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Merge pull request #267 from Adriana394/week3-exercise

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Create synthetic_dataset_generator_deepseek_qwen_llama.ipynb
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Ed Donner
2025-03-22 07:12:16 -04:00
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{
"cells": [
{
"cell_type": "markdown",
"id": "843542f7-220a-4408-9f8a-848696092434",
"metadata": {
"id": "843542f7-220a-4408-9f8a-848696092434"
},
"source": [
"# Build a Model to generate Synthetic Data"
]
},
{
"cell_type": "markdown",
"id": "a8816fc8-9517-46ff-af27-9fd0060840aa",
"metadata": {},
"source": [
"Code was written in Google Colab. "
]
},
{
"cell_type": "markdown",
"id": "08a8d539-950b-4b58-abf4-f17bd832c0af",
"metadata": {
"id": "08a8d539-950b-4b58-abf4-f17bd832c0af"
},
"source": [
"## Imports"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "Ienu-NHTuUlT",
"metadata": {
"id": "Ienu-NHTuUlT"
},
"outputs": [],
"source": [
"!pip install -q gradio"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "c5e737cd-27b0-4a2e-9a0c-dbb30ce5cdbf",
"metadata": {
"id": "c5e737cd-27b0-4a2e-9a0c-dbb30ce5cdbf"
},
"outputs": [],
"source": [
"import os\n",
"import requests\n",
"import json\n",
"from google.colab import userdata\n",
"\n",
"from huggingface_hub import login\n",
"from transformers import AutoTokenizer, AutoModelForCausalLM, TextStreamer, BitsAndBytesConfig\n",
"import torch\n",
"\n",
"import gradio as gr"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "khD9X5-V_txO",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "khD9X5-V_txO",
"outputId": "e2b8d8d0-0433-4b5f-c777-a675213a3f4c"
},
"outputs": [],
"source": [
"!pip install -U bitsandbytes"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "e47ead5f-b4e9-4e9f-acf9-be1ffb7fa6d7",
"metadata": {
"id": "e47ead5f-b4e9-4e9f-acf9-be1ffb7fa6d7"
},
"outputs": [],
"source": [
"hf_token = userdata.get('HF_TOKEN')"
]
},
{
"cell_type": "markdown",
"id": "ba104a9c-f298-4e90-9ceb-9d907e392d0d",
"metadata": {
"id": "ba104a9c-f298-4e90-9ceb-9d907e392d0d"
},
"source": [
"## Open Source Models from HF"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "11b1eb65-8ef5-4e6d-9176-cf1f70d07fb6",
"metadata": {
"id": "11b1eb65-8ef5-4e6d-9176-cf1f70d07fb6"
},
"outputs": [],
"source": [
"deepseek_model = 'deepseek-ai/deepseek-llm-7b-chat'\n",
"llama_model = 'meta-llama/Meta-Llama-3.1-8B-Instruct'\n",
"qwen2 = 'Qwen/Qwen2-7B-Instruct'"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "90fb1d2e-5d25-4d73-b629-8273ab71503c",
"metadata": {
"id": "90fb1d2e-5d25-4d73-b629-8273ab71503c"
},
"outputs": [],
"source": [
"login(hf_token, add_to_git_credential=True)"
]
},
{
"cell_type": "markdown",
"id": "52948c01-8dc6-404b-a2c1-c87f9f6dbd64",
"metadata": {
"id": "52948c01-8dc6-404b-a2c1-c87f9f6dbd64"
},
"source": [
"## Creating Prompts"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "79374337-34fe-4002-b173-ac9b132a54d8",
"metadata": {
"id": "79374337-34fe-4002-b173-ac9b132a54d8"
},
"outputs": [],
"source": [
"system_prompt = \"You are an expert in generating synthetic datasets. Your goal is to generate realistic datasets \\\n",
"based on a given business and its requirements from the user. You will also be given the desired datset format.\"\n",
"system_prompt += \"Do not repeat the instructions.\"\n",
"\n",
"user_prompt = (\"Please provide me a dataset for the following business.\"\n",
"\"For example:\\n\"\n",
"\"The Business: A retail store selling luxury watches.\\n\"\n",
"\"The Data Format: CSV.\\n\"\n",
"\"Output:\\n\"\n",
"\"Item,Price,Quantity,Brand,Sale Date\\n\"\n",
"\"Superocean II, 20.000$, 3, Breitling, 2025-04-08 \\n\"\n",
"\"If I don't provide you the necessary columns, please create the columns based on your knowledge about the given business\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "dcd90b5e-a7d2-4cdc-81ff-17974c5ff1fe",
"metadata": {
"id": "dcd90b5e-a7d2-4cdc-81ff-17974c5ff1fe"
},
"outputs": [],
"source": [
"def dataset_format(data_format, num_records):\n",
" format_message = ''\n",
" if data_format == 'CSV':\n",
" format_message = 'Please provide the dataset in a CSV format.'\n",
" elif data_format == 'JSON':\n",
" format_message = 'Please provide the dataset in a JSON format'\n",
" elif data_format == 'Tabular':\n",
" format_message = 'Please provide the dataset in a Tabular format'\n",
"\n",
" return format_message + f'Please generate {num_records} records'"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "39243edb-3eba-46fd-a610-e474ed421b01",
"metadata": {
"id": "39243edb-3eba-46fd-a610-e474ed421b01"
},
"outputs": [],
"source": [
"def complete_user_prompt(user_input, data_format, num_records):\n",
" messages = [\n",
" {'role': 'system', 'content': system_prompt},\n",
" {'role': 'user', 'content': user_input + user_prompt + dataset_format(data_format, num_records)}\n",
" ]\n",
"\n",
" return messages"
]
},
{
"cell_type": "markdown",
"id": "1ac81127-b9cc-424b-8b38-8a8b09bcc226",
"metadata": {
"id": "1ac81127-b9cc-424b-8b38-8a8b09bcc226"
},
"source": [
"## Accessing the Models"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "cc4aaab5-bde1-463b-b873-e8bd1a231dc1",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "cc4aaab5-bde1-463b-b873-e8bd1a231dc1",
"outputId": "16c9420d-2c4a-4e57-f281-7c531b5145db"
},
"outputs": [],
"source": [
"print(\"CUDA available:\", torch.cuda.is_available())\n",
"if torch.cuda.is_available():\n",
" print(\"GPU-Device:\", torch.cuda.get_device_name(torch.cuda.current_device()))\n",
"else:\n",
" print(\"No GPU found.\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "6b8e648d-747f-4684-a20b-b8da550efc23",
"metadata": {
"id": "6b8e648d-747f-4684-a20b-b8da550efc23"
},
"outputs": [],
"source": [
"quant_config = BitsAndBytesConfig(\n",
" load_in_4bit = True,\n",
" bnb_4bit_use_double_quant = False,\n",
" bnb_4bit_compute_dtype= torch.bfloat16,\n",
" bnb_4bit_quant_type= 'nf4'\n",
")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "b3ae602f-0abf-420d-8c7b-1938cba92528",
"metadata": {
"id": "b3ae602f-0abf-420d-8c7b-1938cba92528"
},
"outputs": [],
"source": [
"def generate_model(model_id, messages):\n",
" try:\n",
" tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code = True)\n",
" inputs = tokenizer.apply_chat_template(messages, return_tensors = 'pt').to('cuda')\n",
" streamer = TextStreamer(tokenizer)\n",
" model = AutoModelForCausalLM.from_pretrained(model_id, device_map = 'auto', quantization_config = quant_config)\n",
" outputs = model.generate(inputs, max_new_tokens = 2000, streamer = streamer)\n",
" generated_text = tokenizer.decode(outputs[0], skip_special_tokens = True)\n",
" del tokenizer, streamer, model, inputs, outputs\n",
" return generated_text\n",
"\n",
" except Exception as e:\n",
" return f'Error during generation: {str(e)}'"
]
},
{
"cell_type": "markdown",
"id": "7c575c9e-4674-4eee-a9b9-c8d14ceed474",
"metadata": {
"id": "7c575c9e-4674-4eee-a9b9-c8d14ceed474"
},
"source": [
"## Generate Dataset"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "d9c5963e-9f4e-4990-b744-b9ead03e623a",
"metadata": {
"id": "d9c5963e-9f4e-4990-b744-b9ead03e623a"
},
"outputs": [],
"source": [
"def generate_dataset(user_input, target_format, model_choice, num_records):\n",
" if model_choice == 'DeepSeek':\n",
" model_id = deepseek_model\n",
" elif model_choice == 'Llama-3.1-8B':\n",
" model_id = llama_model\n",
" elif model_choice == 'Qwen2':\n",
" model_id = qwen2\n",
"\n",
" messages = complete_user_prompt(user_input, target_format, num_records)\n",
" return generate_model(model_id, messages)"
]
},
{
"cell_type": "markdown",
"id": "ff574cfe-567f-4c6d-b944-fb756bf7ebca",
"metadata": {
"id": "ff574cfe-567f-4c6d-b944-fb756bf7ebca"
},
"source": [
"## Creating Gradio UI"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "61d2b056-0d00-4b73-b083-024a8f374fef",
"metadata": {
"id": "61d2b056-0d00-4b73-b083-024a8f374fef"
},
"outputs": [],
"source": [
"with gr.Blocks(title = 'Synthetic Data Generator') as ui:\n",
" gr.Markdown('# Synthetic Data Generator')\n",
"\n",
" with gr.Row():\n",
" with gr.Column(min_width=600):\n",
" user_inputs = gr.Textbox(label = 'Enter your Business details and data requirements',\n",
" placeholder = 'Type here...', lines = 15)\n",
"\n",
" model_choice = gr.Dropdown(\n",
" ['DeepSeek', 'Llama-3.1-8B', 'Qwen2'],\n",
" label = 'Choose your Model',\n",
" value = 'DeepSeek'\n",
" )\n",
"\n",
" target_format = gr.Dropdown(\n",
" ['CSV', 'JSON', 'Tabular'],\n",
" label = 'Choose your Format',\n",
" value = 'CSV'\n",
" )\n",
" num_records = gr.Dropdown(\n",
" [50, 100, 150, 200],\n",
" label = 'Number of Records',\n",
" value = 50\n",
" )\n",
"\n",
" generate_button = gr.Button('Generate')\n",
"\n",
" with gr.Column():\n",
" output = gr.Textbox(label = 'Generated Synthetic Data',\n",
" lines = 30)\n",
"\n",
" generate_button.click(fn = generate_dataset, inputs = [user_inputs, target_format, model_choice, num_records],\n",
" outputs = output\n",
" )"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "958d9cbf-50ff-4c50-a305-18df6d5f5eda",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 626
},
"id": "958d9cbf-50ff-4c50-a305-18df6d5f5eda",
"outputId": "a6736641-85c3-4b6a-a28d-02ac5caf4562",
"scrolled": true
},
"outputs": [],
"source": [
"ui.launch(inbrowser = True)"
]
}
],
"metadata": {
"accelerator": "GPU",
"colab": {
"gpuType": "T4",
"provenance": []
},
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.11"
}
},
"nbformat": 4,
"nbformat_minor": 5
}