1  {
  2    "cells": [
  3      {
  4        "cell_type": "markdown",
  5        "id": "930bc11c",
  6        "metadata": {
  7          "id": "930bc11c"
  8        },
  9        "source": [
 10          "# Meta Synthetic Data Generator (LLaMA 3.2 - 3B)\n",
 11          "\n",
 12          "This notebook demonstrates how to use Meta's LLaMA 3.2 3B model to generate synthetic data for use in AI training or application prototyping."
 13        ]
 14      },
 15      {
 16        "cell_type": "markdown",
 17        "source": [
 18          "[![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/DhivyaBharathy-web/PraisonAI/blob/main/examples/cookbooks/Meta_LLaMA3_SyntheticData.ipynb)\n"
 19        ],
 20        "metadata": {
 21          "id": "KPi7FpbV9J2d"
 22        },
 23        "id": "KPi7FpbV9J2d"
 24      },
 25      {
 26        "cell_type": "markdown",
 27        "id": "80f68ecf",
 28        "metadata": {
 29          "id": "80f68ecf"
 30        },
 31        "source": [
 32          "## Dependencies"
 33        ]
 34      },
 35      {
 36        "cell_type": "code",
 37        "execution_count": null,
 38        "id": "7eeb508d",
 39        "metadata": {
 40          "id": "7eeb508d"
 41        },
 42        "outputs": [],
 43        "source": [
 44          "!pip install -q transformers accelerate bitsandbytes"
 45        ]
 46      },
 47      {
 48        "cell_type": "markdown",
 49        "id": "eda75a0c",
 50        "metadata": {
 51          "id": "eda75a0c"
 52        },
 53        "source": [
 54          "## Tools\n",
 55          "* `transformers` for model loading and text generation\n",
 56          "* `pipeline` for simplified inference\n",
 57          "* `AutoTokenizer`, `AutoModelForCausalLM` for LLaMA 3.2"
 58        ]
 59      },
 60      {
 61        "cell_type": "markdown",
 62        "id": "6ee96383",
 63        "metadata": {
 64          "id": "6ee96383"
 65        },
 66        "source": [
 67          "## YAML Prompt"
 68        ]
 69      },
 70      {
 71        "cell_type": "code",
 72        "execution_count": null,
 73        "id": "aa4e56ef",
 74        "metadata": {
 75          "id": "aa4e56ef"
 76        },
 77        "outputs": [],
 78        "source": [
 79          "prompt: |\n",
 80          "  task: \"Generate a customer complaint email\"\n",
 81          "  style: \"Professional\"\n"
 82        ]
 83      },
 84      {
 85        "cell_type": "markdown",
 86        "id": "e9fbe400",
 87        "metadata": {
 88          "id": "e9fbe400"
 89        },
 90        "source": [
 91          "## Main"
 92        ]
 93      },
 94      {
 95        "cell_type": "code",
 96        "execution_count": null,
 97        "id": "3e35336f",
 98        "metadata": {
 99          "id": "3e35336f"
100        },
101        "outputs": [],
102        "source": [
103          "from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline\n",
104          "\n",
105          "# Load tokenizer and model (LLaMA 3.2 - 3B)\n",
106          "model_id = \"meta-llama/Meta-Llama-3-3B-Instruct\"\n",
107          "tokenizer = AutoTokenizer.from_pretrained(model_id)\n",
108          "model = AutoModelForCausalLM.from_pretrained(model_id)\n",
109          "\n",
110          "# Create a simple text generation pipeline\n",
111          "generator = pipeline(\"text-generation\", model=model, tokenizer=tokenizer)\n",
112          "\n",
113          "# Example synthetic data prompt\n",
114          "prompt = \"Create a customer support query about a late delivery.\"\n",
115          "\n",
116          "# Generate synthetic text\n",
117          "output = generator(prompt, max_length=60, do_sample=True)[0]['generated_text']\n",
118          "print(\"📝 Synthetic Output:\", output)"
119        ]
120      },
121      {
122        "cell_type": "markdown",
123        "id": "ed28cc2e",
124        "metadata": {
125          "id": "ed28cc2e"
126        },
127        "source": [
128          "## Output"
129        ]
130      },
131      {
132        "cell_type": "markdown",
133        "id": "6fc8f19e",
134        "metadata": {
135          "id": "6fc8f19e"
136        },
137        "source": [
138          "🖼️ Output Preview (Text Summary):\n",
139          "\n",
140          "Prompt: \"Create a customer support query about a late delivery.\"\n",
141          "\n",
142          "📝 Output: The LLaMA model generates a realistic complaint, such as:\n",
143          "\n",
144          "\"Dear Support Team, I placed an order two weeks ago and have yet to receive it...\"\n",
145          "\n",
146          "🎯 This illustrates how the model can be used to generate realistic synthetic data for tasks like training chatbots or support models.\n"
147        ]
148      }
149    ],
150    "metadata": {
151      "colab": {
152        "provenance": []
153      }
154    },
155    "nbformat": 4,
156    "nbformat_minor": 5
157  }