5182 lines
156 KiB
Plaintext
5182 lines
156 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "s7zEKhq9h6eb"
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},
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"source": [
|
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"# Resume and Job Description Data Generator"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "cH3arpfLiAWR"
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},
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"source": [
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"The purpose of this notebook is to generate resumes and job description data for training a model that will assist job applicants finetune their resumes to an advertised role; we will use an open source model and create a ui with gradio"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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||
"colab": {
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"base_uri": "https://localhost:8080/"
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},
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||
"executionInfo": {
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||
"elapsed": 9567,
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||
"status": "ok",
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||
"timestamp": 1761736938504,
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||
"user": {
|
||
"displayName": "Kwabena Baah-Boakye",
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||
"userId": "14758998715370101460"
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||
},
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"user_tz": 240
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},
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||
"id": "p2cGxlB2j5r5",
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||
"outputId": "a025aa62-d896-45e8-b3bd-6e615cedc455"
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},
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"outputs": [],
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"source": [
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"!pip install -q transformers accelerate bitsandbytes torch gradio"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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||
"metadata": {
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||
"executionInfo": {
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"elapsed": 24516,
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"status": "ok",
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"timestamp": 1761736965621,
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"user": {
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"displayName": "Kwabena Baah-Boakye",
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"userId": "14758998715370101460"
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},
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"user_tz": 240
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},
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"id": "Hhqp912jj_Fb"
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},
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"outputs": [],
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"source": [
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"# imports\n",
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"\n",
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"import os\n",
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"import requests\n",
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"from huggingface_hub import login\n",
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"from google.colab import userdata\n",
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"from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig, pipeline\n",
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"import torch\n",
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"import json\n",
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"import pandas as pd\n",
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"import gradio as gr"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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||
"metadata": {
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||
"executionInfo": {
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"elapsed": 688,
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"status": "ok",
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"timestamp": 1761736970821,
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"user": {
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"displayName": "Kwabena Baah-Boakye",
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"userId": "14758998715370101460"
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},
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"user_tz": 240
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},
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"id": "1GlDBWg2kYW9"
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},
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"outputs": [],
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"source": [
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"# Sign in to HuggingFace Hub\n",
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"\n",
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"hf_token = userdata.get('HF_TOKEN')\n",
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"login(hf_token, add_to_git_credential=True)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"executionInfo": {
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"elapsed": 6,
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"status": "ok",
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"timestamp": 1761736993552,
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"user": {
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"displayName": "Kwabena Baah-Boakye",
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"userId": "14758998715370101460"
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},
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"user_tz": 240
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},
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"id": "CF1l129DlaD6"
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},
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"outputs": [],
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"source": [
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"# Available models\n",
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"AVAILABLE_MODELS = {\n",
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" \"Mistral-7B\": \"mistralai/Mistral-7B-Instruct-v0.2\",\n",
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" \"Phi-2\": \"microsoft/phi-2\",\n",
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" \"Gemma-2B\": \"google/gemma-2b-it\",\n",
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" \"TinyLlama\": \"TinyLlama/TinyLlama-1.1B-Chat-v1.0\",\n",
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"}"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"executionInfo": {
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"elapsed": 11,
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"status": "ok",
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"timestamp": 1761737009179,
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"user": {
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"displayName": "Kwabena Baah-Boakye",
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"userId": "14758998715370101460"
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||
},
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"user_tz": 240
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},
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"id": "KdPYdC9tmoEy"
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},
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"outputs": [],
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"source": [
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"# Available industries with roles\n",
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"AVAILABLE_INDUSTRIES = {\n",
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" \"Technology\": [\"Software Engineer\", \"Data Scientist\", \"Product Manager\", \"DevOps Engineer\", \"Frontend Developer\"],\n",
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" \"Healthcare\": [\"Registered Nurse\", \"Medical Assistant\", \"Healthcare Administrator\", \"Pharmacist\", \"Physical Therapist\"],\n",
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" \"Finance\": [\"Financial Analyst\", \"Accountant\", \"Investment Banker\", \"Risk Manager\", \"Portfolio Manager\"],\n",
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" \"Marketing\": [\"Digital Marketing Manager\", \"Content Strategist\", \"SEO Specialist\", \"Brand Manager\", \"Social Media Manager\"],\n",
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" \"Sales\": [\"Account Executive\", \"Sales Manager\", \"Business Development Rep\", \"Sales Engineer\", \"Customer Success Manager\"],\n",
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" \"Education\": [\"Teacher\", \"Curriculum Developer\", \"Academic Advisor\", \"Education Consultant\", \"Training Coordinator\"],\n",
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"}"
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]
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},
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{
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||
"cell_type": "markdown",
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||
"metadata": {
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"id": "spQDmFzOtfn0"
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},
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"source": [
|
||
"## Load Model"
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]
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},
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{
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"cell_type": "code",
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||
"execution_count": null,
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||
"metadata": {
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||
"executionInfo": {
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||
"elapsed": 11,
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||
"status": "ok",
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||
"timestamp": 1761737069184,
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"user": {
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||
"displayName": "Kwabena Baah-Boakye",
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"userId": "14758998715370101460"
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||
},
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"user_tz": 240
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},
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"id": "LBXsJk8Ith7j"
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},
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"outputs": [],
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"source": [
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"\n",
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"# Global variables for model\n",
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"current_model = None\n",
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"current_pipeline = None\n",
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"current_model_name = None\n"
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]
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},
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{
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||
"cell_type": "code",
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||
"execution_count": null,
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||
"metadata": {
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||
"executionInfo": {
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||
"elapsed": 43,
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||
"status": "ok",
|
||
"timestamp": 1761737106153,
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||
"user": {
|
||
"displayName": "Kwabena Baah-Boakye",
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||
"userId": "14758998715370101460"
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||
},
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"user_tz": 240
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},
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||
"id": "he0q6JGCtmRd"
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||
},
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||
"outputs": [],
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||
"source": [
|
||
"def load_model(model_name):\n",
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" \"\"\"Load the selected model\"\"\"\n",
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" global current_model, current_pipeline, current_model_name\n",
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"\n",
|
||
" # If model is already loaded, return\n",
|
||
" if current_model_name == model_name and current_pipeline is not None:\n",
|
||
" return f\"✅ {model_name} already loaded!\"\n",
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"\n",
|
||
" # Clear previous model\n",
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||
" if current_model is not None:\n",
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" del current_model\n",
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" del current_pipeline\n",
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" torch.cuda.empty_cache()\n",
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"\n",
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" print(f\"🤖 Loading {model_name}...\")\n",
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"\n",
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" # Quantization config\n",
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" bnb_config = BitsAndBytesConfig(\n",
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" load_in_4bit=True,\n",
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" bnb_4bit_quant_type=\"nf4\",\n",
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" bnb_4bit_compute_dtype=torch.float16,\n",
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" )\n",
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"\n",
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" # Load model\n",
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" model_path = AVAILABLE_MODELS[model_name]\n",
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" tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)\n",
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" current_model = AutoModelForCausalLM.from_pretrained(\n",
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" model_path,\n",
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" quantization_config=bnb_config,\n",
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" device_map=\"auto\",\n",
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" trust_remote_code=True\n",
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||
" )\n",
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"\n",
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" # Create pipeline\n",
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" current_pipeline = pipeline(\n",
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" \"text-generation\",\n",
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" model=current_model,\n",
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" tokenizer=tokenizer,\n",
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" max_new_tokens=800,\n",
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" temperature=0.8,\n",
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" top_p=0.95,\n",
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" do_sample=True\n",
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||
" )\n",
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"\n",
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" current_model_name = model_name\n",
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||
" return f\"✅ {model_name} loaded successfully!\"\n"
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]
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||
},
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||
{
|
||
"cell_type": "markdown",
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||
"metadata": {
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||
"id": "lnfqfrVztx3t"
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||
},
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||
"source": [
|
||
"## Data Generation Functions"
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||
]
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||
},
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||
{
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||
"cell_type": "code",
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||
"execution_count": null,
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||
"metadata": {
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||
"executionInfo": {
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||
"elapsed": 9,
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||
"status": "ok",
|
||
"timestamp": 1761737187566,
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||
"user": {
|
||
"displayName": "Kwabena Baah-Boakye",
|
||
"userId": "14758998715370101460"
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||
},
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||
"user_tz": 240
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||
},
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||
"id": "bl0iTahzt24r"
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||
},
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||
"outputs": [],
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||
"source": [
|
||
"def generate_job_description(role, industry):\n",
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||
" \"\"\"Generate a realistic job description\"\"\"\n",
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||
" prompt = f\"\"\"Create a detailed job description for a {role} position in the {industry} industry.\n",
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||
"\n",
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||
" Include:\n",
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||
" - Job title and company type\n",
|
||
" - Job overview (2-3 sentences)\n",
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||
" - Key responsibilities (4-5 bullet points)\n",
|
||
" - Required qualifications (3-4 items)\n",
|
||
" - Preferred skills (2-3 items)\n",
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||
"\n",
|
||
" Job Description:\n",
|
||
" \"\"\"\n",
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||
"\n",
|
||
" result = current_pipeline(prompt, return_full_text=False)[0]['generated_text']\n",
|
||
" return result.strip()\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {
|
||
"executionInfo": {
|
||
"elapsed": 5,
|
||
"status": "ok",
|
||
"timestamp": 1761737230115,
|
||
"user": {
|
||
"displayName": "Kwabena Baah-Boakye",
|
||
"userId": "14758998715370101460"
|
||
},
|
||
"user_tz": 240
|
||
},
|
||
"id": "XEgO5PvXuE3x"
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"def generate_matching_resume(role, industry, job_description):\n",
|
||
" \"\"\"Generate a resume that matches the job description\"\"\"\n",
|
||
" prompt = f\"\"\"Create a professional resume for a qualified {role} candidate applying to this position in {industry}.\n",
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"\n",
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||
" Job Requirements Summary:\n",
|
||
" {job_description[:400]}...\n",
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||
"\n",
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||
" Generate a resume with:\n",
|
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" - Name and contact info\n",
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" - Professional summary (2-3 sentences)\n",
|
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" - Work experience (2-3 relevant positions with bullet points)\n",
|
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" - Skills section (matching job requirements)\n",
|
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" - Education\n",
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"\n",
|
||
" Resume:\n",
|
||
" \"\"\"\n",
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||
"\n",
|
||
" result = current_pipeline(prompt, return_full_text=False)[0]['generated_text']\n",
|
||
" return result.strip()\n"
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||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {
|
||
"executionInfo": {
|
||
"elapsed": 9,
|
||
"status": "ok",
|
||
"timestamp": 1761737302930,
|
||
"user": {
|
||
"displayName": "Kwabena Baah-Boakye",
|
||
"userId": "14758998715370101460"
|
||
},
|
||
"user_tz": 240
|
||
},
|
||
"id": "RhtLuV3huNbK"
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"# main generation function\n",
|
||
"def generate_synthetic_data(model_name, industry, selected_roles, num_samples):\n",
|
||
" \"\"\"Main function to generate synthetic data\"\"\"\n",
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"\n",
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||
" # Validate inputs\n",
|
||
" if not selected_roles:\n",
|
||
" return \"❌ Please select at least one role!\", None, None\n",
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"\n",
|
||
" # Load model if needed\n",
|
||
" status = load_model(model_name)\n",
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||
" if \"❌\" in status:\n",
|
||
" return status, None, None\n",
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"\n",
|
||
" synthetic_data = []\n",
|
||
" progress_text = f\"🚀 Generating data with {model_name}...\\n\\n\"\n",
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"\n",
|
||
" for role in selected_roles:\n",
|
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" progress_text += f\"🔹 Generating {num_samples} samples for: {role}\\n\"\n",
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"\n",
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" for i in range(num_samples):\n",
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" # Generate job description\n",
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" job_desc = generate_job_description(role, industry)\n",
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"\n",
|
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" # Generate matching resume\n",
|
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" resume = generate_matching_resume(role, industry, job_desc)\n",
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"\n",
|
||
" # Store the pair\n",
|
||
" synthetic_data.append({\n",
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" 'id': len(synthetic_data) + 1,\n",
|
||
" 'industry': industry,\n",
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" 'role': role,\n",
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" 'job_description': job_desc,\n",
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" 'resume': resume\n",
|
||
" })\n",
|
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"\n",
|
||
" progress_text += f\" ✅ Sample {i+1}/{num_samples}\\n\"\n",
|
||
"\n",
|
||
" progress_text += f\"\\n✨ Generated {len(synthetic_data)} job-resume pairs!\\n\"\n",
|
||
"\n",
|
||
" # Save as JSON\n",
|
||
" json_file = 'synthetic_resume_data.json'\n",
|
||
" with open(json_file, 'w') as f:\n",
|
||
" json.dump(synthetic_data, f, indent=2)\n",
|
||
"\n",
|
||
" # Save as CSV\n",
|
||
" csv_file = 'synthetic_resume_data.csv'\n",
|
||
" df = pd.DataFrame(synthetic_data)\n",
|
||
" df.to_csv(csv_file, index=False)\n",
|
||
"\n",
|
||
" # Create preview\n",
|
||
" preview = f\"{'='*80}\\n\"\n",
|
||
" preview += f\"📊 GENERATED DATA SUMMARY\\n\"\n",
|
||
" preview += f\"{'='*80}\\n\\n\"\n",
|
||
" preview += f\"Total Samples: {len(synthetic_data)}\\n\"\n",
|
||
" preview += f\"Industry: {industry}\\n\"\n",
|
||
" preview += f\"Roles: {', '.join(selected_roles)}\\n\\n\"\n",
|
||
"\n",
|
||
" # Show first sample\n",
|
||
" sample = synthetic_data[0]\n",
|
||
" preview += f\"{'='*80}\\n\"\n",
|
||
" preview += f\"📄 SAMPLE #1\\n\"\n",
|
||
" preview += f\"{'='*80}\\n\\n\"\n",
|
||
" preview += f\"Role: {sample['role']}\\n\\n\"\n",
|
||
" preview += f\"JOB DESCRIPTION:\\n{'-'*80}\\n{sample['job_description'][:400]}...\\n\\n\"\n",
|
||
" preview += f\"MATCHING RESUME:\\n{'-'*80}\\n{sample['resume'][:400]}...\\n\\n\"\n",
|
||
"\n",
|
||
" return progress_text, preview, [json_file, csv_file]"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {
|
||
"id": "5DpmmFlpuhOI"
|
||
},
|
||
"source": [
|
||
"## Gradio UI"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {
|
||
"executionInfo": {
|
||
"elapsed": 185,
|
||
"status": "ok",
|
||
"timestamp": 1761737389227,
|
||
"user": {
|
||
"displayName": "Kwabena Baah-Boakye",
|
||
"userId": "14758998715370101460"
|
||
},
|
||
"user_tz": 240
|
||
},
|
||
"id": "I_WI5ZHcujfv"
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"with gr.Blocks(title=\"Resume Data Generator\", theme=gr.themes.Soft()) as demo:\n",
|
||
"\n",
|
||
" gr.Markdown(\"\"\"\n",
|
||
" # 🎯 Synthetic Resume & Job Description Generator\n",
|
||
" Generate realistic job descriptions and matching resumes using AI\n",
|
||
" \"\"\")\n",
|
||
"\n",
|
||
" with gr.Row():\n",
|
||
" with gr.Column(scale=1):\n",
|
||
" gr.Markdown(\"### ⚙️ Configuration\")\n",
|
||
"\n",
|
||
" model_dropdown = gr.Dropdown(\n",
|
||
" choices=list(AVAILABLE_MODELS.keys()),\n",
|
||
" value=\"Phi-2\",\n",
|
||
" label=\"Select Model\",\n",
|
||
" info=\"Phi-2 is fastest, Mistral-7B is highest quality\"\n",
|
||
" )\n",
|
||
"\n",
|
||
" industry_dropdown = gr.Dropdown(\n",
|
||
" choices=list(AVAILABLE_INDUSTRIES.keys()),\n",
|
||
" value=\"Technology\",\n",
|
||
" label=\"Select Industry\"\n",
|
||
" )\n",
|
||
"\n",
|
||
" roles_checkbox = gr.CheckboxGroup(\n",
|
||
" choices=AVAILABLE_INDUSTRIES[\"Technology\"],\n",
|
||
" value=[\"Software Engineer\"],\n",
|
||
" label=\"Select Roles\",\n",
|
||
" info=\"Choose one or more roles\"\n",
|
||
" )\n",
|
||
"\n",
|
||
" num_samples_slider = gr.Slider(\n",
|
||
" minimum=1,\n",
|
||
" maximum=5,\n",
|
||
" value=2,\n",
|
||
" step=1,\n",
|
||
" label=\"Samples per Role\",\n",
|
||
" info=\"Number of job-resume pairs per role\"\n",
|
||
" )\n",
|
||
"\n",
|
||
" generate_btn = gr.Button(\"🚀 Generate Data\", variant=\"primary\", size=\"lg\")\n",
|
||
"\n",
|
||
" with gr.Column(scale=2):\n",
|
||
" gr.Markdown(\"### 📊 Generation Progress\")\n",
|
||
" progress_output = gr.Textbox(\n",
|
||
" label=\"Status\",\n",
|
||
" lines=10,\n",
|
||
" interactive=False\n",
|
||
" )\n",
|
||
"\n",
|
||
" gr.Markdown(\"### 👀 Data Preview\")\n",
|
||
" preview_output = gr.Textbox(\n",
|
||
" label=\"Sample Output\",\n",
|
||
" lines=15,\n",
|
||
" interactive=False\n",
|
||
" )\n",
|
||
"\n",
|
||
" download_files = gr.Files(\n",
|
||
" label=\"📥 Download Generated Data\",\n",
|
||
" interactive=False\n",
|
||
" )\n",
|
||
"\n",
|
||
" # Update roles when industry changes\n",
|
||
" def update_roles(industry):\n",
|
||
" return gr.CheckboxGroup(\n",
|
||
" choices=AVAILABLE_INDUSTRIES[industry],\n",
|
||
" value=[AVAILABLE_INDUSTRIES[industry][0]]\n",
|
||
" )\n",
|
||
"\n",
|
||
" industry_dropdown.change(\n",
|
||
" fn=update_roles,\n",
|
||
" inputs=[industry_dropdown],\n",
|
||
" outputs=[roles_checkbox]\n",
|
||
" )\n",
|
||
"\n",
|
||
" # Generate button click\n",
|
||
" generate_btn.click(\n",
|
||
" fn=generate_synthetic_data,\n",
|
||
" inputs=[model_dropdown, industry_dropdown, roles_checkbox, num_samples_slider],\n",
|
||
" outputs=[progress_output, preview_output, download_files]\n",
|
||
" )\n",
|
||
"\n",
|
||
" gr.Markdown(\"\"\"\n",
|
||
" ---\n",
|
||
" ### 📝 Instructions:\n",
|
||
" 1. Select your preferred AI model (Phi-2 recommended for speed)\n",
|
||
" 2. Choose an industry\n",
|
||
" 3. Select one or more job roles\n",
|
||
" 4. Set number of samples per role\n",
|
||
" 5. Click \"Generate Data\"\n",
|
||
" 6. Download the JSON and CSV files\n",
|
||
"\n",
|
||
" **Note:** First generation will take longer as the model loads (~1-2 min)\n",
|
||
" \"\"\")"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
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||
"metadata": {
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||
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||
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||
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|
||
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|
||
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||
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||
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