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minor update on local machine

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Ransford Okpoti
2025-10-27 11:42:28 +00:00
parent 7ad790be3d
commit e8dc9bd396
1 changed files with 119 additions and 136 deletions
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week3/community-contributions/ranskills-week3-coherent-data-generator.ipynb
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@@ -1,49 +1,30 @@
{
"nbformat": 4,
"nbformat_minor": 0,
"metadata": {
"colab": {
"provenance": [],
"collapsed_sections": [
"tqSpfJGnme7y"
],
"gpuType": "T4"
},
"kernelspec": {
"name": "python3",
"display_name": "Python 3"
},
"language_info": {
"name": "python"
},
"accelerator": "GPU"
},
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "KbMea_UrO3Ke"
},
"source": [
"# ✨ Coherent Data Generator\n",
"\n",
"## In real life, data has meaning, relationships, etc. and this is where this tool shines.\n",
"## In real life, data has meaning, relationships, etc., and this is where this tool shines.\n",
"\n",
"Dependencies between fields are detected and a coherent data is generated.\n",
"Dependencies between fields are detected, and coherent data is generated.\n",
"Example:\n",
"When asked to generate data with **Ghana** cited as the context, fields like `name`, `food`, etc. will be Ghanaian. Fields such as phone number will have the appropriate prefix of `+233`, etc.\n",
"When asked to generate data with **Ghana** cited as the context, fields like `name`, `food`, etc., will be Ghanaian. Fields such as phone number will have the appropriate prefix of `+233`, etc.\n",
"\n",
"This is better than Faker.\n",
"\n",
"## Steps\n",
"Schema -> Generate Data\n",
"\n",
"Schema Sources:\n",
"Schema Sources: \n",
"- Use the guided schema builder\n",
"- Bring your own schema from an SQL Data Definition Language (DDL)\n",
"- Prompting\n",
"- Providing a domain to an old-hat to definition features for a dataset"
],
"metadata": {
"id": "KbMea_UrO3Ke"
}
"- Providing a domain to an old hat to define features for a dataset"
]
},
{
"cell_type": "code",
@@ -65,6 +46,11 @@
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "DOBBN3P2GD2O"
},
"outputs": [],
"source": [
"model_id = \"Qwen/Qwen3-4B-Instruct-2507\"\n",
"\n",
@@ -78,24 +64,24 @@
" dtype=\"auto\",\n",
" device_map=\"auto\"\n",
")"
],
"metadata": {
"id": "DOBBN3P2GD2O"
},
"execution_count": null,
"outputs": []
]
},
{
"cell_type": "markdown",
"source": [
"## Schema Definitions"
],
"metadata": {
"id": "HSUebXa1O3MM"
}
},
"source": [
"## Schema Definitions"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "5LNM76OQjAw6"
},
"outputs": [],
"source": [
"# This is for future use where errors in SQL DDL statements can be fixed if the\n",
"# specifies that from the UI\n",
@@ -115,33 +101,33 @@
"class Schema(BaseModel):\n",
" name: str = Field(..., description='Name of the schema')\n",
" fields: list[FieldDescriptor] = Field(..., description='List of fields in the schema')"
],
"metadata": {
"id": "5LNM76OQjAw6"
},
"execution_count": null,
"outputs": []
]
},
{
"cell_type": "markdown",
"source": [
"## LLM Interactions"
],
"metadata": {
"id": "6QjitfTBPa1E"
}
},
"source": [
"## LLM Interactions"
]
},
{
"cell_type": "markdown",
"source": [
"### Generate Content from LLM"
],
"metadata": {
"id": "dXiRHok7Peir"
}
},
"source": [
"### Generate Content from LLM"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "daTUVG8_PmvM"
},
"outputs": [],
"source": [
"def generate(messages: list[dict[str, str]], temperature: float = 0.1) -> any:\n",
" text = tokenizer.apply_chat_template(\n",
@@ -161,24 +147,24 @@
" content = tokenizer.decode(output_ids, skip_special_tokens=True)\n",
"\n",
" return content"
],
"metadata": {
"id": "daTUVG8_PmvM"
},
"execution_count": null,
"outputs": []
]
},
{
"cell_type": "markdown",
"source": [
"### Generate Data Given A Valid Schema"
],
"metadata": {
"id": "sBHJKn8qQhM5"
}
},
"source": [
"### Generate Data Given A Valid Schema"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "Fla8UQf4Qm5l"
},
"outputs": [],
"source": [
"def generate_data(schema: str, context: str = '', num_records: int = 5):\n",
" system_prompt = f'''\n",
@@ -211,24 +197,24 @@
" ]\n",
"\n",
" return generate(messages)"
],
"metadata": {
"id": "Fla8UQf4Qm5l"
},
"execution_count": null,
"outputs": []
]
},
{
"cell_type": "markdown",
"source": [
"### SQL"
],
"metadata": {
"id": "izrClU6VPsZp"
}
},
"source": [
"### SQL"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "aQgY6EK0QPPd"
},
"outputs": [],
"source": [
"def sql_validator(ddl: str):\n",
" system_prompt = '''\n",
@@ -267,26 +253,26 @@
" ]\n",
"\n",
" return generate(messages)"
],
"metadata": {
"id": "aQgY6EK0QPPd"
},
"execution_count": null,
"outputs": []
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "4mgwDQyDQ1wv"
},
"source": [
"### Data Scientist\n",
"\n",
"Just give it a domain and you will be amazed the features will give you."
],
"metadata": {
"id": "4mgwDQyDQ1wv"
}
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "P36AMvBq8AST"
},
"outputs": [],
"source": [
"def create_domain_schema(domain: str):\n",
" system_prompt = f'''\n",
@@ -326,35 +312,35 @@
" created_at TIMESTAMP DEFAULT NOW()\n",
");\n",
"'''"
],
"metadata": {
"id": "P36AMvBq8AST"
},
"execution_count": null,
"outputs": []
]
},
{
"cell_type": "code",
"source": [
"print(f'{model.get_memory_footprint() / 1e9:, .2f} GB')"
],
"execution_count": null,
"metadata": {
"id": "QuVyHOhjDtSH"
},
"execution_count": null,
"outputs": []
"outputs": [],
"source": [
"print(f'{model.get_memory_footprint() / 1e9:, .2f} GB')"
]
},
{
"cell_type": "markdown",
"source": [
"## Export Functions"
],
"metadata": {
"id": "tqSpfJGnme7y"
}
},
"source": [
"## Export Functions"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "pAu5OPfUmMSm"
},
"outputs": [],
"source": [
"from enum import StrEnum\n",
"\n",
@@ -451,24 +437,28 @@
" tmp.write(content)\n",
" tmp.flush()\n",
" return tmp.name"
],
"metadata": {
"id": "pAu5OPfUmMSm"
},
"execution_count": null,
"outputs": []
]
},
{
"cell_type": "markdown",
"source": [
"## Gradio UI"
],
"metadata": {
"id": "Q0fZsCuso_YZ"
}
},
"source": [
"## Gradio UI"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "TJYUWecybDpP",
"outputId": "e82d0a13-3ca3-4a01-d45c-78fc94ade9bc"
},
"outputs": [],
"source": [
"import gradio as gr\n",
"from pydantic import BaseModel, Field\n",
@@ -718,34 +708,27 @@
" )\n",
"\n",
"\n",
"ui.launch(debug=True)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "TJYUWecybDpP",
"outputId": "e82d0a13-3ca3-4a01-d45c-78fc94ade9bc"
},
"execution_count": 10,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Keyboard interruption in main thread... closing server.\n",
"Killing tunnel 127.0.0.1:7860 <> https://5954eb89d994d7a5ee.gradio.live\n"
]
},
{
"output_type": "execute_result",
"data": {
"text/plain": []
},
"metadata": {},
"execution_count": 10
}
"ui.launch(debug=True)\n"
]
}
]
],
"metadata": {
"accelerator": "GPU",
"colab": {
"collapsed_sections": [
"tqSpfJGnme7y"
],
"gpuType": "T4",
"provenance": []
},
"kernelspec": {
"display_name": "Python 3",
"name": "python3"
},
"language_info": {
"name": "python"
}
},
"nbformat": 4,
"nbformat_minor": 0
}