b(prompt)\n",
        "md(output)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 46
        },
        "id": "NYbCUrnJ6Dj0",
        "outputId": "ab3a45b7-16dd-40ae-c25b-2a3f9cf5dcef"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Markdown object>"
            ],
            "text/markdown": "Yes"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# how about Llama 2\n",
        "output = llama2(prompt)\n",
        "md(output)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 70
        },
        "id": "D2aeGVS76b8I",
        "outputId": "793e5ad3-aba3-430e-8d78-8da38425b2ed"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Markdown object>"
            ],
            "text/markdown": " Sure! Here is the answer to the question:\n\nVicuna - No"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Note: Clearly Llama 3 performing better than Llama 2!**"
      ],
      "metadata": {
        "id": "7T2zKKXc6jEq"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mbr124Y197xl"
      },
      "source": [
        "#### **4.2 - Chain of Thought**\n",
        "\"Chain of thought\" enables complex reasoning through logical step by step thinking and generates meaningful and contextually relevant responses."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 85,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 46
        },
        "id": "Xn8zmLBQzpgj",
        "outputId": "85217f57-9927-4c8a-d203-6f1188403261"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Markdown object>"
            ],
            "text/markdown": "Seventeen."
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Standard prompting\n",
        "prompt = '''\n",
        "Llama started with 5 tennis balls. It buys 2 more cans of tennis balls. Each can has 3 tennis balls.\n",
        "How many tennis balls does Llama have?\n",
        "\n",
        "Answer in one word.\n",
        "'''\n",
        "\n",
        "output = llama3_8b(prompt)\n",
        "md(output)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "output = llama3_70b(prompt)\n",
        "md(output)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 46
        },
        "id": "BtJu3lURc4bX",
        "outputId": "60b336da-b7c7-4250-c822-79bb688eaa64"
      },
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Markdown object>"
            ],
            "text/markdown": "Eleven."
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Note: Llama 3-8b did not get the right answer because it was asked to answer in one word.**"
      ],
      "metadata": {
        "id": "GaLrTzsLc-1H"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 86,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 163
        },
        "id": "lKNOj79o1Kwu",
        "outputId": "2f5895a5-a80e-43ed-b7b7-a65e47d423e0"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Markdown object>"
            ],
            "text/markdown": "Let's break it down step by step!\n\nLlama starts with 5 tennis balls.\n\nThen, Llama buys 2 more cans of tennis balls. Each can has 3 tennis balls.\n\nSo, Llama gets 2 x 3 = 6 more tennis balls.\n\nIn total, Llama has 5 (initial) + 6 (additional) = 11 tennis balls.\n\nSo, the answer is 11!"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# By default, Llama 3 models follow \"Chain-Of-Thought\" prompting\n",
        "prompt = '''\n",
        "Llama started with 5 tennis balls. It buys 2 more cans of tennis balls. Each can has 3 tennis balls.\n",
        "How many tennis balls does Llama have?\n",
        "'''\n",
        "\n",
        "output = llama3_8b(prompt)\n",
        "md(output)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "output = llama3_70b(prompt)\n",
        "md(output)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 187
        },
        "id": "yKauEFrAdQaK",
        "outputId": "13eaf869-6435-4596-b101-43a295dd5c82"
      },
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Markdown object>"
            ],
            "text/markdown": "Let's break it down step by step!\n\nLlama starts with 5 tennis balls.\n\nIt buys 2 more cans of tennis balls, and each can has 3 tennis balls. So, Llama gets:\n\n2 cans x 3 tennis balls/can = 6 new tennis balls\n\nNow, let's add the new tennis balls to the ones Llama already had:\n\n5 (initial tennis balls) + 6 (new tennis balls) = 11\n\nSo, Llama now has 11 tennis balls!"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Note: By default, Llama 3 models identify word problems and solves it step by step!**"
      ],
      "metadata": {
        "id": "nlFGjySboVhw"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Another example of reasoning without asking for thinking step by step\n",
        "prompt = '''\n",
        "Who lived longer Newton or Einstein and by how many years?\n",
        "Answer in a sentence.\n",
        "'''\n",
        "\n",
        "output = llama3_8b(prompt)\n",
        "md(output)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 46
        },
        "id": "eeawJbT7vzrX",
        "outputId": "a409d47f-e6f8-4fed-95e6-f34bfe0bbde4"
      },
      "execution_count": 87,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Markdown object>"
            ],
            "text/markdown": "Sir Isaac Newton lived from 1643 to 1727, while Albert Einstein lived from 1879 to 1955, with Einstein living 128 years, 28 years longer than Newton."
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Note: Einstein's age was calculated incorrectly.**"
      ],
      "metadata": {
        "id": "EUhSQJnNmjDh"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "output = llama3_70b(prompt)\n",
        "md(output)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 64
        },
        "id": "NSQvuz9ywHGX",
        "outputId": "1e1e6984-2950-45ac-baaa-a6efafe69416"
      },
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Markdown object>"
            ],
            "text/markdown": "Albert Einstein lived longer than Isaac Newton, passing away at the age of 76 in 1955, while Newton died at the age of 84 in 1727, so Newton outlived Einstein by 8 years."
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Note: Llama 2 often needs encourgement for step by step thinking to correctly reasoning. Llama 3 understands, reasons and explains better, making chain of thought unnecessary in the cases above.**"
      ],
      "metadata": {
        "id": "oa1hPHR4wIRA"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### **4.3 - Knowledge Prompting**\n",
        "\n",
        " Knowledge prompting involves crafting a prompt or question that provides context, defines the task, and specifies the desired output. The goal is to \"prompt\" the AI to produce a relevant and accurate response.\n",
        "\n"
      ],
      "metadata": {
        "id": "F8Onz2U1DJS9"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# knowledge prompting\n",
        "prompt = '''\n",
        "  Part of golf is trying to get a higher point total than others. Yes or No?\n",
        "'''\n",
        "\n",
        "output = llama3_8b(prompt)\n",
        "md(output)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 46
        },
        "id": "3MSyKn0CIqjP",
        "outputId": "3ff11e79-b1ff-4cad-bcf1-28353733b859"
      },
      "execution_count": 88,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Markdown object>"
            ],
            "text/markdown": "Yes."
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "prompt = '''\n",
        "Part of golf is trying to get a higher point total than others. Yes or No?\n",
        "The objective of golf is to play a set of holes in the least number of strokes.\n",
        "\n",
        "Explain your answer.\n",
        "'''\n",
        "\n",
        "output = llama3_8b(prompt)\n",
        "md(output)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 128
        },
        "id": "aN7MnLy8IzFF",
        "outputId": "c40fa173-d260-4691-fb29-f6fd98ebef18"
      },
      "execution_count": 89,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Markdown object>"
            ],
            "text/markdown": "I would answer NO.\n\nThe objective of golf, as stated, is to play a set of holes in the least number of strokes. This means that the goal is to complete the course in as few strokes as possible, not to get a higher point total than others.\n\nIn golf, the scoring system is based on the number of strokes taken to complete each hole. The player with the lowest total score at the end of the round is the winner."
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "prompt = '''\n",
        "Part of golf is trying to get a higher point total than others. Yes or No?\n",
        "'''\n",
        "\n",
        "output = llama3_70b(prompt)\n",
        "md(output)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 87
        },
        "id": "YCMUJoMII1Wf",
        "outputId": "e1e14f35-358e-4a86-e982-33c84fa38b61"
      },
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Markdown object>"
            ],
            "text/markdown": "No.\n\nIn golf, the objective is actually to achieve the lowest score possible, not to get a higher point total than others. The golfer with the lowest score at the end of the round is typically considered the winner."
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Note: Smaller models sometimes can be inaccurate. But by providing relevant context, they can perform as good as larger model.**"
      ],
      "metadata": {
        "id": "VnQbr1QNYzXT"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "C7tDW-AH770Y"
      },
      "source": [
        "### **4.4 - Retrieval Augmented Generation (RAG)**\n",
        "* Prompt Engineering Limitations - Knowledge cutoff & lack of specialized data\n",
        "\n",
        "* Retrieval Augmented Generation(RAG) allows us to retrieve snippets of information from external data sources and augment it to the user's prompt to get tailored responses from Llama 3.\n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XiZuIIAPpM77"
      },
      "source": [
        "#### **4.4.1 - In-place RAG**\n",
        "- A model's knowledge of the world ends at the moment of its training - so it won't know about more recent events.\n",
        "- Llama 3 8B has cutoff date of March 2023, and Llama 3 70B December 2023, while Llama 2 September 2022.\n",
        "- Ask the model about an event, in this case, FIFA Women's World Cup 2023, which started on July 20, 2023, and see how the model responses."
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "prompt = '''\n",
        "Today's date: April 22, 2024. Who won the 2023 Women's World Cup?\n",
        "'''\n",
        "output = llama2(prompt)\n",
        "print(output)"
      ],
      "metadata": {
        "id": "hp5MQqqHXhA5",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c558bd26-1f74-4cf2-f46b-61bb744d6c0e"
      },
      "execution_count": 90,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            " Hello! I'm here to help you with your question. However, I must inform you that the 2023 Women's World Cup has not yet taken place. The Women's World Cup is held every four years, and the most recent tournament was held in 2019. Therefore, there is no winner for the 2023 Women's World Cup. I hope this information is helpful. If you have any other questions, please feel free to ask!\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# cutoff date for 8B is March 2023.\n",
        "output = llama3_8b(prompt)\n",
        "print(output)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4M3isB_t-D3P",
        "outputId": "5901dc6a-59d5-4e60-e8b6-96bce8d6e360"
      },
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The 2023 Women's World Cup was won by the United States Women's National Team (USWNT). They defeated England 2-1 in the final on August 20, 2023.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# cutoff dte for 70B is December 2023\n",
        "output = llama3_70b(prompt)\n",
        "print(output)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SawSsUzJ-EAL",
        "outputId": "8d936b22-f9ab-4963-b9e8-79077b932297"
      },
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The 2023 Women's World Cup was won by Spain! They defeated England 1-0 in the final on August 20, 2023.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Note: Hmm, Llama 3 8b hallucinates due to lack of data. But 70b with cutoff date December 2023 is able to answer the question correctly.**"
      ],
      "metadata": {
        "id": "okrRMdag-SJp"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "context = \"\"\"\n",
        "The 2023 FIFA Women's World Cup was the ninth edition of the FIFA Women's World Cup, the quadrennial international women's\n",
        "football championship contested by women's national teams and organised by FIFA. The tournament, which took place from\n",
        "20 July to 20 August 2023, was jointly hosted by Australia and New Zealand.[2][3][4] It was the first FIFA Women's World Cup\n",
        "with more than one host nation, as well as the first World Cup to be held across multiple confederations, as Australia is in the\n",
        "Asian confederation, while New Zealand is in the Oceanian confederation. It was also the first Women's World Cup to be held\n",
        "in the Southern Hemisphere.[5]\n",
        "This tournament was the first to feature an expanded format of 32 teams from the previous 24, replicating the format used\n",
        "for the men's World Cup from 1998 to 2022.[2] The opening match was won by co-host New Zealand, beating Norway at Eden Park\n",
        "in Auckland on 20 July 2023 and achieving their first Women's World Cup victory.[6]\n",
        "Spain were crowned champions after defeating reigning European champions England 1–0 in the final. It was the first time a\n",
        "European nation had won the Women's World Cup since 2007 and Spain's first title, although their victory was marred by the\n",
        "Rubiales affair. Spain became the second nation to win both the women's and men's World Cup since Germany in the 2003 edition.\n",
        "[10] In addition, they became the first nation to concurrently hold the FIFA women's U-17, U-20, and senior World Cups.[11]\n",
        "Sweden would claim their fourth bronze medal at the Women's World Cup while co-host Australia achieved their best placing yet,\n",
        "finishing fourth.[12] Japanese player Hinata Miyazawa won the Golden Boot scoring five goals throughout the tournament. Spanish\n",
        "player Aitana Bonmatí was voted the tournament's best player, winning the Golden Ball, whilst Bonmatí's teammate Salma\n",
        "Paralluelo was awarded the Young Player Award. England goalkeeper Mary Earps won the Golden Glove, awarded to the best-performing\n",
        "goalkeeper of the tournament.\n",
        "Of the eight teams making their first appearance, Morocco were the only one to advance to the round of 16 (where they lost\n",
        "to France; coincidentally, the result of this fixture was similar to the men's World Cup in Qatar, where France defeated\n",
        "Morocco in the semi-final). The United States were the two-time defending champions,[13] but were eliminated in the round of\n",
        "16 by Sweden, the first time the team had not made the semi-finals at the tournament, and the first time the defending\n",
        "champions failed to progress to the quarter-finals.[14]\n",
        "Australia's team, nicknamed the Matildas, performed better than expected, and the event saw many Australians unite to\n",
        "support them. The Matildas, who beat France to make the semi-finals for the first time, saw record numbers of fans watching\n",
        "their games, their 3–1 loss to England becoming the most watched television broadcast in Australian history, with an average\n",
        "viewership of 7.13 million and a peak viewership of 11.15 million viewers.[18]\n",
        "It was the most attended edition of the competition ever held.\n",
        "\"\"\"\n",
        "\n",
        "prompt = f\"\"\"\n",
        "Given the following context, who won the 2023 Women's World cup?\n",
        "context: {context}\n",
        "\"\"\"\n",
        "output = llama2(prompt)\n",
        "print(output)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BwnCyEu--aqh",
        "outputId": "f4f0cfe3-6cf3-4ef8-8875-b48b9153a384"
      },
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            " I'm not able to provide an answer to your question as the context you provided does not mention the winner of the 2023 Women's World Cup. The tournament took place from July 20 to August 20, 2023, and the winner was not specified in the provided text.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "output = llama3_8b(prompt)\n",
        "print(output)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oimdbMm9-tz2",
        "outputId": "bb414d38-92ed-4eaa-81ed-470d42b808b7"
      },
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The 2023 FIFA Women's World Cup was the 9th edition of the tournament, held in Australia and New Zealand from July 20 to August 20, 2023.\n",
            "\n",
            "The tournament featured 32 teams, including the host nations Australia and New Zealand, as well as the defending champions United States.\n",
            "\n",
            "The tournament saw many exciting matches, with several teams making their debut in the tournament.\n",
            "\n",
            "The tournament was won by Spain, who defeated England 1-0 in the final.\n",
            "\n",
            "The tournament saw many records broken, including the highest attendance ever recorded in the history of the tournament.\n",
            "\n",
            "The tournament was a huge success, with many fans\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "output = llama3_70b(prompt)\n",
        "print(output)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_jQLVTNy-t3x",
        "outputId": "93b09e43-407c-459d-8a98-d0472328e7d8"
      },
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "According to the context, Spain won the 2023 Women's World Cup by defeating England 1-0 in the final.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Note: Llama 3 70b gives the best answer. Llama 2 fails.**"
      ],
      "metadata": {
        "id": "HYTklwK5_R8S"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "K2l8S5tBxlkc"
      },
      "source": [
        "#### **4.3.2 - Decomposing Question**\n",
        "Break down a complex question into sub questions to query RAG and Llama 3 with each sub question before getting the final answer from the sub questions and answers."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 110
        },
        "id": "NmEhBe3Kiyre",
        "outputId": "6c4e840b-aa05-4e93-b6fe-0b84c495adf0"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Markdown object>"
            ],
            "text/markdown": " Great, I'm here to help! To answer your question, I will break it down into simpler sub-parts. Here are the sub-questions that I have identified:\n\n{\"sub-questions\":[\n\"What is the time period being compared?\",\n\"What is the revenue data source?\",\n\"How was the revenue data collected and verified?\"\n]}\n\nPlease provide answers to these sub-questions, and I will help you calculate the revenue difference between Q1 of 2022 and Q2 of 2022."
          },
          "metadata": {}
        }
      ],
      "source": [
        "prompt = \"\"\"GENERAL INSTRUCTIONS\n",
        "You are a domain expert. Your task is to break down a complex question into simpler sub-parts.\n",
        "\n",
        "USER QUESTION\n",
        "What's the revenue difference between Q1 of 2022 and Q2 of 2022?\n",
        "\n",
        "ANSWER FORMAT\n",
        "{\"sub-questions\":[\"<FILL>\"]}\"\"\"\n",
        "\n",
        "output = llama2(prompt)\n",
        "md(output)\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "output = llama3_8b(prompt)\n",
        "md(output)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 87
        },
        "id": "KLBNz6JWZFlT",
        "outputId": "b467ff40-387a-44f1-9252-7853badff472"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Markdown object>"
            ],
            "text/markdown": "Here's the breakdown of the complex question into simpler sub-parts:\n\n{\"sub-questions\": [\n\"What is the revenue for Q1 of 2022?\",\n\"What is the revenue for Q2 of 2022?\",\n\"What is the difference between the revenue for Q1 of 2022 and Q2 of 2022?\"\n]}\nLet me know if you'd like me to help with any of these sub-questions!"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "output = llama3_70b(prompt)\n",
        "md(output)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 87
        },
        "id": "qPYhfW1Ad8wq",
        "outputId": "35ee5bed-1148-42a5-c6ae-e82b7d2439b2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Markdown object>"
            ],
            "text/markdown": "Here's the breakdown of the complex question into simpler sub-parts:\n\n{\"sub-questions\":[\n\"What was the revenue in Q1 of 2022?\",\n\"What was the revenue in Q2 of 2022?\",\n\"What is the difference between the revenue in Q1 and Q2 of 2022?\"\n]}"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Note: Llama 3 correctly breaks down a complex question according to the instruction. Llama 2 doesn't.**"
      ],
      "metadata": {
        "id": "MTP-I1psfJUZ"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### **4.3.3 - Query a Doc using LangChain**\n",
        "We'll show how to use LangChain, a framework for building non-trivial LLM apps, to build a RAG app.\n",
        "\n"
      ],
      "metadata": {
        "id": "SWCqFW46VocT"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "rag_arch()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 258
        },
        "id": "9pqROU1TWcTl",
        "outputId": "ff6f8d50-04aa-4557-eafd-6e417515dcf4"
      },
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<img src=\"https://mermaid.ink/img/CiAgZmxvd2NoYXJ0IFRECiAgICBBW1VzZXIgUHJvbXB0c10gLS0+IEIoRnJhbWV3b3JrcyBlLmcuIExhbmdDaGFpbikKICAgIEIgPC0tPiB8RGF0YWJhc2UsIERvY3MsIFhMU3xDW2ZhOmZhLWRhdGFiYXNlIEV4dGVybmFsIERhdGFdCiAgICBCIC0tPnxBUEl8RFtMbGFtYSAzXQogICAgY2xhc3NEZWYgZGVmYXVsdCBmaWxsOiNDQ0U2RkYsc3Ryb2tlOiM4NEJDRjUsdGV4dENvbG9yOiMxQzJCMzMsZm9udEZhbWlseTp0cmVidWNoZXQgbXM7CiAg\"/>"
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# this won't be needed after langchain's new release with Replicate fix for Llama 3\n",
        "!pip install git+https://github.com/langchain-ai/langchain.git#subdirectory=libs/community\n",
        "\n",
        "!pip install langchain\n",
        "!pip install sentence-transformers\n",
        "!pip install faiss-cpu\n",
        "!pip install bs4"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pyuCvDLvWJoV",
        "outputId": "5b0111b3-c4a2-4a87-d4f8-9d9d361b79bb"
      },
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting git+https://github.com/langchain-ai/langchain.git#subdirectory=libs/community\n",
            "  Cloning https://github.com/langchain-ai/langchain.git to /tmp/pip-req-build-06avv7dq\n",
            "  Running command git clone --filter=blob:none --quiet https://github.com/langchain-ai/langchain.git /tmp/pip-req-build-06avv7dq\n",
            "  Resolved https://github.com/langchain-ai/langchain.git to commit 9111d3a6369da71eb4c78d69bb20d20d00475d9a\n",
            "  Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
            "  Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n",
            "  Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
            "Requirement already satisfied: PyYAML>=5.3 in /usr/local/lib/python3.10/dist-packages (from langchain-community==0.0.34) (6.0.1)\n",
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            "Collecting dataclasses-json<0.7,>=0.5.7 (from langchain-community==0.0.34)\n",
            "  Downloading dataclasses_json-0.6.4-py3-none-any.whl (28 kB)\n",
            "Collecting langchain-core<0.2.0,>=0.1.45 (from langchain-community==0.0.34)\n",
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            "\u001b[?25hCollecting langsmith<0.2.0,>=0.1.0 (from langchain-community==0.0.34)\n",
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            "Collecting marshmallow<4.0.0,>=3.18.0 (from dataclasses-json<0.7,>=0.5.7->langchain-community==0.0.34)\n",
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            "\u001b[?25hCollecting typing-inspect<1,>=0.4.0 (from dataclasses-json<0.7,>=0.5.7->langchain-community==0.0.34)\n",
            "  Downloading typing_inspect-0.9.0-py3-none-any.whl (8.8 kB)\n",
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            "  Downloading jsonpatch-1.33-py2.py3-none-any.whl (12 kB)\n",
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            "Collecting orjson<4.0.0,>=3.9.14 (from langsmith<0.2.0,>=0.1.0->langchain-community==0.0.34)\n",
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            "Collecting jsonpointer>=1.9 (from jsonpatch<2.0,>=1.33->langchain-core<0.2.0,>=0.1.45->langchain-community==0.0.34)\n",
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            "Collecting mypy-extensions>=0.3.0 (from typing-inspect<1,>=0.4.0->dataclasses-json<0.7,>=0.5.7->langchain-community==0.0.34)\n",
            "  Downloading mypy_extensions-1.0.0-py3-none-any.whl (4.7 kB)\n",
            "Building wheels for collected packages: langchain-community\n",
            "  Building wheel for langchain-community (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for langchain-community: filename=langchain_community-0.0.34-py3-none-any.whl size=1940119 sha256=5b8995827c4550d5222a67ec6f7a709c8b1ca67bc0ca79d76cdbd4adbabdbe6c\n",
            "  Stored in directory: /tmp/pip-ephem-wheel-cache-1g9i6a0j/wheels/51/b6/e9/993f6e112cfeb4196235a0fb8cca5d64cc5602606897481e64\n",
            "Successfully built langchain-community\n",
            "Installing collected packages: packaging, orjson, mypy-extensions, jsonpointer, typing-inspect, marshmallow, jsonpatch, langsmith, dataclasses-json, langchain-core, langchain-community\n",
            "  Attempting uninstall: packaging\n",
            "    Found existing installation: packaging 24.0\n",
            "    Uninstalling packaging-24.0:\n",
            "      Successfully uninstalled packaging-24.0\n",
            "Successfully installed dataclasses-json-0.6.4 jsonpatch-1.33 jsonpointer-2.4 langchain-community-0.0.34 langchain-core-0.1.45 langsmith-0.1.50 marshmallow-3.21.1 mypy-extensions-1.0.0 orjson-3.10.1 packaging-23.2 typing-inspect-0.9.0\n",
            "Collecting langchain\n",
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            "Installing collected packages: langchain-text-splitters, langchain\n",
            "Successfully installed langchain-0.1.16 langchain-text-splitters-0.0.1\n",
            "Collecting sentence-transformers\n",
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            "Successfully installed bs4-0.0.2\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from langchain_community.embeddings import HuggingFaceEmbeddings\n",
        "from langchain_community.vectorstores import FAISS\n",
        "from langchain.text_splitter import RecursiveCharacterTextSplitter\n",
        "from langchain_community.document_loaders import WebBaseLoader\n",
        "import bs4\n",
        "\n",
        "# Step 1: Load the document from a web url\n",
        "loader = WebBaseLoader([\"https://huggingface.co/blog/llama3\"])\n",
        "documents = loader.load()\n",
        "\n",
        "# Step 2: Split the document into chunks with a specified chunk size\n",
        "text_splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=50)\n",
        "all_splits = text_splitter.split_documents(documents)\n",
        "\n",
        "# Step 3: Store the document into a vector store with a specific embedding model\n",
        "vectorstore = FAISS.from_documents(all_splits, HuggingFaceEmbeddings(model_name=\"sentence-transformers/all-mpnet-base-v2\"))\n"
      ],
      "metadata": {
        "id": "rmQt-NREWXW_",
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        },
        "outputId": "3fcaede3-d3ea-4c31-cdf2-c90274bc98c4"
      },
      "execution_count": 58,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:88: UserWarning: \n",
            "The secret `HF_TOKEN` does not exist in your Colab secrets.\n",
            "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n",
            "You will be able to reuse this secret in all of your notebooks.\n",
            "Please note that authentication is recommended but still optional to access public models or datasets.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "display_data",
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          "metadata": {}
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          "metadata": {}
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          "metadata": {}
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          },
          "metadata": {}
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            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from langchain_community.llms import Replicate\n",
        "llm = Replicate(\n",
        "    model=\"meta/meta-llama-3-8b-instruct\",\n",
        "    model_kwargs={\"temperature\": 0.0, \"top_p\": 1, \"max_new_tokens\":500}\n",
        ")\n",
        "\n",
        "from langchain.chains import ConversationalRetrievalChain\n",
        "chain = ConversationalRetrievalChain.from_llm(llm,\n",
        "                                              vectorstore.as_retriever(),\n",
        "                                              return_source_documents=True)\n",
        "\n",
        "result = chain({\"question\": \"What’s new with Llama 3?\", \"chat_history\": []})\n",
        "md(result['answer'])\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 118
        },
        "id": "MQkn4wWSWyER",
        "outputId": "ac7789a2-5a1e-4755-e5c8-2881afcf5ef4"
      },
      "execution_count": 59,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/langchain_core/_api/deprecation.py:119: LangChainDeprecationWarning: The method `Chain.__call__` was deprecated in langchain 0.1.0 and will be removed in 0.2.0. Use invoke instead.\n",
            "  warn_deprecated(\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Markdown object>"
            ],
            "text/markdown": "According to the text, the main new feature of Llama 3 is the use of a new tokenizer that expands the vocabulary size to 128,256 (from 32K tokens in the previous version). This larger vocabulary can encode text more efficiently (both for input and output) and potentially yield stronger multilingualism."
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "There's a new way of building advanced RAG by using LangChain Expresssion Language. See [here](https://python.langchain.com/docs/use_cases/question_answering/quickstart/) for more info."
      ],
      "metadata": {
        "id": "lzbWecYQd9VY"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TEvefAWIJONx"
      },
      "source": [
        "## **5 - Fine-Tuning Models**\n",
        "\n",
        "* Limitations of Prompt Eng and RAG\n",
        "* Fine-Tuning\n",
        "* Types (PEFT, LoRA, QLoRA)\n",
        "* Evals + Quality\n",
        "\n",
        "* PyTorch for Pre-Training & Fine-Tuning\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 60,
      "metadata": {
        "id": "0a9CvJ8YcTzV",
        "outputId": "bfd25be2-c186-4f62-b048-279db38cb6bc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 78
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<img src=\"https://mermaid.ink/img/CiAgZ3JhcGggTFI7CiAgICAgIEN1c3RvbV9EYXRhc2V0IC0tPiBQcmUtdHJhaW5lZF9MbGFtYQogICAgICBQcmUtdHJhaW5lZF9MbGFtYSAtLT4gRmluZS10dW5lZF9MbGFtYQogICAgICBGaW5lLXR1bmVkX0xsYW1hIC0tPiBSTEhGCiAgICAgIFJMSEYgLS0+IHxMb3NzOkNyb3NzLUVudHJvcHl8RmluZS10dW5lZF9MbGFtYQogICAgICBjbGFzc0RlZiBkZWZhdWx0IGZpbGw6I0NDRTZGRixzdHJva2U6Izg0QkNGNSx0ZXh0Q29sb3I6IzFDMkIzMyxmb250RmFtaWx5OnRyZWJ1Y2hldCBtczsKICA=\"/>"
            ],
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {}
        }
      ],
      "source": [
        "fine_tuned_arch()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tXSwZziL3bE0"
      },
      "source": [
        "### **5.1 - Types Of Fine-Tuning**\n",
        "\n",
        "1. Full Parameter Fine-Tuning - Best performance, resource intensive and time consuming\n",
        "2. PEFT (Parameter Efficient Fine-Tuning)\n",
        "\n",
        "  2.1 - LoRA (Low Rank Adaptation)\n",
        "\n",
        "  2.2 - QLoRA (Quantized LoRA)\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GozTWQwi7f4d"
      },
      "source": [
        "### **5.2 - Examples of Fine-Tuning**\n",
        "\n",
        "1. [Meta Llama Recipes](https://github.com/meta-llama/llama-recipes/tree/main/recipes/finetuning)\n",
        "2. [Hugging Face fine-tuning with Llama 3](https://huggingface.co/blog/llama3#fine-tuning-with-%F0%9F%A4%97-trl)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_8lcgdZa8onC"
      },
      "source": [
        "## **6 - Responsible AI**\n",
        "\n",
        "* Power + Responsibility\n",
        "* Hallucination\n",
        "* Input & Output Safety\n",
        "* Red-teaming (simulating real-world cyber attackers)\n",
        "* Review [Responsible Use Guide](https://ai.meta.com/llama/responsible-use-guide/)\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pbqb006R-T_k"
      },
      "source": [
        "##**7 - Conclusion**\n",
        "* Active research on LLMs and Llama\n",
        "* Leverage the power of Llama and its open community (Github - 45k stars, 8k forks)\n",
        "* Call-To-Action\n",
        "  * Build a project using Llama!\n",
        "  * Interested in contributing to Llama?\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gSz5dTMxp7xo"
      },
      "source": [
        "#### **Resources**\n",
        "* [Meta Llama 3 Blog](https://ai.meta.com/blog/meta-llama-3/)\n",
        "* [Getting Started with Meta Llama](https://llama.meta.com/docs/get-started)\n",
        "- [Llama 3 repo](https://github.com/meta-llama/llama3)\n",
        "- [Llama 3 model card](https://github.com/meta-llama/llama3/blob/main/MODEL_CARD.md)\n",
        "- [LLama 3 Recipes repo](https://github.com/meta-llama/llama-recipes)\n",
        "- [Responsible Use Guide](https://ai.meta.com/llama/responsible-use-guide/)\n",
        "- [Acceptable Use Policy](https://ai.meta.com/llama/use-policy/)\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "V7aI6fhZp-KC"
      },
      "source": [
        "#### **Author** - Amit Sangani, Director, AI Partner Engineering @Meta\n",
        "\n",
        "1. LinkedIn - https://www.linkedin.com/in/amitsangani\n",
        "\n",
        "![bit.ly_amit-li.png](data:image/png;base64,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)\n",
        "\n",
        "2. Github - https://github.com/amitsangani\n",
        "\n",
        "![bit.ly_amit-github.png](data:image/png;base64,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