CARSE_02_FineTuning.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "#! pip install openai"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "from dotenv import load_dotenv\n",
    "from openai import OpenAI\n",
    "import os"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Preparación para Fine-Tuning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "load_dotenv()\n",
    "API_KEY = os.getenv('OPENAI_KEY')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "client = OpenAI()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Separamos en Training y Validation cada file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Función para separar la data\n",
    "def dividir_training_validation(ruta_archivo, proporcion_training=0.8):\n",
    "    # Leer todas las líneas del archivo\n",
    "    with open(ruta_archivo, 'r', encoding='utf-8') as file:\n",
    "        lineas = file.readlines()\n",
    "\n",
    "    # Calcular el punto de corte para el conjunto de entrenamiento\n",
    "    corte = int(len(lineas) * proporcion_training)\n",
    "\n",
    "    # Dividir las líneas en conjuntos de entrenamiento y validación\n",
    "    lineas_training = lineas[:corte]\n",
    "    lineas_validation = lineas[corte:]\n",
    "\n",
    "    # Crear archivos para training y validation\n",
    "    ruta_archivo_base = ruta_archivo.replace('.jsonl', '')\n",
    "    archivo_training = f'{ruta_archivo_base}_train.jsonl'\n",
    "    archivo_validation = f'{ruta_archivo_base}_val.jsonl'\n",
    "\n",
    "    # Escribir el conjunto de entrenamiento\n",
    "    with open(archivo_training, 'w', encoding='utf-8') as file:\n",
    "        file.writelines(lineas_training)\n",
    "\n",
    "    # Escribir el conjunto de validación\n",
    "    with open(archivo_validation, 'w', encoding='utf-8') as file:\n",
    "        file.writelines(lineas_validation)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Llamadas a la función para crear la separación\n",
    "#dividir_training_validation('Training_Data/Training_Prompts.jsonl')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Subimos files de entrenamiento y validación"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'\\nupload_val_response = client.files.create(\\n  file=open(\"Training_Data/Training_Prompts_val.jsonl\", \"rb\"),\\n  purpose=\"fine-tune\"\\n)\\n'"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Para Training\n",
    "upload_train_response = client.files.create(\n",
    "  file=open(\"Training_Data/Training_Prompts.jsonl\", \"rb\"),\n",
    "  purpose=\"fine-tune\"\n",
    ")\n",
    "\n",
    "# Para Validation\n",
    "\"\"\"\n",
    "upload_val_response = client.files.create(\n",
    "  file=open(\"Training_Data/Training_Prompts_val.jsonl\", \"rb\"),\n",
    "  purpose=\"fine-tune\"\n",
    ")\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training file id:\tfile-exTRUxeCWwPXQRRThzhgWIM3\n"
     ]
    }
   ],
   "source": [
    "train_file_id = upload_train_response.id\n",
    "#val_file_id = upload_val_response.id\n",
    "\n",
    "print(f'Training file id:\\t{train_file_id}')\n",
    "#print(f'Validation file id:\\t{val_file_id}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Trabajo de fine-tuning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "fine_tune_response = client.fine_tuning.jobs.create(\n",
    "  training_file=train_file_id, \n",
    "  #validation_file=val_file_id,\n",
    "  model=\"ft:gpt-3.5-turbo-1106:personal:carse:8U71tg31\", \n",
    "  #suffix=\"CARSE\",\n",
    "  hyperparameters={\n",
    "    \"n_epochs\":5\n",
    "  }\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fine-tune id:\tftjob-Q2icwONwvwSm87GrJwKWz7iC\n"
     ]
    }
   ],
   "source": [
    "fine_tune_id = fine_tune_response.id\n",
    "\n",
    "print(f'Fine-tune id:\\t{fine_tune_id}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "FineTuningJob(id='ftjob-Q2icwONwvwSm87GrJwKWz7iC', created_at=1702193022, error=None, fine_tuned_model='ft:gpt-3.5-turbo-1106:personal::8U9RIZTw', finished_at=1702196815, hyperparameters=Hyperparameters(n_epochs=5, batch_size=2, learning_rate_multiplier=2), model='ft:gpt-3.5-turbo-1106:personal:carse:8U71tg31', object='fine_tuning.job', organization_id='org-IXFDgE8ZZcQzb9yKJmEuFxvC', result_files=['file-0kRZWjXqRDsSC8xKE5Cfx71c'], status='succeeded', trained_tokens=375855, training_file='file-exTRUxeCWwPXQRRThzhgWIM3', validation_file=None)"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Retrieve the state of a fine-tune\n",
    "client.fine_tuning.jobs.retrieve(fine_tune_id)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SyncCursorPage[FineTuningJobEvent](data=[FineTuningJobEvent(id='ftevent-croFh7WoWockQEWeBDvXwGiS', created_at=1702194442, level='info', message='Step 701/1875: training loss=1.77', object='fine_tuning.job.event', data={'step': 701, 'train_loss': 1.7663604021072388, 'train_mean_token_accuracy': 0.4166666567325592}, type='metrics'), FineTuningJobEvent(id='ftevent-NP6nA8Xl5wRSbywnScVrjPbo', created_at=1702194263, level='info', message='Step 601/1875: training loss=1.06', object='fine_tuning.job.event', data={'step': 601, 'train_loss': 1.0635706186294556, 'train_mean_token_accuracy': 0.7547169923782349}, type='metrics'), FineTuningJobEvent(id='ftevent-GStfnspE3KFJZcgVJJfwQUym', created_at=1702194085, level='info', message='Step 501/1875: training loss=1.98', object='fine_tuning.job.event', data={'step': 501, 'train_loss': 1.9760814905166626, 'train_mean_token_accuracy': 0.6052631735801697}, type='metrics'), FineTuningJobEvent(id='ftevent-t8KEMvBhN4GpbjolgnF4FhxY', created_at=1702193910, level='info', message='Step 401/1875: training loss=1.30', object='fine_tuning.job.event', data={'step': 401, 'train_loss': 1.2954835891723633, 'train_mean_token_accuracy': 0.6326530575752258}, type='metrics'), FineTuningJobEvent(id='ftevent-DEfvjFzsJGHPY4nec8drnQfw', created_at=1702193733, level='info', message='Step 301/1875: training loss=1.69', object='fine_tuning.job.event', data={'step': 301, 'train_loss': 1.6948037147521973, 'train_mean_token_accuracy': 0.4871794879436493}, type='metrics'), FineTuningJobEvent(id='ftevent-MnFJc3qTZ2EvaBBwzLrDJs9v', created_at=1702193558, level='info', message='Step 201/1875: training loss=2.27', object='fine_tuning.job.event', data={'step': 201, 'train_loss': 2.2718074321746826, 'train_mean_token_accuracy': 0.5102040767669678}, type='metrics'), FineTuningJobEvent(id='ftevent-V3xt82MrAL6jryV0UDcDzW1k', created_at=1702193385, level='info', message='Step 101/1875: training loss=1.79', object='fine_tuning.job.event', data={'step': 101, 'train_loss': 1.7939976453781128, 'train_mean_token_accuracy': 0.5}, type='metrics'), FineTuningJobEvent(id='ftevent-v3izLGiuwF9VcFITMFhqOgWz', created_at=1702193215, level='info', message='Step 1/1875: training loss=1.27', object='fine_tuning.job.event', data={'step': 1, 'train_loss': 1.274328351020813, 'train_mean_token_accuracy': 0.75}, type='metrics'), FineTuningJobEvent(id='ftevent-gjF6biaEA6HFpX4zAnreVR14', created_at=1702193115, level='info', message='Fine-tuning job started', object='fine_tuning.job.event', data=None, type='message'), FineTuningJobEvent(id='ftevent-0nQ0YxnmfiSDDboQZPm9h2nG', created_at=1702193114, level='info', message='Files validated, moving job to queued state', object='fine_tuning.job.event', data={}, type='message')], object='list', has_more=True)"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# List up to 10 events from a fine-tuning job\n",
    "client.fine_tuning.jobs.list_events(fine_tuning_job_id=fine_tune_id, limit=10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Probamos modelo aquí"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [],
   "source": [
    "system_content = \"CARSE es un chatbot que imita el estilo en que Sebas conversa. Fue creado con amor solo para Carmen. Su objetivo es ofrecer una experiencia de chat divertida y familiar que exprese la personalidad, el humor y el amor de Sebas hacia Carmen.\"\n",
    "\n",
    "response = client.chat.completions.create(\n",
    "  model=\"ft:gpt-3.5-turbo-1106:personal::8U9RIZTw\",\n",
    "  #temperature=1,\n",
    "  \n",
    "  messages=[\n",
    "    {\"role\": \"system\", \"content\": system_content},\n",
    "    {\"role\": \"user\", \"content\": \"Tengo mucho sueño :(\"}\n",
    "  ]\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ay amorcito :(((((\\nIntenta acabar pronto tus cosas para que duermas mucho en casita\n"
     ]
    }
   ],
   "source": [
    "print(f'{response.choices[0].message.content}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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