1.Getting_to_know_Llama.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "LERqQn5v8-ak"
   },
   "source": [
    "# **Getting to know Llama 3.1\n",
    "This jupyter notebook provide a guided tour of Llama 3.1  including understanding different Llama 3.1 models, how and where to access them, Generative AI and Chatbot architectures, prompt engineering, RAG (Retrieval Augmented Generation), Fine-tuning and more. All this is implemented with a starter code."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING: Ignoring invalid distribution -aiss-cpu (c:\\users\\meenakshi.h\\anaconda3\\envs\\python39\\lib\\site-packages)\n",
      "WARNING: Ignoring invalid distribution -aiss-cpu (c:\\users\\meenakshi.h\\anaconda3\\envs\\python39\\lib\\site-packages)\n",
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     ]
    }
   ],
   "source": [
    "!pip install matplotlib ipywidgets"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Let us print the Llam3.1 family"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import base64\n",
    "from IPython.display import Image, display\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# presentation layer code\n",
    "\n",
    "def PLOT(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))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "executionInfo": {
     "elapsed": 248,
     "status": "ok",
     "timestamp": 1695832228254,
     "user": {
      "displayName": "Amit Sangani",
      "userId": "11552178012079240149"
     },
     "user_tz": 420
    },
    "id": "ktEA7qXmwdUM"
   },
   "outputs": [],
   "source": [
    "\n",
    "def genai_app_arch():\n",
    "  PLOT(\"\"\"\n",
    "  flowchart TD\n",
    "    A[Users] --> B(Applications e.g. mobile, web)\n",
    "    B --> |Hosted API|C(Platforms e.g. Custom, HuggingFace, Replicate)\n",
    "    B -- optional --> E(Frameworks e.g. LangChain)\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",
    "  PLOT(\"\"\"\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 llama2_family():\n",
    "  PLOT(\"\"\"\n",
    "  graph LR;\n",
    "      llama-2 --> llama-2-7b\n",
    "      llama-2 --> llama-2-13b\n",
    "      llama-2 --> llama-2-70b\n",
    "      llama-2-7b --> llama-2-7b-chat\n",
    "      llama-2-13b --> llama-2-13b-chat\n",
    "      llama-2-70b --> llama-2-70b-chat\n",
    "      classDef default fill:#CCE6FF,stroke:#84BCF5,textColor:#1C2B33,fontFamily:trebuchet ms;\n",
    "  \"\"\")\n",
    "\n",
    "def llama3_family():\n",
    "  PLOT(\"\"\"\n",
    "  graph LR;\n",
    "      llama-3 --> llama-3-8b\n",
    "      llama-3 --> llama-3-70b\n",
    "      llama-3-8b --> llama-3-8b\n",
    "      llama-3-8b --> llama-3-8b-instruct\n",
    "      llama-3-70b --> llama-3-70b\n",
    "      llama-3-70b --> llama-3-70b-instruct\n",
    "      classDef default fill:#CCE6FF,stroke:#84BCF5,textColor:#1C2B33,fontFamily:trebuchet ms;\n",
    "  \"\"\")\n",
    "  \n",
    "def llama3_1_family():\n",
    "  PLOT(\"\"\"\n",
    "  graph LR;\n",
    "      llama-3-1 --> llama-3-8b\n",
    "      llama-3-1 --> llama-3-70b\n",
    "      llama-3-1 --> llama-3-4050b\n",
    "      llama-3-1-8b --> llama-3-1-8b\n",
    "      llama-3-1-8b --> llama-3-1-8b-instruct\n",
    "      llama-3-1-70b --> llama-3-1-70b\n",
    "      llama-3-1-70b --> llama-3-1-70b-instruct\n",
    "      llama-3-1-405b --> llama-3-1-405b-instruct\n",
    "      classDef default fill:#CCE6FF,stroke:#84BCF5,textColor:#1C2B33,fontFamily:trebuchet ms;\n",
    "  \"\"\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "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",
    "  PLOT(\"\"\"\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",
    "  PLOT(\"\"\"\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",
    "  PLOT(\"\"\"\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",
    "  PLOT(\"\"\"\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 3.1**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 240
    },
    "executionInfo": {
     "elapsed": 248,
     "status": "ok",
     "timestamp": 1695832233087,
     "user": {
      "displayName": "Amit Sangani",
      "userId": "11552178012079240149"
     },
     "user_tz": 420
    },
    "id": "OXRCC7wexZXd",
    "outputId": "1feb1918-df4b-4cec-d09e-ffe55c12090b"
   },
   "outputs": [
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     "metadata": {},
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   ],
   "source": [
    "llama2_family()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
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   "source": [
    "llama3_family()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
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   ],
   "source": [
    "llama3_1_family()"
   ]
  }
 ],
 "metadata": {
  "colab": {
   "collapsed_sections": [
    "ioVMNcTesSEk"
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   "machine_shape": "hm",
   "provenance": [],
   "toc_visible": true
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