qlora-tuning-using-peft-from-hugging-face.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "j9Y40X0WCY_H"
   },
   "source": [
    "# QLoRA Tuning using PEFT from Hugging Face\n",
    "\n",
    "<!-- ### Introduction to Quantization & Fine-tune a Quantized Model -->"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "_afIANF7iKXp"
   },
   "source": [
    "## Brief Introduction to Quantization\n",
    "The main idea of quantization is simple: Reduce the precision of floating-point numbers, which normally occupy 32 bits, to integers of 8 or even 4 bits.\n",
    "\n",
    "This reduction occurs in the model’s parameters, specifically in the weights of the neural layers, and in the activation values that flow through the model’s layers.\n",
    "\n",
    "This means that we not only achieve an improvement in the model’s storage size and memory consumption but also greater agility in its calculations.\n",
    "\n",
    "Naturally, there is a loss of precision, but particularly in the case of 8-bit quantization, this loss is minimal.\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "jyoaSeEAi8_W"
   },
   "source": [
    "## Let's see a example of a quantized number.\n",
    "\n",
    "In reality, what I want to examine is the precision loss that occurs when transitioning from a 32-bit number to a quantized 8/4-bit number and then returning to its original 32-bit value.\n",
    "\n",
    "First, I'm going to create a function to quantize and another to unquantize."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "id": "yj5-xG8WogNP"
   },
   "outputs": [],
   "source": [
    "#Importing necessary libraries\n",
    "import numpy as np\n",
    "import math\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "id": "k17vVVU9iKws"
   },
   "outputs": [],
   "source": [
    "#Functions to quantize and unquantize\n",
    "def quantize(value, bits=4):\n",
    "    quantized_value = np.round(value * (2**(bits - 1) - 1))\n",
    "    return int(quantized_value)\n",
    "\n",
    "def unquantize(quantized_value, bits=4):\n",
    "    value = quantized_value / (2**(bits - 1) - 1)\n",
    "    return float(value)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "8vtGC-Mhh3nH"
   },
   "source": [
    "Quatizied values:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "XXRJ7mJMlpjA",
    "outputId": "9dfe2dc9-7419-46b1-c65d-0e15ba01ef27"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4\n",
      "79\n"
     ]
    }
   ],
   "source": [
    "quant_4 = quantize(0.622, 4)\n",
    "print (quant_4)\n",
    "quant_8 = quantize(0.622, 8)\n",
    "print(quant_8)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "IN7K4714h8S8"
   },
   "source": [
    "Unquantized values:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Y50sGnmbmCqv",
    "outputId": "9961e612-beec-4b99-a528-181151b7f91d"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.5714285714285714\n",
      "0.6220472440944882\n"
     ]
    }
   ],
   "source": [
    "unquant_4 = unquantize(quant_4, 4)\n",
    "print(unquant_4)\n",
    "unquant_8 = unquantize(quant_8, 8)\n",
    "print(unquant_8)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "eyjuQtfFTalb"
   },
   "source": [
    "If we consider that the original number was 0.622, it can be said that 8-bit quantization barely loses precision, and the loss from 4-bit quantization is manageable."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "id": "KzCAXBmMnNSA"
   },
   "outputs": [],
   "source": [
    "x = np.linspace(-1, 1, 50)\n",
    "y = [math.cos(val) for val in x]\n",
    "\n",
    "\n",
    "y_quant_8bit = np.array([quantize(val, bits=8) for val in y])\n",
    "y_unquant_8bit = np.array([unquantize(val, bits=8) for val in y_quant_8bit])\n",
    "\n",
    "y_quant_4bit = np.array([quantize(val, bits=4) for val in y])\n",
    "y_unquant_4bit = np.array([unquantize(val, bits=4) for val in y_quant_4bit])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "xKYNb0nMjWWu"
   },
   "source": [
    "Let’s plot a curve with the unquantized values of a cosine.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 264
    },
    "id": "u35LgstBoaTQ",
    "outputId": "e39dc62a-2eb3-4fa5-9880-7de8f07dc3d7"
   },
   "outputs": [
    {
     "data": {
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ff9WrV+fIkSNkZmYWe+W/u8X0ML8r/31/FfX3tKD3WkGaNWvGhQsXWL16NRs3bmTu3LlMmTKFWbNmMWjQoCLFeDem+swQQpifJDpCiIfWuXNnfvjhB3bt2sXTTz+d7/WdO3dy6dKlPF1JXFxcCpwA8b9XW9esWUNGRgZ//PFHniuv27Zte+B4izqL+hdffMGqVatYuXJlvopYHh4eODg4oNfrad269T33ExAQwIkTJ1BVNU8M4eHhhYqjU6dOzJ49m71799K0adN7rpt7J+G/bVxQBbX76dGjB6+//jrh4eEsW7YMW1tbOnfubHy9UqVKQE4ydr82APDx8eH111/n9ddfJyYmhvr16/Ppp5/eNdEB6NixI3PnziU0NNRY2OBBHT9+nLNnz7Jo0SL69etnXH5nJa87GQwGLl68aLyLA3D27FmAAisHPojOnTuzd+9eVqxYQa9eve65bu48QeHh4XnuNGVmZhIREVGon0FRnTt3Ls9dmPPnz2MwGIznb4rf01yurq4MGDCAAQMGkJycTLNmzZgwYcJdE52AgAAMBgPnzp3LU2QkOjqa+Pj4UjvPkhCi6KTrmhDiob399tvY2try2muvcevWrTyv3b59m6FDh+Lo6JhnnEOlSpVISEjIcycoMjIyX0Wk3Kutd15dTUhIYMGCBQ8cb+48KoWZaX7z5s2MGzeODz74gK5du+Z7XavV8uKLL7JixYoC7zjExsYa/9+hQwdu3LhhLO0LGMtyF8aYMWOws7Nj0KBBREdH53v9woULTJs2DchJOtzd3dmxY0eedR6k6t2LL76IVqvl559/5tdff6VTp0555qJp0KABlSpV4uuvvy6we1luG+j1+nxdmDw9PfH19b1vie0xY8Zga2vLwIEDCzz3olx9L+g9paqqse0K8v333+dZ9/vvv0en0/Hss88W+rj3MnToUHx8fHjrrbeMSdSdYmJi+L//+z8AWrdujaWlJd9++22ec5g3bx4JCQl07NixWGK603/HPn333XcAxuTUFL+nQL7PE3t7eypXrnzP90uHDh2AnIqJd5o8eTKASdpHCFEyyR0dIcRDq1y5MosXL6ZXr1488cQTvPrqq1SoUIFLly4xb9484uLi+OWXX/JcEe7ZsyfvvvsuL7zwAm+88QapqanMnDmTqlWr5plTpW3btlhaWtK5c2dee+01kpOTmTNnDp6enkRGRj5QvHXr1kWr1fLll1+SkJCAlZWVcf6P/+rVqxceHh5UqVIl3ziQNm3a4OXlxRdffMG2bdto3LgxgwcPpmbNmty+fZtDhw6xefNmbt++DeQMFv/+++/p168fBw8exMfHhyVLlhhLHt9PpUqVWLp0KT169KBGjRr069eP2rVrk5mZyZ49e/j111/zzEEyaNAgvvjiCwYNGkTDhg3ZsWNHgV+i78fT05OWLVsyefJkkpKS6NGjR57XNRoNc+fOpX379tSqVYsBAwbg5+fH9evX2bZtG46OjqxZs4akpCTKlSvHSy+9RFBQEPb29mzevJmwsDC++eabe8ZQpUoVli5dSq9evahWrRp9+vQhKCgIVVWJiIhg6dKlaDSaAsfj/Ff16tWpVKkSb7/9NtevX8fR0ZEVK1bcdUyGtbU169evJyQkhMaNG/PXX3+xdu1a3n///XzzRj0oFxcXfv/9dzp06EDdunXp27cvDRo0AODQoUP8/PPPxrt4Hh4ejB07lokTJ/Lcc8/x/PPPEx4ezowZM2jUqBF9+/YtlpjuFBERwfPPP89zzz3H3r17+fHHH+nduzdBQUGAaX5PIadQQIsWLWjQoAGurq4cOHDAWJ78boKCgggJCWH27NnEx8fTvHlzQkNDWbRoEV27dqVly5YPHI8QopR55HXehBBl1vHjx9XevXur3t7eqkajUQHV2tpaPXnyZIHrb9y4Ua1du7ZqaWmpVqtWTf3xxx8LLC/9xx9/qHXq1FGtra3VwMBA9csvv1Tnz5+vAmpERIRxvYCAgAJL0RZUMnrOnDlqxYoVVa1Wm6e083/XBe76uLMcdHR0tDp8+HDV399f1el0qre3t/rss8+qs2fPznPcy5cvq88//7xqa2ururu7qyNHjlTXr19/3/LSdzp79qw6ePBgNTAwULW0tFQdHBzUp556Sv3uu+/U9PR043qpqanqq6++qjo5OakODg5q9+7d1ZiYmLuWl84tlVyQOXPmqIDq4OCgpqWlFbjO4cOH1W7duqlubm6qlZWVGhAQoHbv3l3dsmWLqqqqmpGRob7zzjtqUFCQ6uDgoNrZ2alBQUHqjBkzCnXeqqqq58+fV4cNG6ZWrlxZtba2Vm1sbNTq1aurQ4cOVY8cOZJn3ZCQENXOzq7A/Zw6dUpt3bq1am9vr7q7u6uDBw9Wjx49mq+8cu4+Lly4oLZt21a1tbVVvby81PHjx+cp7ZxbXnrSpEn5jvXf9r6XGzduqG+++aZatWpV1draWrW1tVUbNGigfvrpp2pCQkKedb///nu1evXqqk6nU728vNRhw4apcXFxedZp3ry5WqtWrXzHudvvCpCn/Hfue+PUqVPqSy+9pDo4OKguLi7qiBEj8r0PHvb3NPe1O8tL/9///Z8aHBysOjs7G3/Wn376qZqZmZkvxjtlZWWpEydOVCtUqKDqdDrV399fHTt2bJ7fj3vFcr8y80KI0kFRVRltJ4QwjcWLF9O/f3/69u0rM42LUql///789ttvBXbJexzkTkwaGxv7QBUThRDCnKTrmhDCZPr160dkZCTvvfce5cqV47PPPjN3SEIIIYR4TEiiI4QwqXfffZd3333X3GEIIYQQ4jEjVdeEEEIIIYQQZY6M0RFCCCGEEEKUOXJHRwghhBBCCFHmlIoxOgaDgRs3buDg4FDkGc2FEEIIIYQQZYeqqiQlJeHr64tGc/f7NqUi0blx4wb+/v7mDkMIIYQQQghRQly9evWeE0WXikTHwcEByDkZR0dHs8aSlZXFxo0badu2LTqdzqyxlEXSvqYl7Wta0r6mJe1rWtK+piXta1rSvqZV0to3MTERf39/Y45wN6Ui0cntrubo6FgiEh1bW1scHR1LxA+6rJH2NS1pX9OS9jUtaV/TkvY1LWlf05L2Na2S2r73G9IixQiEEEIIIYQQZY4kOkIIIYQQQogyRxIdIYQQQgghRJlT5ERnx44ddO7cGV9fXxRFYdWqVffdZvv27dSvXx8rKysqV67MwoULHyBUIYQQQgghhCicIic6KSkpBAUFMX369EKtHxERQceOHWnZsiVHjhxh1KhRDBo0iA0bNhQ5WCGEEEIIIYQojCJXXWvfvj3t27cv9PqzZs2iQoUKfPPNNwDUqFGDXbt2MWXKFNq1a1fUwwshRD67r+/mSOwRc4eBXq/nfNp5Lh+7jFarNXc45pGVBomRkBwNBn2el1RUVBUMBhW9qqI3qBhUFb2Bf/7NWa6qd9u5SkpKCgeWLgDyV9pRAI0CWo2CRqOgVf79V6tR0Cig0SgoBWyLtRM4+oKtKzymE1OXpPevnYUdL1d7GTudnVnjEEKUbiYvL713715at26dZ1m7du0YNWrUXbfJyMggIyPD+DwxMRHIKW2XlZVlkjgLK/f45o6jrJL2Na2y2L4JGQmM2DqCbEO2uUMx2nZim7lDKLvu971XBfT/PIoiEYh5oIjKnJLy/k3NTGXwE4PNHUaxKYufvyWJtK9plbT2LWwcJk90oqKi8PLyyrPMy8uLxMRE0tLSsLGxybfN559/zsSJE/Mt37hxI7a2tiaLtSg2bdpk7hDKNGlf0ypL7Xsq8xTZhmzsFXtq6WqZO5wyIUuF9GxI1+c8sgyQqQeNIQNHQyJOajxuJOJKIhZK/owiSbXllupIBnefa0FBRVH+6T/9z7+KknNX5kHvp6h3PtQ7/s39/132rEHFSUnBjQS0iiHf64mqLbdUJ+IUJxI1TqRp7LHQKlhqwFIL1lqw0eb8Xyr8PLzbhtucyz7HulPr8LvqZ+5wil1Z+vwtiaR9TauktG9qamqh1iuRE4aOHTuW0aNHG5/nzn7atm3bEjFh6KZNm2jTpk2JmjCprJD2Na2y2L4nDpyAs9C+cnvGNhpr1lhKZPsmRaHcOATpCcZFqqqSmJHN7eRMbqXkPO78f1rWv8mLC0nU1ZynnuY8vsrt/LvHlrMW1bhqW5NYpydIcQvC08WTxvaWOFpbYGOpxc7SAltL7T8PC2x0Giy0RU8JHqZ9VVUlI9tASqae1MxsUjP0pGbpSc3Uk5KRTVxqFrcSUrC4dRrnuGP4JZ+kYsZpyqs3gDjgunFf6aqO42oFDhuqcF71xfBPeqNRFFxtdbjaW+Jm9+/D1d4SV1tLdHees0aL6l4NPGuCtmS8V0rK+/dK0hW6runKdfU6Ldu2xMYi/wXR0qiktG9ZJe1rWiWtfXN7e92PyRMdb29voqOj8yyLjo7G0dGxwLs5AFZWVlhZWeVbrtPpSkTjQsmKpSyS9jWtstS+B2IOANDYt3GJOSeztW9WGkQehWsH4FoYhmthaBKvF7iq2z+PKgW9eJfQVTSkuFQj26cBFgHB2AY2xsGjKg00GhoU0ykUxoO2r6UlONy3U0Bt4OV/n6beJuNyGOkR++H6AWxjjmCdlUAj5SyNNGfzb54J3P7nUQgGC2sU33oo5RpCuUY5D0ffwm1sIub+fKjoUhFPW09iUmM4GXeSpr5NzRaLKZi7fcs6aV/TKintW9gYTJ7oNG3alHXr1uVZtmnTJpo2LVsfXEKIR+92+m3Ox58HoJF3IzNH84ipKty+CNcOoF4LI+tyKBaxJ9Go/45V0gB6VeGs6k+k6ppnc0UBawstNpZabHT5/9Vq/unmpbMB33pQrhGKT13srewf4UmWALauWNVoh1WNf4rnGAxw+4IxmSThKpDz48jI1pOWpSctM/+/2Ya8FRasyaSW5hJO2alwZW/O4x+Ztt5oyzdC6x+ck/j4BIFlyei2/SgoikKwdzB/XvyTsKiwMpfoCCEenSInOsnJyZw/f974PCIigiNHjuDq6kr58uUZO3Ys169fZ/HixQAMHTqU77//njFjxjBw4EC2bt3K8uXLWbt2bfGdhRDisXQgKuduTmXnyrhau95n7VIuPQGuHyTrcihpEfuxijqEVVY8kDOmxfKf1WJVJw4bKnPYUIXDamUibasT6OtFZU97At1sKe9mR4CrLX4uNnm7UonC0WjAvUrOo24v42IFsP7n4fKfTVRVJS41i8u3Urh8K5XLt1KJuJlMeGQC+thz1FHOUVfJ6R5YXbmCZWoUnFmT8wAMaElxqY6mfDC2FYJRygWDW6UyXR3uzkRHCCEeVJETnQMHDtCyZUvj89yxNCEhISxcuJDIyEiuXLlifL1ChQqsXbuWN998k2nTplGuXDnmzp0rpaWFEA8tNCoUyPlSVKbosyH2NIarB0i+sBf1WhgOyRFoUNHxb8+yDNWCE2oFjhgqc4wqxLkG4e5biRq+Tjzl48ggHwfc7PN3AxaPlqIouNpZ4mpnSb3yedOgjOxnuBCTwunIRH6PTOTijWg0UUeplHGGeprz1NOcw0uJxyHuJMSdhKMLAEizcCLNsy52FZtgFRgMfg3A5r8pVumVe4f2xM0TpGalYqt7fO5oCSGKT5ETnRYtWqDefZIDFi5cWOA2hw8fLuqhhBDinnKv9pb6RCcpGq6FkXUljNSL+7CNPYrOkIYGuLP8yhWDB4fUKpy1qEaSR11sy9Wlqp87TX0cecXTHksLuUNT2lhZaKnp60hN39yfdE1UtQUxSRmcikxkxY0EIq9eRBd5AN+kE9TVnKe2EoFNdgI2N/6GG3/Drpwtk+wroPFvhG3FJjljfjxrgbZE1hy6r3IO5fC18+VGyg0OxxzmKb+nzB2SEKIUKp2fgEKIx97NtJtcTLiIgkJD74bmDqfwstIh6hhcCyMtYj/qtTBsU28AOXdqnP5ZLUm14aihIseVqiS61cWuYjA1KleiqZ8TXRysUMpwt6XHnaIoeDla4+VoTctqnuSUjGhHepae8Kgkfr0cS9S5A2ivHyQw4zT1lHNU0ETjkBwBpyPg9HIAsjTWZHgGYVOhMVr/3EIHPmY9t6Jo5N2I1RdWExoVKomOEOKBSKIjhCiVcu/mVHOthpOV033WNhNVhbiIfwauHyDj8n4sYk6g/adgQG7dSYOqEK6W44ihMhesqoNfI/yqBFE/0J1Bvo4ylkYAYK3TEuTvTJC/MzxdBehFZEIaBy/H8euFCNIiQnGNO0YQ56iruYCjIRVd1H6I2g//1DrIsPVBFxCMJjfx8QnKKThRAgX7BLP6wmoZpyOEeGCS6AghSqXc8TklqtpaeiIeiSfQ7DoNNw5huHYATdot48u5o2ViVUeOGKpwVK3ELZcg7Cs0onbFcjwT4EJPZxu5WyMKzcfJhk51bOhUxxd4itTMbI5dS2DJpVvcOH8M7Y2DVM8+Q13NBaopV7BKjYTTq3MegKqxAK/aKOUaofjUwy49OSdBLwEaeeX8bp+6dYrkzGTsLR+zin9CiIcmiY4QolTKvcqb+2XokTPoIeZ0Tonhawfg+gEsYsN5EhUu5KyiIadgwCk1kMOGyhylCtneDahSrSaNKrgx1N8Zeyv5GBbFx9bSgiYV3WhS0Q1aVcVgeJGLN5M5cCmOheevknA+lIoZZ4yTwHoa4iHyCEQewQJoDaiXPge/3Hl9Gpit0IGPvQ/l7MtxLfkah2IO0axcs0cegxCidJO/sEKIUic6JZrLiZfRKBoaeD/KqSqB+CsQOhsOLYH0+DwvKeQUDDisVuGIoRKHDVVIda1Jk6o+PFPFg5cruuJgbf6J1sTjQ6NRqOzpQGVPB3oGl8dgeJIzUUnsPBfLkrOxXLt8llqGc9TTnKOe5jy1lUtYpcXB+U05j1wVmkOTYVClXU6J7Uck2CeYa+euERYVJomOEKLIJNERQpQ6YdE5d3Oqu1bH0dLxPmsXA1WFK/tg3wzUM3+iqAYAUrDhiL4ih9XKHDZU5oihMqlaR5pV86J5NS8GVnHH31XK4oqSQ6NRjFXeXmteibTMRoReus3Os7GMPRtLREw8NZTL1NOcp67mPA005ymvREPE3zkP14oQ/BrU6wNWDiaPt5F3I1aeW2nsqiqEEEUhiY4QotR5ZGWlszPgxEoM+2aiiToK5Ny12aWvxQL9c2wz1EPRaKnn78wzVTx4raIz147toXPHuuh0cudGlHw2llqaV/WgeVUPsrKyWPr7OqwCnmfPxTg+OX+T2ymZlFNieUW7kV7abTjevgjr38Ww9f/Q1H8FgoeAawWTxZf7O37m9hkSMxMfzYUNIUSZIYmOEKLUCY00cSGC5BjS982BsHlYZ9xCA6SrOn7XP81CfTtibSvTuoYnM6p70rSSO042OUlNVlYWkcdNE5IQj4KzFXSo70fPxoEYDCqnIhP5+2ws605WZtq1F+mm3ckA7XoqZUbm3OHcN5PkwDbYN/8fSuAzUMyFNDxtPQl0DORS4iUORh2kZfmW999ICCH+IYmOEKJUiUyO5FryNbSKlgZexTs+J+78AeK2TcP/+l9YkwVAlOrC4uw2bLPrQOPaVZlQy5tGgS5YSMlnUcZpNAq1/Zyo7efE8JaVuRGfxsaT9fnwRHcsL29ngPYvmmuP4XBpI1zaSLRNZdIaDKF8s35oLIuvZHUj70ZcSrxEaFSoJDpCiCKRREcIUark9tWv5VYLO53dQ+/v6s0kwv/+BZ8zC6mVdYLc2lKHDZVZa9cVmzov8NwT5XjHz0nKPovHmq+zDf2fqkD/pypwO6Uhm0+/woeH91Pj6s90VXbilXYedo3h9q7/46jXC9g9+Rr1atd46Hmggr2D+fXsrzKfjhCiyCTREUKUKrmJTkPvhg+8j1vJGWw4cIb0sMW0SVpNa00sAFmqlr1WTxFTayB1m7ZhnKfM2yFEQVztLOne0J/uDf1JzujKzuPnSdu/gMYxv+Kj3KJl9CIyV/7Ixt+f5GrVEBo91Zr65Z0f6GJB7u96eFw48enxOFs7F/PZCCHKKkl0hBClhqqqD1yIID1Lz6ZT0ewN3U+NK0vppvkbOyUDNJCkceRiwMt4PTuCZuUqmiJ0IcoseysL2jWsDg2/JCPzY078/Qv2h+cQmHqcjuyEszs5cKYqE22ex6Xhi3SpF0Cge+HvxrrbuFPRqSIXEy5yIPoArQNam/BshBBliSQ6QohS43rydSJTIrFQLKjnWe++6xsMKvsibvH7wWsknNxIT8NaPtMeAW3O63F2lbB48nUcgvsQpCu+MQVCPK6sLK2o3SYE2oSgv3aYm5un4HZpLQ01Z2mY8TU3ds1n8fa2nPJ5gTYNqtOpji8udpb33W8j70ZcTLhIWFSYJDpCiEKTREcIUWrk3s2p7V4bW93d56c5G53EykPXWX/4Ik+mbGaIdj1VNNdBCyoKqYGtsWs2ApcKzYu9SpQQIoe2XD28+i+GpCiy9s9FHzYf34xbvKf7hbTYlaxc+wy9/3wOv6r16Vbfj1bVPbHWaQvcV7B3MMvCl8l8OkKIIpFERwhRauR+ySmorHRMYjp/HL3BykPXiYuMoJ/FRlZpt+KsSwFAb2GHpn5flMavYedW6ZHGLcRjzcEbXetx6Jq/DSdWkLVnBjaxJ+hjsYU+bGHH+SeYH/4c71k2oMMTfrxQz49Gga5oNP9ehMj9nT8ff57b6bdxtXY119kIIUoRSXSEEKWCqqrGRCfYJ2d8TrbewJYzMfwceoUdZ2Ooyzlet/iL56zCsFAMABicA9E0fg1tvT5g7WS2+IV47OmsoV4fdHV7w+U9OfPwhK+jmfY4zbTHuWDwYeGhdgwIa4aLsws9GvnTo5E/Xo7WuFi7UMWlCufizhEWFUa7wHbmPhshRCkgiY4QolS4knSFmNQYdBod3lbVmLLpLL+GXsIx+QINNGf5XbedIM3FfzcIfAaavI6majvQFNwdRghhBooCgU9B4FMocZchdDbqocVUyojkE81C3rFYzvLk5uzYUocFWyoTXKMifRoH0MirkSQ6QogikURHCFEq7LuRczfHPcuNvya/QRPlPIM1F7G3Sv93Ja0V1OkOjYeCd20zRSqEKDSXAGj3KUqLsXD0Z9g/C8db5xlk8ReD+AuA8+d8ORxemUgXV3CHvdf3mTdmIUSpIYmOEKJkykqHqGMkXdhL1Mld7OIE2FvQJTGcYRb/DkhWLe1R/BpAxRZQvx/YuZsvZiHEg7Gyh+DB0PBVOL8ZTvwG18Lg9kUqa25QWXODtikannHz40ryZfZPeopygU/iW+sZlHKNwNHH3GcghCiBJNERQpQMmalw9i+4sh/12gHUqGNoDFk4APbACX8/ACqrriTU6IBTlSehXCMU96rSNU2IskKjgaptcx4AKbfg+kGyLu8nLXw3VTIvc9bKgttcpPHJE3ByNgAGBz80/o2gXEOo+hy4VzHjSQghSgpJdIQQ5pVwHcLmwsEFkBYHgPLPI1Z15IihCqdcKnDTIhRLjSUt3tiDldbKrCELIR4ROzeo2hZd1bZ4t4HGoV9y9vSP/OLehKRkhdrqeaopV9AmXYdT1+HUKtg4Dio9C01eh0qtcpInIcRjSRIdIYR5XA2DfTPg1GpQ9TmLVA826Rtw2FCZs7rqBNetS+8mAdxO+Av2h1LXs64kOUI8xoJ9GrPk9I/ccs6iw/urWH3kOh/sDccq5ih1lfM00ZymmfYYmgtb4MIWcKsCjV+DoF453eOEEI8VSXSEEI9OdmZOYrN/Jlw/aFy8V1+TBfp2bDY0oKafM30aB/BFkC92VjkfUXPD7z5/jhDi8dHAqwEaRcPlxMukZN+iT+MAegeX58jVRizdf4Whx27gkRFJiHYjPS3+xv7WOVj3Nmz9JGcMX/AQcC5v7tMQQjwikugIIUwv5WZO17TQuZAcBUAmOlZlP8lCfTtOE0i7mt782qwCDQLyTgSoqioHog8AObOjCyEeXw6WDtRwrcHJWycJjQqlc6XOKIpCvfIu1CvvwvsdarA09Ao/7CnPlKSXeEm7g4EWGwhIj4I938He6VC9EzQZBuWb5pS6FkKUWZLoCCFMJ/ok7JsJx5aDPgOAmzizKKs1S/XPkmbpSvdgf2Y+FUiAm12Bu8idCd1aa80T7k88yuiFECVQsHcwJ2+dJCwqjM6VOud5zcXOkuEtKzPomQqsORrJ3J2eLI5qQ0vNEQZarOdpzQk4/UfOw7tOzjie2t3AQrrEClEWSaIjhCheBj2c3ZAz/ubSTuPi42pF5mU9x1pDE1wd7Rj0ZAV6B5fHyVZ3z92FRuV0W6vrWRed9t7rCiHKvobeDVlwcoHxs6EgVhZaXmpQjhfr+7H7/C3m7vKib3h9qipX6a9dz4sWu7GKOgarhsKmj6DhwJyHg9cjPBMhhKlJoiOEKD5Rx2H1CIg8AoAeDev1jZiX3Z5DahVq+jjxVbMKdHzCF0uLwlVCOhAl3daEEP9q4NUAraLlevJ1biTfwNfe967rKorC01XcebqKO2ejk5i3M4IJRwKYlN6DXtpt9NdtxjMlBv7+AnZPg1Yf5NzlkZL1QpQJkugIIR5edgbs+Bp112QUQzbJih1LslqxJLsNN3Dn2eqeLH2mAk0ruqEUoU+8QTUQFh0GSCECIUQOO50dtdxqcezmMcKiwuhSuUuhtqvq5cCXL9Xh7XbVWLLvMj/uc2d2Skee04QxRPcXdbLP55SmPvk7dJkOnjVMfCZCCFOTREcI8XCuHUBdNRzl5hkUYJ0+mPFZ/Um0cOXFxuUY+FQFKns+WFnXc3HnSMhIwMbChlrutYo3biFEqdXIuxHHbh4jNCq00IlOLg8HK0a3qcrrLSqx8tB15u5y4vnYJnTXbmecxU84Xj+IOusZlGbvwNNvgoWlaU5CCGFyDzSL1vTp0wkMDMTa2prGjRsTGnr3frJZWVl8/PHHVKpUCWtra4KCgli/fv0DByyEKCEyU9Gvfx91bhuUm2eIVZ0YmjmK97Rv0+vZRux5rxWfvfDEAyc58O/4nPpe9dFpZHyOECJHblfWsKgwVFV9oH1Y67T0blyezW82Z37/Rpz3e4E2GV+xSd8AxZAF2z8jc1ZzuHG4OEMXQjxCRb6js2zZMkaPHs2sWbNo3LgxU6dOpV27doSHh+Pp6Zlv/XHjxvHjjz8yZ84cqlevzoYNG3jhhRfYs2cP9erVK5aTEEI8Wlnnd5C+8nUcUq8CsEL/DN9aDKB7myC+ahqAo3XxJCW5iY6MzxFC3KmuZ10sNBZEpkRyLfka/g7+D7wvjUahVXUvWlbzZM+Fany7uRJ/XFnLBN0i3G6eQj/7WZLrD8Wp/YegsynGsxBCmFqRE53JkyczePBgBgwYAMCsWbNYu3Yt8+fP57333su3/pIlS/jggw/o0KEDAMOGDWPz5s188803/PjjjwUeIyMjg4yMDOPzxMREIOfuUFZWVlFDLla5xzd3HGWVtK9pPWz7ZiTHc+23d6l+fQU64IbqyufaodRo/gKrG/kbJ/gsjp+f3qA3FiKo716/VLwn5P1rWtK+plWa2leHjtputTkSe4R91/fhXcm7WPYbHODEj682IvRSJd7f8jQdrn9LF+0enA5NJ+b4H6S3n4LPEy0eaN+lqX1LI2lf0ypp7VvYOBS1CPd8MzMzsbW15bfffqNr167G5SEhIcTHx7N69ep827i5ufHVV1/x6quvGpf17duXXbt2cenSpQKPM2HCBCZOnJhv+dKlS7G1tS1suEKIYpKph7irR3n+9gK8ldsALDc8yyHvHjTwtsbSBAWKrmdfZ2byTKyw4n2n99EqUgVJCPGvzWmb2Z6xnSBdEC/bvWySY0QkQcLlwwzNXICXEo9BVViva831Ci/jaW9tkmMKIe4vNTWV3r17k5CQgKOj413XK9IdnZs3b6LX6/Hyyltn3svLizNnzhS4Tbt27Zg8eTLNmjWjUqVKbNmyhZUrV6LX6+96nLFjxzJ69Gjj88TERPz9/Wnbtu09T+ZRyMrKYtOmTbRp0wadTsYMFDdpX9MqavumZGSzYs9xvPZ+wsvqDlDgGt6cqP8xHVp34QWd6ZKPJaeXwGFo5NuIzi0633+DEkDev6Yl7Wtapa193aLc2L51O5EWkbRv375IFR2LpgOnLvbj7JqxPJO8ng7Zm7h29jC/+rxDqw4vU9OncN9LSlv7ljbSvqZV0to3t7fX/Zi86tq0adMYPHgw1atXR1EUKlWqxIABA5g/f/5dt7GyssLKKv8sxTqdrkQ0LpSsWMoiaV/Tul/7pmRks3DPJS7t+Ikxhrl4KIno0XC2witU6v4Zz9k8eIGBwjoYexCAJr5NSt17Qd6/piXta1qlpX0b+jREp9ERkxZDZHokAY4BJjtWULWKUG0Zl0PXYrdxNOWyo3gz6l2Wz17HvIqjGNKuAbX9nAq1r9LSvqWVtK9plZT2LWwMRaq65u7ujlarJTo6Os/y6OhovL0L7h/r4eHBqlWrSElJ4fLly5w5cwZ7e3sqVqxYlEMLIR6BzGwDi/Zc4vWvfqDq1iFMUifjoSSS4FAZdeBGaoR8i+UjSHKyDdkcjM5JdGT+HCFEQawtrKnjUQf4t3CJqQUEd8T9nYPEPTEQAwrdLf5m3OUB/DxjAqN/3E3EzZRHEocQonCKlOhYWlrSoEEDtmzZYlxmMBjYsmULTZs2vee21tbW+Pn5kZ2dzYoVK+jSpWh174UQpqM3qPx+IIJPv/yEJ9a/yCL9+7TRHsSgWGBoNgankXuwKP/oEo7Tt06TkpWCg6UD1VyqPbLjCiFKF2OZ6ciwR3dQK3tcXpyCZuB6Mp0r4anE86luPh+de5mN017ji182EZWQ/ujiEULcVZG7ro0ePZqQkBAaNmxIcHAwU6dOJSUlxViFrV+/fvj5+fH5558DsH//fq5fv07dunW5fv06EyZMwGAwMGbMmOI9EyFEkamqyt9Hwrm4fjrt0//kBeU2aECv6FCeeAnN0yPNMjt47tXZhl4N0WqkCIEQomCNvBsx8+hMQqNCUVXVhON0ClC+CZbD98DBBWTumYlz4mVe064h+/RaNp0KJrb2AJ7v+ALOdvm74gshHo0iJzo9evQgNjaWjz76iKioKOrWrcv69euNBQquXLmCRvPvjaL09HTGjRvHxYsXsbe3p0OHDixZsgRnZ+diOwkhRNEdO7iH6E1TeSZtKy2ULFAgVeeGrskgdI0HgX3+ebEelbConKuzMn+OEOJegjyCsNJacSv9FhEJEVR0fsTd4nXW0GQYlsFD4OwGEv/+DsfIPbRX9sGpfZw89SVh1QfwVJdB6CzMP65BiMfNAxUjGDFiBCNGjCjwte3bt+d53rx5c06dOvUghxFCFDfVgEX0Yc5+PYU6Gf/M9q1AlF01HFuMxLbeS2Bh3quPWYYsDsUcAmR8jhDi3iy1ltT1qMv+qP2ERoU++kQnl0YL1TvgWL0DatQJIjdOwf3iampxkVpnPuTmmW+4WrEnWuva5olPiMeUyauuCSFKgIwkbu2aj2HfLDpm3QBAryqcdm6O73Oj8a7eDB5ll497OHnzJGnZaThbOVPFpYq5wxFClHANvRsaE52e1XuaOxwU79r49puHIflLTq39Fo8zS/BQb+N+cRa1VAuuJ26ifIe30PgGmTtUIco8SXSEKMuSY0jZOgntkR9xM6QCkKDaEurameqd36J2xZI30D+321pDr4ZolCLVSxFCPIaCvYOZznQORB149ON07kFj707NHh+TmfEBO9bOx/XYXGor5wm8thpmrybOIxjntmNQqrQxd6hClFmS6AhRFqkqGQd/Ql0/FrvsnEm1zht82e32IuludRjY44USUQe/ILmFCKTbmhCiMJ5wfwIbCxviMuI4H3++xN0JtrSyolm3YSS068+kOTOpFbeJNuzHJTYUfnqJ+IrP4/ziFLBzN3eoQpQ5crlUiDLGEHeFqJmdsfpzONbZiZw0BPCJ8yfcCtlB79fH4+1QcisAZeozORJzBJBCBEKIwtFpddT1qAs8uvl0HoStpQVVK1Si4ejf+KHe7ywwdECvKjhf/IPkyfVJCF0KqmruMIUoUyTREaKsMBi4vP5b0r8NxjtmJxmqBT/o+nL1xT8ZN/J/NK7kYe4I7+v4zeOk69NxtXalknMlc4cjhCglgn3+mU8n6hHOp/OAXGwtGdG1Oe1Gz2dq4ExOG/yx1yfgtG4YEd91Jv3WVXOHKESZIYmOEGVAZMRJzk1qQcC+D7FV0zisVuP3xssIGfMtzwWVLzF91u/nzm5rpSVmIYT55XZ1PRB9AINqMHM0hePrbMNbA3qRPmALS237kqlqqXB7J9nfBXN09TRUQ+k4DyFKMkl0hCjFUtIy2L7gI5wXtqBK2lFSVSv+8BlJudHb6dmhNda60jXZpsyfI4R4EDXdamJrYUtCRgJn486aO5wiqVfBi55vf8/OVis5qVTBnlSCDn/E8S9acvr0MXOHJ0SpJomOEKWQwaCycds2Ir56ihaXp2GjZHLcsi7Xem3m+dc+xsPJ1twhFlmGPoOjMUcBKUQghCganUZHfa/6AIRGltxxOnej0Sg827wFFd7dzY7AkaSrOupkHiHgl9asmjmO6PgUc4coRKkkVdeEKKJ9kfuYe3wu2YZssxw/OS2T1JuX8dDHonip6PEh3aE8dq6+cGkyXLr7tqqqcjvpNis3ryxxXcPSs9PJNGTibuNOoGOgucMRQpQyjbwbsev6LhacXMDWq1vNHU4+hf789YYfnFuRHXsOG30ysIwff/mDVIcKuLu6oDHTR3c1l2q8G/yulP0XpYokOkIU0feHv+do7FHzBmEJV7mjelp2JMREFnrzSzGXij+mYtK8XPMSl4QJIUq+Z/yeYerBqdxMu8nNtJvmDueuCv35awlg/c8TFbIvcjXGNDEVxsHog3Ss2JE6HnXMF4QQRSSJjhBFkJqVysmbJwGY+ORE7HX2Jj9mZraBzcevYnl2Na2Ug2hQSdfYodbrg02lp4DCJwV6vZ5Dhw5Rv359tNqSN35Hp9HR2KexucMQQpRCVVyqsLTjUm4k3zB3KAV60M9fNfUWibtm4xR/GoCrqifbHTrTokkj/F0fTTfln07/xKGYQ4RGhUqiI0oVSXSEKIJDMYfIVrPxs/ejW5VuJj/eltPRrPv9V95K/55Kmpw7NgmVnsep24NNLpeVlUXmiUxal29dYicMFUKIB1XbvTa13WubO4wCPdTnb42eZB76GcNf72KdfZmQ1Bn8sLIzoY1GMbLdEzhYm/bzPDYtlkMxhwiLCmPQE4NMeiwhipMkOkIUwZ3lj03pWlwqX6w+QKPz3zLDYhNoIN3aA6suU3Gq0cmkxxZCCFHCKAqWDXpDtdakr3oT6/N/MtxiNecPhjHq2Ai6PP8Cnev4mKzbb+7fvMMxh8nSZ6HTyoUyUTrIiDIhiiAs0rTljzOzDczcfoHxk7/jvYgBhFhsAiArqC/WIw+gSJIjhBCPL3tPrPv+BN0Xk2ntTmXNDeZkf0Dsr6N5dc7fXIhNNslhKztXxsXKhbTsNE7cOmGSYwhhCpLoCFFISZlJnLp9CjDNHZ29F27RfdpfuG1+k3nazyin3CTLwR9eWYXuhelg41zsxxRCCFEK1eyC5Rth6J/oiUZRedXiL8ZfG8yEaTP4ekM4aZn6Yj2cRtHQ0LshUDrLd4vHlyQ6QhTSoehDGFQD5R3K423nXWz7jUlK581lR1gw7ztmJ75Od4u/UVFQg4egG7EPKrUstmMJIYQoI2xd0b74A/T5jWx7XwI0MSyx+BTfne/SdfI6tpyOLtbD5fZkyJ3YWYjSQBIdIQop98O9uO7m6A0qi/de4uVv/uDZE+8y23IKnko8etfKKAPXo3SYBFamr+omhBCiFKvSBosR+1EbvgpAb4ttLEx7g5+WzGbw4gNci0stlsPk/u07EnuETH1msexTCFOTREeIQirOQgRHrsbT5fudHFzzA7+rb9JJuw9V0cLTo9EO2w3lmzz0MYQQQjwmrB1ROk2G/msxuFTER7nNfMuv6XjuQ16avIYZ28+TmW14qENUdKqIm7UbGfoMjsUeK6bAhTAtSXSEKISEjATO3D4DPFwhgoTULD74/TjDZvzBm7EfMc1yBq5KMqpXbZTBW6H1eNBZ339HQgghxH8FPo1m2G548n+oioau2j38qXmbkxsX0X7q3+y58OATqSqKYrzQJ93XRGkhiY4QhXAw+iAqKoGOgXjYehR5e1VV+ePoDVp9vQ39gYVssBzDs9rDqFpLaDkOZch28K1b7HELIYR4zFjaQtv/Q3l1M6pnTdyVRKZbfsuYhP9j5Jz1jF5+hLiUB+t6lpvo5PZwEKKkk3l0hCiE3KtXD3I3JzIhjQ9XnSD8zHG+tZjLU7qTOS/4NUTpMh08qxdnqEIIIQSUa4Ay5G/Y+Q3qzq9pxwGaaE7xf0f70ia8DRO7PEGHJ7yLNPdO7t/Ao7FHSc9Ox9pCeiCIkk3u6AhRCMbxOT6FH59jMKgs3X+F5yZvp/zZRWywfI+ntCdRLWyg3Wfw6kZJcoQQQpiOhSW0HIvy2g7wrYeTksok3WwmZ37MZ0s3MGTJQaIT0wu9uwDHADxtPMkyZHE09qgJAxeieEiiI8R9xKXHcTbuLACNvAqX6Fy6mULvufuYt2o9C9RxfKRbgq2SAYHPoLy+B5oOB43WlGELIYQQObxqwaubofVEVAtrmmmPs9FqDD7hi2kzeRu/hF5BVdX77kZRFOMFP+m+JkoDSXSEuI+D0QcBqORUCTcbt3uum6038MPfF+g4dSsNLs/nL8ux1NecR7V0gE5Tod8f4FrxEUQthBBC3EFrAU+PQhm6G8o/iZ2Swce6Rcw1jOeH3zfSZ+5+Lt9Kue9uci/4HYg6YOqIhXhokugIcR+FLSt96kYiL8zYwx/r17Nc8wHv6JZjqWRDlXYow/dDwwGgkV85IYQQZuReGfqvhQ5fo1raE6wJZ73lezxxaSEdpm5jzo6L6A13v7uTO07n2M1jpGWnPaqohXgg8q1LiPswFiLwKbgQQUa2nm82hvPS91t5LvoHVluNo5bmMqqNC3SbA72XgZPfowxZCCGEuDuNBoIHo7y+Fyq1wkrJYqzuZ35WxrHirw10m7GbM1GJBW5azqEc3nbeZBuyORxz+BEHLkTRSKIjxD3cSrvF+fjzADT0apjv9YOXb9Nh2k52b1vHHxZjGW7xBxYYoNYLKMPDoE53KEJFGyGEEOKRcS4PfVdClxmo1k7U0USwxvIDWkXNo9u325i86SwZ2fo8myiKYryrI/PpiJJOEh0h7iEsOudDvKpLVVysXYzLUzKymfDHSfrN2kbfuBn8ZjWRypobYO8FPX6ClxeCfdHn2xFCCCEeKUWBen1QhodC9U7oFD0jLVbyu8X7/L31Lzp9u4tDV+LybCLz6YjS4oESnenTpxMYGIi1tTWNGzcmNPTeb/SpU6dSrVo1bGxs8Pf358033yQ9vfDlDIUwl7DI/PPn7Dp3k7ZTdnBu3xrW695lgMUGNKhQtw8M3w81OpkrXCGEEOLBOHhDjx/h5YWodh5U01xjpdUEXr79A31mbuPjNadIy8y5u5P7N/HkzZOkZN2/gIEQ5lLkRGfZsmWMHj2a8ePHc+jQIYKCgmjXrh0xMTEFrr906VLee+89xo8fz+nTp5k3bx7Lli3j/ffff+jghTC1OwsRpGZm89HqE7w+bysjkr/lJ8vP8dfEgpM/9F0BXWeAjct99iiEEEKUUIryT9frUKjTAy0GhlisZZ3uPU7uWUfH73Zy5Go8vva++Nn7oVf1HIo+ZO6ohbirIic6kydPZvDgwQwYMICaNWsya9YsbG1tmT9/foHr79mzh6eeeorevXsTGBhI27Zt6dWr133vAglhbjGpMVxKvISCgkVmZTpM28n1/SvZaDWGXhbbclZqNBhe3wuVW5s3WCGEEKK42LpCt9nQ+1dw9KOCJpplVp8wIO47QmZuZvLGcBr+U2ZaxumIksyiKCtnZmZy8OBBxo4da1ym0Who3bo1e/fuLXCbJ598kh9//JHQ0FCCg4O5ePEi69at45VXXrnrcTIyMsjIyDA+T0zMqfyRlZVFVlZWUUIudrnHN3ccZVVJat/91/cD4GwRwOh5u/nQYjFdLPcAoLpWRN9xGmr5pjkrl4B4C6MktW9ZJO1rWtK+piXta1qlsn0rtIQhu9BsmYD28CJesdhMK/UwH2x/lYhyjmAHoZGhJeKcSmX7liIlrX0LG4eiFmYq3H/cuHEDPz8/9uzZQ9OmTY3Lx4wZw99//83+/fsL3O7bb7/l7bffRlVVsrOzGTp0KDNnzrzrcSZMmMDEiRPzLV+6dCm2traFDVeIh/JTwipOqwcod7siP8YfxE1JwoDCBc8OnPF5AYPG0twhCiGEEI+Ee9Ip6l6Zj11mzlCF+TRhSoUboCq87/QBthprM0coHiepqan07t2bhIQEHB0d77peke7oPIjt27fz2WefMWPGDBo3bsz58+cZOXIkn3zyCR9++GGB24wdO5bRo0cbnycmJuLv70/btm3veTKPQlZWFps2baJNmzbodDqzxlIWlYT21RtU5u66RHjMN2AJ72Xuw01JR/WsiaHjNAJ96xFolsgeXklo37JM2te0pH1NS9rXtEp/+3aArNfR//05mtAfGKjuY3mWH9d1WhbF3GZ6196UdzXfxejS374lW0lr39zeXvdTpETH3d0drVZLdHR0nuXR0dF4e3sXuM2HH37IK6+8wqBBgwB44oknSElJYciQIXzwwQdoCpgp3srKCisrq3zLdTpdiWhcKFmxlEXmat9LN1N4a/kRfKN/xlAxDo2qUi/TAC3eR3n6TSwsysZdHHn/mpa0r2lJ+5qWtK9pler21TlB+y/giZdQVw+nSVoMK3T2VExbyIDpLgzt+CS9gv1RzDh/XKlu31KgpLRvYWMoUjECS0tLGjRowJYtW4zLDAYDW7ZsydOV7U6pqan5khmtVgtAEXrNCWFSqqqyZN9lBk1bwajIMbR1WAFATVWH45C/ocW7UEaSHCGEEOKhlGuI8toOggNzCvHctI3nD2U0B1ZPZ8CCUKITZQoRUTIUuera6NGjmTNnDosWLeL06dMMGzaMlJQUBgwYAEC/fv3yFCvo3LkzM2fO5JdffiEiIoJNmzbx4Ycf0rlzZ2PCI4Q5RSakETJvHxfWfM0fmrd5RnuCUBs7ABrV7gteNc0coRBCCFHCWFjR6NnPADhjaYmiTWOy5Sz6R7xDv8kr+OPoDTMHKMQDjNHp0aMHsbGxfPTRR0RFRVG3bl3Wr1+Pl5cXAFeuXMlzB2fcuHEoisK4ceO4fv06Hh4edO7cmU8//bT4zkKIB6CqKquP3GD+6g18ZJhJQ93ZnOXlnyTMPhPSYgj2bWzmKIUQQoiSycPWgwpOFYhIiOBAcD9ahv1MC47SUB3NF8t7selEXz7uWgcXO+kRIczjgYoRjBgxghEjRhT42vbt2/MewMKC8ePHM378+Ac5lBAmEZ+ayYcrj+B/ei6/WqzESpOFQWeHpu3H3KjWjuu/d0CraKnnWc/coQohhBAlViOvRjmJjqsvzw7dhWH1cOyvhfJ/ugXsD9/Lq1OGM7J7e5pX9TB3qOIxVOSua0KUdvsv3uKNKYsZcnYwY3TLsFKyMFR6Fs3w/dBoEGExBwGo5V4LO52dmaMVQgghSq5GPjkTh4ZGhYJHVTQD18NzX2KwsKGx5gxLs0aza9FHfLrmOBnZejNHKx43kuiIx0a23sDU9ccJmz+aeZljeEJziWwrJ+g6E03fFeDsD/w7y3Owd7A5wxVCCCFKvEZeOYnO2bizxKXHgUYLTYaiGb4PfYXmWCtZfKBbSsewfrz57VIuxCabOWLxOJFERzwWrt5OZdz3C+i4pwcjLFahU/RkV+uMxYgwqNsb/imFqapqzlUpoJF3I3OGLIQQQpR4bjZuVHauDMCB6AP/vuASiLbfanj+O7J0DtTVXGRqwkjWfTeSX/dfkMq74pGQREeUeX8eOMf2aa/y2e23qKK5ToaVG7y8CIteP4KDV551ryVdIyolCguNhYzPEUIIIQoh98JgaGRo3hcUBer3Q/e/MNIrtsNS0fM/zW/UXtuFr+b/QkJqlhmiFY8TSXREmZWckc2MBQt44o8OvKKsQ6OopFR/GauRB6BW1wK3yb2bU8e9DjYWNo8wWiGEEKJ0yu3qndv1Ox9HH6xfWYah2zzSdM7U0Fzl7SvD+PObQRw4L2WohelIoiPKpOMXrrB1Ui9evzyKAE0MSZae6Hstx67nXLB1vet20m1NCCGEKJqGXg1RULiQcIGbaTcLXklR0NR5CZtRB4mr+DxaRaWPfhVui1uy/LdlZOsNjzZo8ViQREeUKQaDyroVC/FY3JznszcCEF21Dw6jD6Kt1u6e26qqKoUIhBBCiCJytnamqktVAA5EHbj3ynbuuPRbQtpLP5Fg4U4FTRTdTwxh09f9uB4d8wiiFY8TSXREmRETfZ09X3ejw/GReCu3idH5ktxzFV69Z4C14323v5x4mdi0WHQaHXU86jyCiIUQQoiyIbcnxF27r/2HTe1OOL11kEsBLwHQPm0Nysym7Nv4q8liFI8fSXRE6aeqHNuwAIuZTXg6dSt6VeFMhRA83jmAffWWhd5Nbre1II8grC2sTRWtEEIIUeYYCxJEhd5nzTvYOBM4YB4xXZcRrfHCl5s02TOIsKk9SYm/Sxc4IYpAEh1RqqUnxHJq6vPU2TsKVxK5pCnPjZfWUD3kWxTLok32Kd3WhBBCiAfTwKsBCgqXEi8Rk1q0LmiedZ/D9Z0DHPLpgUFVaBT/F+nTGnJpzwoTRSseF5LoiFLr2vkT3JrWjJoJO8hStezwGYjPmP34P/FMkfd15/gcKUQghBBCFI2TlRPVXasDhe++diedjSP1X5vNqfbLuKz44abGUX7Dq4T9/AmqQQoViAcjiY4olfZsW4vdkufwM9zgBh4cbb+SZq9Nwcra9oH2dzHhIrfSb2GltZLxOUIIIcQDuG+Z6UKo3aQdTm/uY7tDZzSKSqPwr/n724EkpaYXV5jiMSKJjihVMrL1LFv4HQ22h+CiJHHBogoWQ7bSsEmLh9pvbp/iup51sdRaFkOkQgghxOMl2Ccn0SnSOJ0CODs60vzNxYRWHgVAi/jfOfZNZ05fjnrYEMVjRhIdUWpcvZXCT5Pf4eWID7FSsjjn8gwBb23D07f8Q+9bxucIIYQQD6e+Z300ioarSVeJSnm4pETRaAjuO5GLLb4nAx1P6UPJnt+e33ceQlXVYopYlHWS6IhSYeOJ6+z6bgADU+ehUVSuVelDlf+txsLG4aH3bVANxrr/Mj5HCCGEeDD2lvbUdK0JPFz3tTtVbPEKGb1+J0njyBPKRRpu6s4XS1aTkpFdLPsXZZskOqJEy9Ib+HL1QZRlfenFBgwoJDSbQLne00GjLZZjnI8/T1xGHDYWNtR2q10s+xRCCCEeR418HqDM9H04VnsGu2HbSLAph78mltcvDOPDqTM5G51UbMcQZZMkOqLEuhGfxmsz1tL+4Ku00R4iS7FE/+JCnFq9CYpSbMfJvepUz7MeOq2u2PYrhBBCPG6KoyBBQTQelXEa8TdJHvVxUlL5InU8c6Z/zm8HrxXrcUTZIomOKJG2n41lxLSlfHxzFHU0EWRauqAbuBbdE12L/VihkTlXnaTbmhBCCPFw6nvWx0Kx4Hryda4nXy/endu54zBkHRlVOmGp6Jmkmc6llRMY8+sR0jL1xXssUSZIoiNKlGy9gTWXNcz/6UcWGsZRTrlJllMFLF/bAv7FXyjAoBo4EJ0zPkcKEQghhBAPx1ZnSy33WsC/FxKLlc4Gq15LMDQZAcDbul+pf3Q8PX/YTXRa8R9OlG6S6IgSIzoxnX4LD2IftYdFui9wVFIxlAtGN2QLuFUyyTHDb4eTmJmInc6Omm41TXIMIYQQ4nFiqu5rRhoNmuc+hQ5foyoaelps57248cw6lsGaY5GmOaYolSTRESXCrnM36TB1B02uzmOq5QwsFT3U7IomZA3YuZnsuLmDJet71sdCY2Gy4wghhBCPi9yu4KFRoaYtBR08GKXnUlQLG5ppj/OTxSd89et2Pvj9OBnZ0pVNSKIjzExVVWZuv8DA+bsZkzmdt3S/AaBvMgJeWgA6a5MeX+bPEUIIIYpXXc+6WGgsiE6N5mrSVdMerFp7lAHrUO08qKG5wiqrjzgcuoPuP+zjRrz0ZXvcSaIjzCY5I5vXfzrE8g3b+En3f/Sw2I6qaDhaLgTDsxNAY9q3p96g52D0QUAKEQghhBDFxcbChjrudQATdl+7k199svtvIMnaF28ljl+tPqbWjRU8/+0O9l64ZfrjixJLEh1hFudjknnhu78pf3oOf1m+RyPNWVRLe/QvL+GSx7OPJIYzt8+QnJWMg86B6q7VH8kxhRBCiMfBnd3XHgnn8uys8iGGwGewI53PdPP4LmsC78/7g7k7L5q2C50osSTREY/chpNRjJm+lG8S32Ks7meslSyo2BJl2B7UKu0eWRy5H74NvBqgLabJR4UQQgiRtyDBo0oysizs0Pf6Ddp9jmphQ1PtKdbpxhC5/mveWHqA1MzsRxKHKDlk9LUokEE1FPs+9QaVaRtOYLFnCj9r/0Cn0ZNt5YSm3adQtzcoCoasLAyqwfgwpdxER7qtCSGEEMUryDMIS40lsWmxRCREEOgUaNLjGb87KAqGJkOhalsMa0ZidWkXH+h+4nD4PkZ+O4qxIS8Q6G5X7MfXKHLvoCSSREfksz5iPR/s+oBMQ6ZpDlAF5uH37/PjX+U87vDRzx+Z5tgFCPaRQgRCCCFEcbLSWhHkGURYVBhdVnd5ZMfN8/1BASqU/+dJFjCJLusmmeS4jbwbMbftXEl4Shj5aYh8fjv3m+mSnBKmhmsNqrpUNXcYQgghRJnTuWJnFBRzh/FIhEWFcT7+vLnDEP8hd3REHpn6TI7EHAFgSfslBDgGPNT+9m5fi9/+TyivxACQWKEjjh0ngq1rgetnZWWxefNmWrdujU6ne6hjF4aTlZNcfRFCCCFM4IUqL9A2sC2ZetNfPL3v9wdVJfvEKjI3TsRWn0i2qmGbUxca952Io739Qx37nb/fYX/UfsKiwuTiaQnzQInO9OnTmTRpElFRUQQFBfHdd98RHFxw958WLVrw999/51veoUMH1q5d+yCHFyZ0LPYYGfoMXK1dCfIIQlEe7EpMZko8RxeMosPN3wG4rXHHqss0Aup0uud2Wdos7DR2uFi7PJJERwghhBCmY6ezw05X/GNi/qtQ3x8aDYQanbm6dAT+N9bTPX4Fl6fvI/H57wio9+AVX5v4NmF/1H5CI0PpU6PPA+9HFL8iJzrLli1j9OjRzJo1i8aNGzN16lTatWtHeHg4np6e+dZfuXIlmZn/ZvK3bt0iKCiIl19++eEiFyYRFp1T776Rd6MHTnLij64je/UbNDLEAnDMqyu1Q6ahsXUurjCFKFX0ej1ZWVnmDqPUy8rKwsLCgvT0dPR6mfW8OOh0OrRaqTopHiP2HvgPWcbl3cux2zSGAPU6hlUvcvF4Hyr2+BKsin53J7eo0YHoAxhUg/QUKUGKnOhMnjyZwYMHM2DAAABmzZrF2rVrmT9/Pu+9916+9V1d83ZR+uWXX7C1tZVEp4TKndgrtyxkkWSmcnP5/3A//xsA11RPoltMokHLrsUYoRClh6qqREVFER8fb+5QygRVVfH29ubq1asPfCFG5Ofs7Iy3t7e5wxDikQp4qjvx1Vvw9/wRNE/ZQMWLPxL/zRbse8/HIvDJIu2rpltNbC1sScxM5GzcWZmbrwQpUqKTmZnJwYMHGTt2rHGZRqOhdevW7N27t1D7mDdvHj179sTO7u63MTMyMsjIyDA+T0xMBHKu5pn7qmju8c0dhylk6DM4GnMUgHru9Yp2jskxJCzsjnvCCQyqwu9WnQh65SvqeHsUaT9luX1LAmlf0/pv+0ZHR5OYmIiHhwe2trby5fwhqapKSkoKdnZ20pbFQFVVUlNTiY2NRa/XGy9MyueDacjnr2k9SPvaObrQ+I0f+WXlTzx95v8olxlJ1sLnSejwLbb1inZBvp5HPXZH7mbv9b1UcqhUpG1Lg5L2/i1sHIpahFmcbty4gZ+fH3v27KFp06bG5WPGjOHvv/9m//7999w+NDSUxo0bs3///ruO6QGYMGECEydOzLd86dKl2NraFjZcUUQXsy4yP2U+DooDYxzHFPqLhE3aDeqe/QZPQyxxqj3f2LxBnarVsZLeEOIxpigKPj4+eHt74+DgYO5whLirpKQkoqKiiIyMlNnjxWPrdGw6Da7Mpo3mAAD73LsTXa4jFPK70M70nWxI30B1i+r0te9rylAFkJqaSu/evUlISMDR0fGu6z3Sqmvz5s3jiSeeuGeSAzB27FhGjx5tfJ6YmIi/vz9t27a958k8CllZWWzatIk2bdqUucHyM4/NhBPwdPmn6fhUx0Jtk3J2Bxa//R/2ajKXDZ5sbzCdj9q3eOCrrWW5fUsCaV/TurN9DQYDV65cwdXVFRsbG3OHViaoqkpSUhIODg5yR6cY6XQ6kpKSeOaZZ9ixY4d8PpiIfP6a1sO2bwcg/EYrli8eRXf9nzS5uZwrDuDT81vQ3P/rcuCtQDZs2MA15RrtnmuHVlO2rvaWtPdvbm+v+ylSouPu7o5WqyU6OjrP8ujo6Pv2701JSeGXX37h448/vu9xrKyssLKyyrdcp9OViMaFkhVLcTkYcxCAYN/gQp1b5K4luG0ehSXZHFUrc7vLYkIa1CqWWMpi+5Yk0r6mpdPp0Ov1KIqCVqtFo5GBqcXBYDAAOXfLpE2Lj1arRVEULCxyvhLI54NpSfua1sO0b+0AD3xHL2DRDx/ySsIPlI9YzuUfrlN+yC8o1ve+0F7Lsxb2OnuSs5K5kHSBWu7F832opCkp79/CxlCkvxSWlpY0aNCALVu2GJcZDAa2bNmSpytbQX799VcyMjLo21du55VEadlpHLt5DChEIQJV5fyKifhsHoEl2fytaYz1oHW0LKYkRwghhBDCHFztLOk98nN+rvApaaolAbd3c21KK9JvX7vndhYaCxp4NQAgNCr0UYQqCqHIl8RGjx7NnDlzWLRoEadPn2bYsGGkpKQYq7D169cvT7GCXPPmzaNr1664ubk9fNSi2B2JOUK2IRsvWy/8Hfzvup6qz+LU7AFUPj4ZgD/tXqD2qFVU8/d6VKEKIYQQQpiMTquhT//hbG86n1uqI/4Z50j6vgU3Lxy653a5ZaZzK9gK8ytyotOjRw++/vprPvroI+rWrcuRI0dYv349Xl45X3SvXLlCZGRknm3Cw8PZtWsXr776avFELYrdnWWl79b3PT05njOTO1Az8ncMqsIa31G0fXM+bo5SIEIIkePSpUsoisKRI0cKvc3ChQtxdnY2exxCCHGn9s915tILq4nAFw9DLNZLOnJ+75q7rp/bI+ZQzCGyDdmPKkxxDw/UyXnEiBFcvnyZjIwM9u/fT+PGjY2vbd++nYULF+ZZv1q1aqiqSps2bR4qWGE6ubdZc69G/FfM9QhuTGlJjZRQ0lRL/q4/hc5DJmJpIf3khSiLrl69ysCBA/H19cXS0pKAgABGjhzJrVu37rmdv78/kZGR1K5du9DH6tGjB2fPnn3YkIUQotg1qFsf3eDNHNPWwp5UAtaHcHD1dwWuW821Go6WjqRkpXDq1qlHHKkoiHxLFaRmpXLy5kkAgn3yj885c3Qv6pxnqai/yC2cONf+F1p2GfCowxRCPCIXL16kYcOGnDt3jp9//pnz588za9Ys43jM27dvF7hdZmYmWq0Wb29v48D2wrCxscHT07O4whdCiGJVzs+PiqM3sd+uFTpFT4PD49g1ezR6vSHPehpFQ0OvhoCM0ykpJNERObdY1Wz87P3ws/fL89ruDcsot/IFvLjFVY0f6f02UqfJs2aKVIjSS1VVUjOzzfIo6twow4cPx9LSko0bN9K8eXPKly9P+/bt2bx5M9evX+eDDz4AIDAwkE8++YR+/frh6OjIkCFDCuwy9scff1ClShWsra1p2bIlixYtQlEU4uPjgfxd1yZMmEDdunVZsmQJgYGBODk50bNnT5KSkozrrF+/nqeffhpnZ2fc3Nzo1KkTFy5ceOCfjxBC3Iu9nR2NRv/GPr/+ADx9Yx77JncnMSUlz3q5F4xlnE7J8Ejn0RElU0Hd1vQGlfVLJtH24hfoFD3h1nXwfW0FDi5y1VWIB5GWpafmRxvMcuxTH7fD1rJwH/e3b99mw4YNfPrpp/nm//H29qZPnz4sW7aMGTNmABjHbI4fP77A/UVERPDSSy8xcuRIBg0axOHDh3n77bfvG8eFCxdYtWoVf/75J3FxcXTv3p0vv/ySMWPGADlTFowePZo6deqQnJzMRx99xAsvvMCRI0ek9LQQwiQ0Wi1NBk/j6KoK1Do8kadSNnF4cjtcBy4nwM8X+Pe71OGYw2Tps9BpzV+K+XEmiY4gLDLnqkPuL2dSWibbZ42ic8JPoMAp93ZUH7IYjaW1OcMUQjwC586dQ1VVatSoUeDrNWrUIC4ujtjYWABatWrFW2+9ZXz90qVLedb/4YcfqFatGpMmTQJyxmyeOHGCTz/99J5xGAwGFi5ciIODAwCvvPIKW7duNSY6L774Yp7158+fj4eHB6dOnSrS+CAhhCiqoK6jiPCqgOeGIdTTH+f8nDaEdvmJ4Hp1qexcGWcrZ+Iz4jlx6wT1POuZO9zHmiQ6j7nkzGRO3c4ZMBfsHczV2HjCZ4fQOWs7AOFVX6Nmry9BZiEX4qHY6LSc+rid2Y5dVIXt7tawYcN7vh4eHk6jRnmLnAQH32euLnK6xeUmOQA+Pj7ExMQYn587d46PPvqI/fv3c/PmTeNkoleuXJFERwhhchWaduG2px/pP3WnsuEaMau6sC5mFh3ataeRdyM2Xd5EWFSYJDpmJvf3H3OHYg5hUA34O/gTcyOd6BkdaJ21nWw0XH36C6r1/kqSHCGKgaIo2FpamOVxt5LxBalcuTKKonD69OkCXz99+jQuLi54eHgAYGdnVyzt81//nfVaURRjMgPQuXNnbt++zZw5c9i/fz/79+8HcgoiCCHEo+BaqSF2w7dxw6oinko8LfaE8MuPs2nglXNxRwoSmJ8kOo+50MicX8JySgVsf+xAQ/UkqdiQ8MJP+LceZubohBCPmpubG23atGHGjBmkpaXleS0qKoqffvqJHj16FDp5qlatGgcOHMizLCzs4Qbp3rp1i/DwcMaNG8ezzz5r7E4nhBCPmrVbAD6jtnHFuTG2SgYvnxtD7NacCy9HYo6QqZeLL+Ykic5jLvdqQ6tz66msXOe21h0GrsctqIOZIxNCmMv3339PRkYG7dq1Y8eOHVy9epX169fTpk0b/Pz87ju+5k6vvfYaZ86c4d133+Xs2bMsX77cONdaUe403cnFxQU3Nzdmz57N+fPn2bp1K6NHj36gfQkhxMNSbJwp/7+1XAnohlZRGRm7ADu9BRn6DI7FHjN3eI81SXQeYzdT4jl9K6d7yrMZt4myqYzT//7Gtnxd8wYmhDCrKlWqcODAASpWrEj37t2pVKkSQ4YMoWXLluzduxdXV9dC76tChQr89ttvrFy5kjp16jBz5kxjeWorK6sHik+j0fDLL79w8OBBateuzZtvvmksdiCEEGah1VG+/3yu13sLBXgmLQGAteHbzBvXY06KETym4lMzmbLgTXCAwMwsspwb4zt4OVg7mjs0IUQJEBAQYLzzcjf/rbAGOUUE/lvI4Pnnn+f55583Pv/0008pV64c1tY5lRz79+9P//79ja9PmDCBCRMm5NnHqFGjeOONN0hMTASgdevWnDqVd+bxO49bUBxCCGFSioJfl4+45RFI/b0fsN4eTpxewt+OnWher+BKlsK05I7OY+hSbBIbpwzG0bAVgJqWfvi+vkaSHCGEScyYMYOwsDAuXrzIkiVLmDRpEiEhIeYOSwghTMLtyX7UeTbnLvMFawNeq7qwfOPfcvHFDCTRecyEnbvBuekv0j1rFWH/XE1t+czbIBNaCSFM5Ny5c3Tp0oWaNWvyySef8NZbb+W7YyOEEGVJzaAeuFu5kKUoJNrE03p3b2Yv/ZlsveH+G4tiI4nOY2TtvmNolzxPG/YTq9ERbmUJQEOfRvfZUgghHtyUKVO4ceMG6enpnD17lg8//BALC+k5LYQouxRFIdi3KQDbncrhqiTT/+wbzJz5DUnpWWaO7vEhic5jQFVV5q/eRK11L1Ffc45UjT0H2n4EQCWnSrjbuJs5QiGEEEKIsiXYO2dy5JMBNYnxaYmVksX/bv4fv0x9h2u3U8wc3eNBEp0yLj1Lz9T5S+h6qD+BmmjirXywfm0Lh5VkABp5y90cIYQQQojilpvoHLt1EocBP3KzZn8ABqcvYM93Azh8KdaM0T0eJNEpw24lZ/D991/z+pXRuCrJ3HaqhfP/dqDxqk5YVM6EfcE+wWaOUgghhBCi7CnnUA5vO2+yDdkcvnkM95enktBsAgYUuqsbiJvfnQ2HL5g7zDJNEp0y6mJMEsumvcPbCZ9hpWRxu1xrXIdvAntPbqXd4nz8eQAaejU0c6RCCCGEEGWPoijGuzphUWGgKDi1epOMF+aTqVjSSnMIn99fZMnGfVKRzUQk0SmDDkbEcnDGAF7PWgRA/BMDcR24HCztAAiLzrmbU9WlKi7WLmaLUwghhBCiLMsdIhAaFWpcZhPUDW3/P0mxcKaOJoKWu/swfdka9AZJdoqbJDplzKYjF0hc8DIvswkDCsktPsb5xSmg0RrXCYvMSXRkfI4QQgghhOnkftc6efMkKVn/FiDQBjTGbthWEmzKU065Sb/TrzF1zlzSMvXmCrVMkkSnDFm+LQzvld1oqTlMpmJJVrcF2LcYmW+93Ds6kugIIR5HLVq0YNSoUSY9hqIorFq1qlj2tX37dhRFIT4+/q7rLFy4EGdn52I5nhCi+PjZ++Fn74de1XM45nDeF90q4TRiO7fd6uOopPK/G+/yw3efcjsl0zzBlkGS6JQBBoPKnN/W8uT2njyhuUSK1hlN/z+xqvNCvnVjU2OJSIhAQZHxOUKIMu1uCcLKlSv55JNPzBNUAc6ePUuXLl1wd3fH0dGRp59+mm3bthVpHz169ODs2bPG5xMmTKBu3brFHKkQ4kEU1H3NyM4N16F/cSugA5aKnlFJ37Bq2kgu30x+xFGWTZLolHIZ2Xqmz59Pj+ODKKfcJM4mANvXt2ER0LjA9XOrrVV3rY6TldOjDFUIIUoEV1dXHBwczB2GUadOncjOzmbr1q0cPHiQoKAgOnXqRFRUVKH3YWNjg6enpwmjFEI8KGNBgn+GDuSjs8Yt5Cfi6g0DYGDmUo5M78uRy1J++mFJolOKJaRmMfe7Txl69R0clVRuutbH5X/bUdwq3nWb3KsJ0m1NiEdMVSEzxTyPIlTzCQwMZOrUqXmW1a1blwkTJgA5XbLmzp3LCy+8gK2tLVWqVOGPP/7Is/66deuoWrUqNjY2tGzZkoULF+a5s1LQ3YapU6cSGBhofB4WFkabNm1wd3fHycmJ5s2bc+jQoTzb3CuWS5cu0bJlSwBcXFxQFIX+/fsDebuu5d71+e8jd12A1atXU79+faytralYsSITJ04kOzvb+Pq5c+do1qwZ1tbW1KxZk02bNhWyteHmzZucO3eO9957jzp16lClShW++OILUlNTOXHiRJ51d+/eTZ06dbC2tqZJkyZ5Xr+z69rChQuZOHEiR48eNZ7PwoULCx2TEKJ45X7nOnX7FEmZSQWvpNHg0uULEp/9Cj0auqjbSJn/AtuOnH+EkZY9FuYOQDyYa7dT2PLDWwzP+BkUiA3oiEff+aCzvud2xvlzvGX+HCEeqaxU+MzXPMd+/4ax6mJxmDhxIl999RWTJk3iu+++o0+fPly+fBlXV1euXr1Kt27dGD58OEOGDOHAgQO89dZbRT5GUlISISEhfPfdd6iqyjfffEOnTp0ICwvD0dHxvrH4+/uzYsUKXnzxRcLDw3F0dMTGxibfcZ588kkiIyONz0+fPk2HDh1o1qwZADt37qRfv358++23PPPMM1y4cIEhQ4YAMH78eAwGA926dcPLy4v9+/eTkJBQpPE/bm5uVKtWjcWLF1O/fn2srKz44Ycf8PT0pEGDBnnWfeedd5g2bRre3t68//77dO7cmbNnz6LT6fKs16NHD06cOMH69evZvHkzAE5OcgdfCHPxtvOmvEN5riRd4VD0IZr7N7/ruo7PvEaaa3mU3/rzFMcJX9mVlXHz6Nay4J464t7kjk4pdOpqLIe/60NIxs8A3Kw7HI+QH++b5ESlRHEl6QoaRUN9r/qPIlQhRBnUv39/evXqReXKlfnss89ITk4mNDTnbvHMmTOpVKkS33zzDdWqVaNPnz557o4UVqtWrejbty/Vq1enRo0azJ49m9TUVHbv3l2oWLRaLa6urgB4enri7e1d4Jd9S0tLvL298fb2RqfTMWjQIAYOHMjAgQOBnETqvffeIyQkhIoVK9KmTRs++eQTfvjhBwA2b97MmTNnWLx4MUFBQTRr1ozPPvus0OepKAqbN2/m8OHDODg4YG1tzeTJk1m/fj0uLnnL/48fP542bdrwxBNPsGjRIqKjo/n999/z7dPGxgZ7e3ssLCyM51ZQkieEeHTuOU7nP2xqtUf76noSLdyoprnKU9u7s3DFaplr5wHIHZ1SZveJi2h+fYXOygn0aEh69kvcnxlSqG1z7+bUcK2Bg2XJ6Z8uxGNBZ5tzZ8Vcxy5GderUMf7fzs4OR0dHYmJigJw7Io0b573y2LRp0yIfIzo6mnHjxrF9+3ZiYmLQ6/WkpqZy7dq1QsdSFFlZWbz44osEBAQwbdo04/KjR4+ye/duPv30U+MyvV5Peno6qampnD59Gn9/f3x9/71bV5TzVVWV4cOH4+npyc6dO7GxsWHu3Ll07tyZsLAwfHx8Ctyvq6sr1apV4/Tp00U+VyHEo9fIuxErzq0wfhe7H125eliM2M7N2V3wSr3IS8eGMDs+kgEhQ7C0kPsUhSWJTiny585Qqm4aQFXNNdIVa/QvLsS5dvtCby/d1oQwI0Up1u5jpqLRaPJdNczKysrz/L9dpRRFwWAwFOsxQkJCuHXrFtOmTSMgIAArKyuaNm1a7LHkGjZsGFevXiU0NBQLi3//NCYnJzNx4kS6deuWbxtr63vfRS+MrVu38ueffxIXF2fskjdjxgw2bdrEokWLeO+99x76GEII88v97nXm9hkSMhIKVRBKcS6P+xvbiZ7THa9b+3j1ylgWfn+D7kM/xNFad9/thXRdKxVUVeXn1WsI3vwyVTXXiLdwR/PqeuyKkOSAFCIQQtyfh4dHnjEriYmJREREFHr7GjVqGLux5dq3b1++Y0RFReVJdo4cOZJnnd27d/PGG2/QoUMHatWqhZWVFTdv3izCmeR0S4OcOzD3MnnyZJYvX87q1atxc3PL81r9+vUJDw+ncuXK+R4ajYYaNWpw9erVPG323/O9l9TUVCAn+buTRqPJl7Ddud+4uDjOnj1LjRo1CtyvpaXlfc9bCPHoeNh6EOgYiIrKweiDhd/Q2gmvYWuIrNANC8XAoPhp/DXlNSLjU+6/rZBEp6TL1htYuHA2zx96FU8lnlibSjgO345luXpF2s+N5BtcT76OVtHK+BwhxF21atWKJUuWsHPnTo4fP05ISAharbbQ2w8dOpRz587xzjvvEB4eztKlS/NV/GrRogWxsbF89dVXXLhwgenTp/PXX3/lWadKlSosWbKE06dPs3//fvr06VPkcSYBAQEoisKff/5JbGwsycn556XYvHkzY8aMYdKkSbi7uxMVFUVUVBQJCQkAfPTRRyxevJiJEydy8uRJTp8+zS+//MK4ceMAaN26NVWrViUkJISjR4+yc+dOPvjgg0LH2LRpU1xcXIzbnz17lnfeeYeIiAg6duyYZ92PP/6YLVu2cOLECfr374+7uztdu3YtcL+BgYFERERw5MgRbt68SUZGRqFjEkKYhrHMdCG7rxlZWOLTbz7RDUYD0CNjBce/7U74taJ3033cPFCiM336dAIDA7G2tqZx48b5rt79V3x8PMOHD8fHxwcrKyuqVq3KunXrHijgx0lapp6lMybS79K72CkZRLo1wWPkNjQu/kXeV+7dnFrutbDTlfzuM0II8xg7dizNmzenU6dOdOzYka5du1KpUqVCb1++fHlWrFjBqlWrCAoKYtasWfkG59eoUYMZM2Ywffp0goKCCA0N5e23386zzrx584iLi6N+/fq88sorvPHGG0WeJ8bPz89YTMDLy4sRI0bkW2fXrl3o9XqGDh2Kj4+P8TFy5EgA2rVrx59//snGjRtp1KgRTZo0YcqUKQQEBAA5d15+//130tLSCA4OZtCgQXnG89yPu7s769evJzk5mVatWtGwYUN27drF6tWrCQoKyrPuF198wciRI2nQoAFRUVGsWbPGeNfqv1588UWee+45WrZsiYeHBz///HOhYxJCmEYjn8IXJMhHUfDqPJ5bbaaRjZa2hl0kz+3ModMXijnKskVRi1jCYdmyZfTr149Zs2bRuHFjpk6dyq+//kp4eHiBf4QyMzN56qmn8PT05P3338fPz4/Lly/j7Oyc70P8bhITE3FyciIhISFPWVFzyMrKYt26dXTo0CFf3/DilJCSwbbpr9M19TcArgd2w++V2aB9sGN+sOsD/rjwB4OeGMTI+iOLM9Ri9aja93El7Wtad7avXq8nIiKCChUqFMtYjtJs+/bttGzZkri4OONcLw/CYDCQmJiIo6Njvq5e4sGlp6cTERFBuXLl2Lp1q3w+mIh8/ppWaWjfW2m3aLG8BQA7euzAxdrl3hvcRfLpLSjLX8FOTSFC9eFahyU809i0wxJKWvsWNjcocjGCyZMnM3jwYAYMGADArFmzWLt2LfPnzy9w0OT8+fO5ffs2e/bsMTbMnZPCFSQjIyPPbfbExEQgp5H/OxD1Ucs9vinjiLodz4U5IXTNzimjeqXOSHw6jSPLABiKflxVVQmNzLl6UN+9vtnb8F4eRfs+zqR9TevO9tXr9aiqisFgeKDB8WVJ7vk/bFvkXpfLbVdRPAwGA6qqGidBlc8H05DPX9MqDe3raOFIJadKXEi4wP7r+3m2/LMPtB+rys3I6L+Om4tfpoI+Eod13dgQ9z2tnu1QzBH/q6S1b2HjKNIdnczMTGxtbfntt9/y9AsOCQkhPj6e1atX59umQ4cOuLq6Ymtry+rVq/Hw8KB37968++67d+33PWHCBCZOnJhv+dKlS7G1Ld4yqSVNfHISdc5Oo55ylixVy98+g0jxeeqh9nlLf4spSVPQouUDpw+wVAru6iCEKD65c5j4+/vftXvR42LXrl107tyZS5cuPXYTV37zzTdMmTKlwNeaNGnCb7/99ogjyi8zM5OrV68SFRVlTHaEEKbxZ+qf7MvcR2PLxnS27fxQ+9JlxlP99GQqGi6RrupY7Pw6nhUaoCjFFGwJlpqaSu/evYv3js7NmzfR6/V4eXnlWe7l5cWZM2cK3ObixYts3bqVPn36sG7dOs6fP8/rr79OVlYW48ePL3CbsWPHMnr0aOPzxMRE/P39adu2bYnourZp0ybatGlT7Lfuzpw6huPvfQhQIknGlqSuC2he+8Gy/TuturAK9sMTHk/QtU3Xh96fKZmyfYW0r6nd2b56vZ6rV69ib2//2Hddy+3K97BUVSUpKQkHBweUUvKXfOTIkbzyyisFvmZjY2P2v2mQ03XNxsaGJ598kh07dsjng4nI569plZb2tbpixb5d+7hpc5MOHR7+DozavjPn5vShSsIeBsV/y4bY/9Gq34doNMX7GVnS2je3t9f9mHweHYPBgKenJ7Nnz0ar1dKgQQOuX7/OpEmT7proWFlZYWVllW+5TqcrEY0LxR/L4T0bCdgwEFcliWiNJ1b9VuATWOf+GxbCwdicMobBPsElpv3upyT9rMsiaV/T0ul0aDQaFEVBo9HIeJJikttdLbddSwN3d3fc3d3NHcY95b5Xc+cPks8H05L2Na2S3r6N/XImVL6QcIHE7ETcbNzus8V96Fyp8sYaTs0fSs3rv9L++rdsn3mdJ1//AUvL4m+HktK+hY2hSH8p3N3d0Wq1REdH51keHR2Nt7d3gdv4+PhQtWrVPN3UatSoQVRUFJmZmUU5fJkVum4hNTb0xlVJ4qKuCvbDt+NcTEmOqqqERcpEoUIIIYQQ5uZi7UJVl6oAhEUXscz03WgtqDloDidqvgVAi/gVHJ/SmZTkwt31KMuKlOhYWlrSoEEDtmzZYlxmMBjYsmULTZs2LXCbp556ivPnz+cZOHr27Fl8fHwe+37rqCr7f/qYhvtHYa1kcdyuKeXe3Iqdm1+xHeJy4mVi0mLQaXQEeRSuyp0QQgghhDAN43w6kcWU6AAoCrW7f8TJp74lQ9XRIG0v16c+y+3oq8V3jFKoyPf+R48ezZw5c1i0aBGnT59m2LBhpKSkGKuw9evXj7FjxxrXHzZsGLdv32bkyJGcPXuWtWvX8tlnnzF8+PDiO4tSSNVnc3DWYBqf+waNohLq8SK13vwTS9vi7a+dW6s9yCMIa4vHe5yAEEIIIYS5NfJ+iPl07qNWmxAud/qZOByomn2WjFmtiLpwpNiPU1oUeYxOjx49iI2N5aOPPiIqKoq6deuyfv16Y4GCK1eu5Ok77e/vz4YNG3jzzTepU6cOfn5+jBw5knfffbf4zqKUyU5LInxGTxok7QJgV8VRPNV3PIoJ+pznzr4r3daEEEIIIcyvgVcDFBQuJV4iJjUGT9uiTYZ8P1UbteGy0zpSlr5MOTWKxCUduPL8QsrXb1usxykNHqgYwYgRIwqcYRpyJob7r6ZNm7Jv374HOVSZkx53g6hZXamVEU6GqiOs/hc83WWQSY6lqqox0cm9eiCEEEIIIczHycqJ6q7VOX37NGFRYXSs2LHYjxFQtQ7Rr23m5NyXqKU/g/UfvbgQ/zWVWg0o9mOVZKWjbE0ZkXT1JInftyAwI5w41YEjrRabLMkBiEiI4Fb6Lay0VtTxKJ7iBkIIUdq1aNGCUaNGmfQYiqKwatWqYtnXwoULcXZ2vuc6EyZMoG7dusVyPCGE6eVegM69IG0KXj7++L2xkb1WT2FJNpV2jOL8bxOg8FNolnqS6Dwit09ugflt8NRHcxlvLr+wmsbNTTeDLfzb97OuR10stY954QchxGNn+/btKIpCfHx8nuUrV67kk08+MU9Q95CRkUHdunVRFIUjR44Uadu33347T6Gg/v3755nYWwhRshgLEpgw0QFwdnKi7ujV/OX4MgCVT0zh4vxXQZ9l0uOWFCafR6csydRnEhYVxoWsC4RGhRrnHLifhBMbsTs4AwsrPReUyli1+YhyXtnsj9xv0ni3XMn5oyfd1oQQ4l+urq7mDqFAY8aMwdfXl6NHjxZ5W3t7e+zt7U0QlRDCFOp71UejaLiSdIUtl7dgb2na31/HHq8yaa0lT99agSb2T07Oaotnp4/A0rZQ22dnZ3Mh6wJXkq5QybWSSWMtTpLoFEFiZiJDtw4FYMHWBUXb2Cd3wrhMOD4OjhdvbPcS7COFCIQwN1VVSctOM8uxbSxsUJTCzZIdGBjIqFGj8nTtqlu3Ll27dmXChAkoisKcOXNYu3YtGzZswM/Pj2+++Ybnn3/euP66desYNWoUV69epUmTJoSEhDBgwADi4uJwdnZmwoQJrFq1Ks9di6lTpzJ16lQuXboEQFhYGO+//z6HDx8mKyuLunXr8s0331C5cmXjNveK5dKlS7Rs2RIAFxcXAEJCQli4cCEtWrSgbt26TJ06le3btxvXu1PuugCrV69m4sSJnDp1Cl9fX0JCQvjggw+MF7vOnTvHq6++SmhoKBUrVmTatGmFaus7/fXXX2zcuJEVK1bw119/FbjOqlWreOedd7h69SrNmzdn7ty5+Pv7A+Rp0wkTJrBo0SJjGwFs27aNFi1aFDkuIYRpOFg6UNO1JidunWDU9lGP5qBWsNg3t/DBTdj+RpF3YThv4O3gt4s3LhOSRKcItIqWyk6VSUpKwsHBAe7zvUEffx1tZgIACYoT9m6+aDWF+7JRXKq7VqeOu4zPEcLc0rLTaLy0sVmOvb/3fmx1hbtqVxgTJ07kq6++YtKkSXz33Xf06dOHy5cv4+rqytWrV+nWrRvDhw9nyJAhHDhwgLfeeqvIx0hKSiIkJITvvvsOVVX55ptv6NSpE2FhYTg6/luG/26x+Pv7s2LFCl588UXCw8NxdHTExsYm33GefPJJIiMjjc9Pnz5Nhw4daNasGQA7d+6kX79+fPvttzzzzDNcuHCBIUOGADB+/HgMBgPdunXDy8uL/fv3k5CQUOTxP9HR0QwePJhVq1Zha1vwzyk1NZVPP/2UxYsXY2lpyeuvv07Pnj3ZvXt3vnXffvttTp8+TWJiIgsW5FyUK6l3sYR4nA2pM4SZR2eSZXi03cgSEpOwT7+BFj0GxQKNSwBYWN17IzXnc9nNxu3RBFlMJNEpAhdrF5Z3XM66devo0KEDOp2u4BXTE4lb1BuXyHD0qsJch2H0eH0izrYyTkYIUfr179+fXr16AfDZZ5/x7bffEhoaynPPPcfMmTOpVKkS33zzDQDVqlXj+PHjfPnll0U6RqtWrfI8nz17Ns7OzuzevZvu3bsXKpbcL/eenp53HcxvaWmJt7c3ALdu3WLQoEEMHDiQgQMHAjmJ1HvvvUdISAgAFStW5JNPPmHMmDGMHz+ezZs3c+bMGTZs2ICvr68xjvbt2xfqPFVVpX///gwdOpSGDRsa72j9V1ZWFt9//z2NG+cky4sWLaJGjRqEhoYSHJz3rr29vT02NjZkZGQYz00IUfK0LN+SluXz31F+FFZu3c0T2wdTRXON9Bu30fVcgrZq67uun5WVlfP9t7ppx5cXN0l0ilvCdZLmd8Ul4SypqhUz3N9n2JAR2FlJUwvxOLOxsGF/b9OOy7vXsYtTnTr/3iW2s7PD0dGRmJgYIOeOSO6X8VxNmzYt8jGio6MZN24c27dvJyYmBr1eT2pqKteuXSt0LEWRlZXFiy++SEBAQJ6uZ0ePHmX37t18+umnxmV6vZ709HRSU1M5ffo0/v7+xiQHina+3333HUlJSXkm2i6IhYUFjRr9O96yevXqODs7c/r06XyJjhBC3E+3Vk/xp92v3Fo7iCacQr/0ZbI6TkHXqL+5QytW8u27OEUeI23RizikxxCjOjO73Oe83b8H1jqtuSMTQpiZoijF2n3MVDQaDep/So9mZeXtVvHfu9mKomAwGIr1GCEhIdy6dYtp06YREBCAlZUVTZs2LfZYcg0bNoyrV68SGpq30ExycjITJ06kW7du+baxtrYu8nH+a+vWrezduxcrq7zdRho2bEifPn2MY22EEKK4dWpci822y1n16zC6anaiXTuSzLjLWLb5CAo5rrOkk0SnuJzfTNbPr2CjT+WswY9lVScztldbLLRSwVsIUXp4eHjkGbOSmJhIREREobevUaMGf/zxR55l/50w2sPDg6ioKFRVNQ6W/2855d27dzNjxgw6dMjpJnH16lVu3rxZlFPB0jKnu7Ber7/nepMnT2b58uXs2bMHN7e8/c/r169PeHh4niIId6pRowZXr14lMjISHx8fIP/53su3337L//3f/xmf37hxg3bt2rFs2bI8d8ays7M5cOCA8e5NeHg48fHx1KhRo8D9Wlpa3ve8hRCi9RP+7LFZwMwl7zJMWYHlnsk5yc6LM+8/bqcUkG/hxUA9uAjDj93R6VPZo6/Jyrrz+aB3O0lyhBClTqtWrViyZAk7d+7k+PHjhISEoNUW/q700KFDOXfuHO+88w7h4eEsXbrUWL0sV4sWLYiNjeWrr77iwoULTJ8+PV+lsSpVqrBkyRJOnz7N/v376dOnT4HFBO4lICAARVH4888/iY2NJTk5Od86mzdvZsyYMUyaNAl3d3eioqKIiooiISGnkMxHH33E4sWLmThxIidPnuT06dP88ssvjBs3DoDWrVtTtWpVQkJCOHr0KDt37uSDDz4odIzly5endu3axkfVqlUBqFSpEuXKlTOup9Pp+N///sf+/fs5ePAg/fv3p0mTJnftthYYGMixY8cIDw/n5s2b+e6ECSFEricre9B00GTGK8PIUrVYnl5B5sKukBZn7tAemnwTfxiqirr5Y5Q1b6BBzwr904Q+PZd3X2iM5hFXVxNCiOIwduxYmjdvTqdOnejYsSNdu3alUqXCz5lQvnx5VqxYwapVqwgKCmLWrFl89tlnedapUaMGM2bMYPr06QQFBREaGsrbb+ctVzpv3jzi4uKoX78+r7zyCm+88Qaenp4UhZ+fn7GYgJeXFyNGjMi3zq5du9Dr9QwdOhQfHx/jY+TIkQC0a9eOP//8k40bN9KoUSOaNGnClClTCAgIAHK64f3++++kpaURHBzMoEGD8oznKS62tra8++679O7dm6eeegp7e3uWLVt21/UHDx5MtWrVaNiwIR4eHgVWZxNCiFx1/Z3pM/QD3rT4gCTVBstre8ia3RriLpk7tIeiqP/tKF0CJSYm4uTkREJCQp6youZgrDrR9lm0a0ehOfErAFOzu2HfdhyDmpWeSZRKImP73quqnXhg0r6mdWf76vV6IiIiqFChQrGM5SjNcueqyZ1H50EZDAYSExNxdHREo5HrdMUlPT2diIgIypUrx9atW+XzwUTk89e0pH2Lx5VbqXw4Zxmfp32Mr3KbbBt3LPouJ8uzTolq38LmBvKX4gHospPRLH0JzYlfyVK1vJP1Gr5dPpYkRwghhBBClFrl3Wz56vVejHb8hpOGACzSbmKY3wElfJ25Q3sgkugUVdwlnj77Cdqre0lUbRikf5eWPd+keyN/c0cmhBCihPnss8+wt7cv8FHYuXaEEOJR8nK0ZubQTnzi/jXb9UFo9OlofwuhQuxGc4dWZFJ1rSgij6Fd8gKOGTe5rroxzPAub/XrRvOqHuaOTAghSqwWLVrkKyf9uBg6dGieCU7vVNTiCkII8ai42Fky97VWvLbQjhtXv6a3xVbqXPsR/RZneK74xyGaiiQ6RXBL40JWuoZbhgD+pxnLV6+2o2Ggq7nDEkIIUUK5urri6ip/J4QQpY+9lQXzBjZhxE9juXrOk3d1v3A0xZX65g6sCCTRKYLIbEfe0o8jzmDLnEGtCCovf7yEEPf2IJNXCvEo5b5HlTIyQaAQovhY67TMfKUhby2zoP3xIOorT0uiU1bV9nNifN/2nDy4l5o+5q3+JoQo2SwtLdFoNNy4cQMPDw8sLS3li+RDMhgMZGZmkp6eLlXXioGqqmRmZhIbG4tGoykRlZSEECWPTqth0ou1+TjlGh91rG7ucIpEEp0iahToQuwpc0chhCjpNBoNFSpUIDIykhs3bpg7nDJBVVXS0tKwsbGRpLEY2draUr58eWlTIcRdaTQKwZ5qqZsnUhIdIYQwEUtLS8qXL0/2/7d3/zFV1f8fwF+XC/cCwuXC+D2RD78Cf4BgDQYraMEQY8lmW6JlUA774bKmkVAJCWuRMvvD0Y85fvRHxcShsgVkksxyiEWQCOiAUKOCJiT3IkRceH3/6HuPHvl54R653Pt8bGze93nd6znP++J9zxu45+p0NDExsdS7s+yNj4/T+fPnKTY2Fr99MBK5XE7W1tYkk8lofHx8qXcHAMCosNABAJCQTCYjGxsbnJgbgVwuJ51OR7a2tsgTAADmhD9yBgAAAAAAs4OFDgAAAAAAmB0sdAAAAAAAwOwsi/fo6D9RW6PRLPGe/Pdm2JGREdJoNPgbcQkgX2khX2khX2khX2khX2khX2khX2mZWr76NYF+jTCTZbHQ0Wq1RETk4+OzxHsCAAAAAACmQKvVkpOT04zbZTzXUsgETE5O0h9//EGOjo5Lfp1/jUZDPj4+9Ntvv5FKhQ8NNTbkKy3kKy3kKy3kKy3kKy3kKy3kKy1Ty5eZSavVkre396wfIL0sfqNjZWVFK1euXOrdEFGpVCbxRJsr5Cst5Cst5Cst5Cst5Cst5Cst5CstU8p3tt/k6OFiBAAAAAAAYHaw0AEAAAAAALODhY6BlEol5ebmklKpXOpdMUvIV1rIV1rIV1rIV1rIV1rIV1rIV1rLNd9lcTECAAAAAAAAQ+A3OgAAAAAAYHaw0AEAAAAAALODhQ4AAAAAAJgdLHQAAAAAAMDsYKEDAAAAAABmBwud+7z//vsUExND9vb2pFar53UfZqacnBzy8vIiOzs7SkhIoM7OTlHN4OAgPfvss6RSqUitVtPOnTtpeHhYgiMwbYbmcP36dZLJZNN+VVRUCHXTbS8vL38Qh2RSFtJnjz/++JTsXn75ZVHNzZs3KTk5mezt7cnd3Z0yMzNJp9NJeSgmydB8BwcH6bXXXqPg4GCys7OjVatW0Z49e2hoaEhUZ8n9W1RURP/73//I1taWoqKi6NKlS7PWV1RUUEhICNna2lJoaChVV1eLts9nPrYkhuR77Ngxeuyxx8jZ2ZmcnZ0pISFhSn16evqUXk1KSpL6MEyWIfmWlZVNyc7W1lZUg/4VMyTf6V7LZDIZJScnCzXo37vOnz9PTz31FHl7e5NMJqNTp07NeZ/6+nrasGEDKZVKCgwMpLKysik1hs7pkmMQycnJ4SNHjvDevXvZyclpXvcpKChgJycnPnXqFP/yyy+8efNm9vPz49HRUaEmKSmJ169fzxcvXuTvv/+eAwMDedu2bRIdhekyNAedTsd//vmn6OvgwYPs4ODAWq1WqCMiLi0tFdXdm7+lWEifxcXFcUZGhii7oaEhYbtOp+N169ZxQkICNzc3c3V1Nbu6unJ2drbUh2NyDM23tbWVt2zZwlVVVdzV1cV1dXUcFBTETz/9tKjOUvu3vLycFQoFl5SUcFtbG2dkZLBareb+/v5p6y9cuMByuZwPHTrE7e3t/O6777KNjQ23trYKNfOZjy2Foflu376di4qKuLm5mTs6Ojg9PZ2dnJy4t7dXqElLS+OkpCRRrw4ODj6oQzIphuZbWlrKKpVKlF1fX5+oBv17l6H5DgwMiLK9cuUKy+VyLi0tFWrQv3dVV1fzO++8w5WVlUxEfPLkyVnrf/31V7a3t+e9e/dye3s7Hz16lOVyOdfW1go1hj5nDwIWOjMoLS2d10JncnKSPT09+fDhw8LY7du3WalU8ldffcXMzO3t7UxE/OOPPwo1NTU1LJPJ+Pfffzf6vpsqY+UQHh7OL774omhsPt+k5m6h+cbFxfHrr78+4/bq6mq2srISvSB/8sknrFKpeGxszCj7vhwYq3+PHz/OCoWCx8fHhTFL7d/IyEjevXu3cHtiYoK9vb35gw8+mLb+mWee4eTkZNFYVFQUv/TSS8w8v/nYkhia7/10Oh07Ojry559/LoylpaVxSkqKsXd1WTI037nOK9C/Yovt348++ogdHR15eHhYGEP/Tm8+r0FvvfUWr127VjS2detW3rhxo3B7sc+ZFPCna4vU09NDfX19lJCQIIw5OTlRVFQUNTQ0EBFRQ0MDqdVqeuSRR4SahIQEsrKyosbGxge+z0vFGDk0NTVRS0sL7dy5c8q23bt3k6urK0VGRlJJSQmxhX0W7mLy/eKLL8jV1ZXWrVtH2dnZNDIyInrc0NBQ8vDwEMY2btxIGo2G2trajH8gJspY38dDQ0OkUqnI2tpaNG5p/fvvv/9SU1OTaO60srKihIQEYe68X0NDg6ie6L9e1NfPZz62FAvJ934jIyM0Pj5OLi4uovH6+npyd3en4OBgeuWVV2hgYMCo+74cLDTf4eFh8vX1JR8fH0pJSRHNoejfu4zRv8XFxZSamkorVqwQjaN/F2au+dcYz5kUrOcugdn09fUREYlOAvW39dv6+vrI3d1dtN3a2ppcXFyEGktgjByKi4tp9erVFBMTIxrPy8ujJ554guzt7enMmTP06quv0vDwMO3Zs8do+2/qFprv9u3bydfXl7y9veny5cu0f/9+unbtGlVWVgqPO11/67dZCmP0761btyg/P5927dolGrfE/r116xZNTExM21tXr16d9j4z9eK9c61+bKYaS7GQfO+3f/9+8vb2Fp24JCUl0ZYtW8jPz4+6u7vp7bffpk2bNlFDQwPJ5XKjHoMpW0i+wcHBVFJSQmFhYTQ0NESFhYUUExNDbW1ttHLlSvTvPRbbv5cuXaIrV65QcXGxaBz9u3Azzb8ajYZGR0fp77//XvScIwWLWOhkZWXRhx9+OGtNR0cHhYSEPKA9Mi/zzXexRkdH6csvv6QDBw5M2XbvWEREBN25c4cOHz5sFieKUud770l3aGgoeXl5UXx8PHV3d1NAQMCCH3e5eFD9q9FoKDk5mdasWUPvvfeeaJs59y8sTwUFBVReXk719fWiN8ynpqYK/w4NDaWwsDAKCAig+vp6io+PX4pdXTaio6MpOjpauB0TE0OrV6+mzz77jPLz85dwz8xPcXExhYaGUmRkpGgc/Wt5LGKhs2/fPkpPT5+1xt/ff0GP7enpSURE/f395OXlJYz39/dTeHi4UPPXX3+J7qfT6WhwcFC4/3I233wXm8OJEydoZGSEnn/++Tlro6KiKD8/n8bGxkipVM5Zb8oeVL56UVFRRETU1dVFAQEB5OnpOeWqKf39/URE6N955qvVaikpKYkcHR3p5MmTZGNjM2u9OfXvTFxdXUkulwu9pNff3z9jnp6enrPWz2c+thQLyVevsLCQCgoK6OzZsxQWFjZrrb+/P7m6ulJXV5dFnSguJl89GxsbioiIoK6uLiJC/95rMfneuXOHysvLKS8vb87/x1L7dyFmmn9VKhXZ2dmRXC5f9PeEFCziPTpubm4UEhIy65dCoVjQY/v5+ZGnpyfV1dUJYxqNhhobG4Wf3ERHR9Pt27epqalJqPnuu+9ocnJSOKlczuab72JzKC4ups2bN5Obm9uctS0tLeTs7GwWJ4kPKl+9lpYWIiLhhTY6OppaW1tFJ/nffvstqVQqWrNmjXEOcglJna9Go6HExERSKBRUVVU15XKy0zGn/p2JQqGghx9+WDR3Tk5OUl1dnein3veKjo4W1RP914v6+vnMx5ZiIfkSER06dIjy8/OptrZW9H60mfT29tLAwIDoxNwSLDTfe01MTFBra6uQHfr3rsXkW1FRQWNjY/Tcc8/N+f9Yav8uxFzzrzG+JySxZJdBMFE3btzg5uZm4RLGzc3N3NzcLLqUcXBwMFdWVgq3CwoKWK1W8+nTp/ny5cuckpIy7eWlIyIiuLGxkX/44QcOCgqy2MtLz5ZDb28vBwcHc2Njo+h+nZ2dLJPJuKamZspjVlVV8bFjx7i1tZU7Ozv5448/Znt7e87JyZH8eEyNofl2dXVxXl4e//TTT9zT08OnT59mf39/jo2NFe6jv7x0YmIit7S0cG1tLbu5uVns5aUNyXdoaIijoqI4NDSUu7q6RJc01el0zGzZ/VteXs5KpZLLysq4vb2dd+3axWq1WrjC344dOzgrK0uov3DhAltbW3NhYSF3dHRwbm7utJeXnms+thSG5ltQUMAKhYJPnDgh6lX9659Wq+U333yTGxoauKenh8+ePcsbNmzgoKAg/ueff5bkGJeSofkePHiQv/nmG+7u7uampiZOTU1lW1tbbmtrE2rQv3cZmq/eo48+ylu3bp0yjv4V02q1wjkuEfGRI0e4ubmZb9y4wczMWVlZvGPHDqFef3npzMxM7ujo4KKiomkvLz3bc7YUsNC5T1paGhPRlK9z584JNfT/n3mhNzk5yQcOHGAPDw9WKpUcHx/P165dEz3uwMAAb9u2jR0cHFilUvELL7wgWjxZirly6OnpmZI3M3N2djb7+PjwxMTElMesqanh8PBwdnBw4BUrVvD69ev5008/nbbW3Bma782bNzk2NpZdXFxYqVRyYGAgZ2Zmij5Hh5n5+vXrvGnTJrazs2NXV1fet2+f6PLIlsLQfM+dOzftfEJE3NPTw8zo36NHj/KqVatYoVBwZGQkX7x4UdgWFxfHaWlpovrjx4/zQw89xAqFgteuXctff/21aPt85mNLYki+vr6+0/Zqbm4uMzOPjIxwYmIiu7m5sY2NDfv6+nJGRsaSnsQsNUPyfeONN4RaDw8PfvLJJ/nnn38WPR76V8zQ+eHq1atMRHzmzJkpj4X+FZvp9UmfaVpaGsfFxU25T3h4OCsUCvb39xedC+vN9pwtBRmzmV/DFAAAAAAALI5FvEcHAAAAAAAsCxY6AAAAAABgdrDQAQAAAAAAs4OFDgAAAAAAmB0sdAAAAAAAwOxgoQMAAAAAAGYHCx0AAAAAADA7WOgAAAAAAIDZwUIHAAAAAADMDhY6AAAAAABgdrDQAQAAAAAAs/N/LK2gwQrDni0AAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 1000x1200 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 12))\n",
    "\n",
    "plt.subplot(4, 1, 1)\n",
    "plt.plot(x, y, label=\"Original\")\n",
    "plt.plot(x, y_unquant_8bit, label=\"unquantized_8bit\")\n",
    "plt.plot(x, y_unquant_4bit, label=\"unquantized_4bit\")\n",
    "plt.legend()\n",
    "plt.title(\"Quantized Curves Graph Comparision\")\n",
    "plt.grid(True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "CLv-uYtlT2Bo"
   },
   "source": [
    "As you can see, the difference between the 8-bit and the original values is minimal. However, we need to use 4-bit quantization if we want to load the 7B Model into a 16GB GPU without problems."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "620MdVMk7iUS"
   },
   "source": [
    "### QLoRA. Fine-tuning a 4-bit Quantized Model using LoRA.\n",
    "We are going to fine-tune with LoRA a 7B Model Quantizated to 4 bits."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "uml3wgdri2_X"
   },
   "source": [
    "### Load the PEFT and Datasets Libraries.\n",
    "\n",
    "The PEFT library contains the Hugging Face implementation of differente fine-tuning techniques, like LoRA Tuning.\n",
    "\n",
    "Using the Datasets library we have acces to a huge amount of Datasets."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "_UyyuMGnCPjA",
    "outputId": "f77685f7-1ee3-4df0-8214-f19a7f562796"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m297.6/297.6 kB\u001b[0m \u001b[31m6.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
      "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m119.8/119.8 MB\u001b[0m \u001b[31m5.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
      "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m245.2/245.2 kB\u001b[0m \u001b[31m5.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
      "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m542.0/542.0 kB\u001b[0m \u001b[31m49.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
      "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m102.0/102.0 kB\u001b[0m \u001b[31m13.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
      "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m116.3/116.3 kB\u001b[0m \u001b[31m16.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
      "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m194.1/194.1 kB\u001b[0m \u001b[31m24.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
      "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m134.8/134.8 kB\u001b[0m \u001b[31m18.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
      "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m388.9/388.9 kB\u001b[0m \u001b[31m40.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
      "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m199.1/199.1 kB\u001b[0m \u001b[31m5.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
      "\u001b[?25h"
     ]
    }
   ],
   "source": [
    "!pip install -q accelerate==0.29.3\n",
    "!pip install -q bitsandbytes==0.43.1\n",
    "!pip install -q trl==0.8.6\n",
    "!pip install -q peft==0.10.0\n",
    "!pip install -q transformers==4.40.0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "XuYNBSmTbvtB"
   },
   "source": [
    "I'm going to download the peft and Transformers libraries from their repositories on GitHub instead of using pip. This is not strictly necessary, but this way, you can get the newest versions of the libraries with support for newer models. If you want to check one of the latest models, you can use this trick.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "id": "VVe5LcY9deQ5"
   },
   "outputs": [],
   "source": [
    "#Install the latest versions of peft & transformers library recommended\n",
    "#if you want to work with the most recent models\n",
    "#!pip install -q git+https://github.com/huggingface/peft.git\n",
    "#!pip install -q git+https://github.com/huggingface/transformers.git"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "VOnJlBY-81Wl"
   },
   "source": [
    "From the Transformers library, we import the necessary classes to load the model and the tokenizer.\n",
    "\n",
    "The notebook is ready to work with different Models I tested it with models from the Bloom Family and Llama-3.\n",
    "\n",
    "I recommend you to test different models."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "id": "pBOE-h8sbrNK"
   },
   "outputs": [],
   "source": [
    "from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig\n",
    "from trl import SFTTrainer\n",
    "import torch"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "1Bc5eKi_efxO"
   },
   "source": [
    "## Hugging Face login"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "EHzVpaYMfVde"
   },
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "cwAiEFifgp3-"
   },
   "source": [
    "## Load Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "id": "vziwd2UuCYGl"
   },
   "outputs": [],
   "source": [
    "#Use any model you want, if you want to do some fast test, just use the smallest one.\n",
    "\n",
    "#model_name = \"bigscience/bloomz-560m\"\n",
    "#model_name=\"bigscience/bloom-1b1\"\n",
    "#model_name = \"bigscience/bloom-7b1\"\n",
    "#target_modules = [\"query_key_value\"]\n",
    "\n",
    "model_name = \"meta-llama/Meta-Llama-3-8B\"\n",
    "target_modules = [\"q_proj\", \"v_proj\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "UVF_hKiPd1lh"
   },
   "source": [
    "To load the model, we need a configuration class that specifies how we want the quantization to be performed. We’ll achieve this with the BitesAndBytesConfig from the Transformers library."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "id": "3h_ydWGf6EAd"
   },
   "outputs": [],
   "source": [
    "bnb_config = BitsAndBytesConfig(\n",
    "    load_in_4bit=True,\n",
    "    bnb_4bit_use_double_quant=True,\n",
    "    bnb_4bit_quant_type=\"nf4\",\n",
    "    bnb_4bit_compute_dtype=torch.bfloat16\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "x47-cqrXosTc"
   },
   "source": [
    "We are specifying the use of 4-bit quantization and also enabling double quantization to reduce the precision loss.\n",
    "\n",
    "For the bnb_4bit_quant_type parameter, I've used the recommended value in the paper [QLoRA: Efficient Finetuning of Quantized LLMs.](https://arxiv.org/abs/2305.14314)\n",
    "\n",
    "Now, we can go ahead and load the model."
   ]
  },
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    "id": "W2EZhNQ66EAd",
    "outputId": "ffa0ff48-49e0-4a98-8576-a7b7ac07c485"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "87615983d72645dda29d4f694fd3fb81",
       "version_major": 2,
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      "text/plain": [
       "config.json:   0%|          | 0.00/654 [00:00<?, ?B/s]"
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      "text/plain": [
       "model.safetensors.index.json:   0%|          | 0.00/23.9k [00:00<?, ?B/s]"
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     "data": {
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       "model_id": "152618a632a5475f99bc34c41d599754",
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      "text/plain": [
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      "text/plain": [
       "model-00001-of-00004.safetensors:   0%|          | 0.00/4.98G [00:00<?, ?B/s]"
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    {
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      "text/plain": [
       "model-00002-of-00004.safetensors:   0%|          | 0.00/5.00G [00:00<?, ?B/s]"
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    {
     "data": {
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       "version_minor": 0
      },
      "text/plain": [
       "model-00003-of-00004.safetensors:   0%|          | 0.00/4.92G [00:00<?, ?B/s]"
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     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "model-00004-of-00004.safetensors:   0%|          | 0.00/1.17G [00:00<?, ?B/s]"
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    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
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       "version_minor": 0
      },
      "text/plain": [
       "Loading checkpoint shards:   0%|          | 0/4 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
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       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "generation_config.json:   0%|          | 0.00/126 [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "device_map = {\"\": 0}\n",
    "foundation_model = AutoModelForCausalLM.from_pretrained(model_name,\n",
    "                    quantization_config=bnb_config,\n",
    "                    device_map=device_map,\n",
    "                    use_cache = False)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "nVDiZYbee77R"
   },
   "source": [
    "Now we have the quantized version of the model in memory. Yo can try to load the unquantized version to see if it's possible."
   ]
  },
  {
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   "execution_count": 13,
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      "f60ea61d84a344f58621e972229e3f99"
     ]
    },
    "id": "aU0awofs84q7",
    "outputId": "17ffc588-5767-4650-808b-b5368be37527"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "a6d286ef12d341e2842d1269570cd3c4",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "tokenizer_config.json:   0%|          | 0.00/50.6k [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "26bb3ebb140246b199ae7f6e67df34fc",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "tokenizer.json:   0%|          | 0.00/9.09M [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "ebb09fde28fd48b5830a7d8ac2d2539d",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "special_tokens_map.json:   0%|          | 0.00/73.0 [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.\n"
     ]
    }
   ],
   "source": [
    "tokenizer = AutoTokenizer.from_pretrained(model_name)\n",
    "tokenizer.pad_token = tokenizer.eos_token"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Qtc1gbK39Hp7"
   },
   "source": [
    "## Inference with the pre-trained model.\n",
    "I'm going to do a test with the pre-trained model without fine-tuning, to see if something changes after the fine-tuning."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "id": "jak6FzpvFTHk"
   },
   "outputs": [],
   "source": [
    "#this function returns the outputs from the model received, and inputs.\n",
    "def get_outputs(model, inputs, max_new_tokens=100):\n",
    "    outputs = model.generate(\n",
    "        input_ids=inputs[\"input_ids\"],\n",
    "        attention_mask=inputs[\"attention_mask\"],\n",
    "        max_new_tokens=max_new_tokens,\n",
    "        repetition_penalty=1.5, #Avoid repetition.\n",
    "        early_stopping=False, #The model can stop before reach the max_length\n",
    "        eos_token_id=tokenizer.eos_token_id,\n",
    "    )\n",
    "    return outputs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "GkFqjS459jAa"
   },
   "source": [
    "The dataset used for the fine-tuning contains prompts to be used with Large Language Models.\n",
    "\n",
    "I'm going to request the pre-trained model that acts like a motivational coach."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "3BAYg7czFYeK",
    "outputId": "c8ce2e89-78d3-4c9c-849f-c04eb484f51a"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Setting `pad_token_id` to `eos_token_id`:128001 for open-end generation.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['I want you to act as a motivational coach. \\xa0You are going on an adventure with me, and I need your help.\\nWe will be traveling through the land of “What If.” \\xa0 This is not some place that exists in reality; it’s more like one those places we see when watching']\n"
     ]
    }
   ],
   "source": [
    "#Inference original model\n",
    "input_sentences = tokenizer(\"I want you to act as a motivational coach. \", return_tensors=\"pt\").to('cuda')\n",
    "foundational_outputs_sentence = get_outputs(foundation_model, input_sentences, max_new_tokens=50)\n",
    "\n",
    "print(tokenizer.batch_decode(foundational_outputs_sentence, skip_special_tokens=True))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "KQUGY47p9ysI"
   },
   "source": [
    "The answer is good enough, the models used is a really well trained Model. But we will try to improve the quality with a sort fine-tuning process.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "FL5L_DcR9ggA"
   },
   "source": [
    "## Preparing the Dataset.\n",
    "The Dataset useds is:\n",
    "\n",
    "https://huggingface.co/datasets/fka/awesome-chatgpt-prompts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 214,
     "referenced_widgets": [
      "7bebce07ab7a4993918ad9e11b65fbe3",
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      "ccd025fb45404921b20877c37e047849",
      "dfd0b4ca20e34f78bda9d7cda9a02706",
      "1b409245d55346939541e74f3bf88ed8",
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      "df1c86eafec848d886dd4c4ae60f582a",
      "f056a3423406409b861d8d9093773d55",
      "503c506ef690461aa046ecac56017c32",
      "ec19b568ef784d0fbcfe127e6a39c308",
      "6bc929e078fa48bb9dbcac48f93951e8",
      "13673f8833054d6d8e033cccad54b2b4",
      "eea911f809e64b6e9c168e35771adf07",
      "0c6ab132e6d04cb89b777c34746c5104",
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      "afbe9173d6514a92a4b4e449c547959e",
      "20b9b90134424f428709bd1b39b960dc",
      "01bbdac89de740208dcbc2454f3554c2",
      "099cc8dbe2eb44c5bce6b89cd4740d9b",
      "e1cec2155876408e8b545b308d369827",
      "11976d6a0718404abce925e9dd877dd3",
      "fe1a33d52847403e826ae9077c2cce26",
      "f0c556cb6ee1477abf2a4d211bd6b9aa",
      "9963d329b2874a68983c1404049d182b",
      "d026497f248f48a9a5d4fa229632eff5",
      "0627ac0907f04dcebb6df857525ced39",
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      "8e5b2f9cffec48b2ad3948b0cc89701f",
      "6cba2537c6814c0e85573b7711dd5977",
      "485f1e7c052c4c4daa458c8357c5709a",
      "2c377bda0e3046498d1611eb2fe23855"
     ]
    },
    "id": "DyIMQ7IHFbIx",
    "outputId": "23164e99-e684-40fe-e57b-20f90c5c0d83"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "7bebce07ab7a4993918ad9e11b65fbe3",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Downloading readme:   0%|          | 0.00/274 [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "d7976ba31ed947c691cdd8b94ab734e2",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Downloading data:   0%|          | 0.00/74.6k [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "ec19b568ef784d0fbcfe127e6a39c308",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Generating train split:   0%|          | 0/153 [00:00<?, ? examples/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "11976d6a0718404abce925e9dd877dd3",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Map:   0%|          | 0/153 [00:00<?, ? examples/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "Dataset({\n",
       "    features: ['prompt', 'input_ids', 'attention_mask'],\n",
       "    num_rows: 50\n",
       "})"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from datasets import load_dataset\n",
    "dataset = \"fka/awesome-chatgpt-prompts\"\n",
    "\n",
    "#Create the Dataset to create prompts.\n",
    "data = load_dataset(dataset)\n",
    "\n",
    "data = data.map(lambda samples: tokenizer(samples[\"prompt\"]), batched=True)\n",
    "train_sample = data[\"train\"].select(range(50))\n",
    "\n",
    "del data\n",
    "train_sample = train_sample.remove_columns('act')\n",
    "\n",
    "display(train_sample)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "gmlZY3fk_9fm",
    "outputId": "c9806e7f-905c-4273-feaf-5de864f41967"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'prompt': ['I want you to act as a linux terminal. I will type commands and you will reply with what the terminal should show. I want you to only reply with the terminal output inside one unique code block, and nothing else. do not write explanations. do not type commands unless I instruct you to do so. when i need to tell you something in english, i will do so by putting text inside curly brackets {like this}. my first command is pwd'], 'input_ids': [[128000, 40, 1390, 499, 311, 1180, 439, 264, 37345, 15372, 13, 358, 690, 955, 11545, 323, 499, 690, 10052, 449, 1148, 279, 15372, 1288, 1501, 13, 358, 1390, 499, 311, 1193, 10052, 449, 279, 15372, 2612, 4871, 832, 5016, 2082, 2565, 11, 323, 4400, 775, 13, 656, 539, 3350, 41941, 13, 656, 539, 955, 11545, 7389, 358, 21745, 499, 311, 656, 779, 13, 994, 602, 1205, 311, 3371, 499, 2555, 304, 30063, 11, 602, 690, 656, 779, 555, 10917, 1495, 4871, 69203, 40029, 314, 4908, 420, 7966, 856, 1176, 3290, 374, 33806]], 'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]}\n"
     ]
    }
   ],
   "source": [
    "print(train_sample[:1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "oVPAJsrUAHiJ"
   },
   "source": [
    "## Fine-Tuning.\n",
    "The first step will be to create a LoRA configuration object where we will set the variables that specify the characteristics of the fine-tuning process."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "id": "uCalslQFGL7K"
   },
   "outputs": [],
   "source": [
    "# TARGET_MODULES\n",
    "# https://github.com/huggingface/peft/blob/39ef2546d5d9b8f5f8a7016ec10657887a867041/src/peft/utils/other.py#L220\n",
    "\n",
    "import peft\n",
    "from peft import LoraConfig, get_peft_model\n",
    "\n",
    "lora_config = LoraConfig(\n",
    "    r=16, #As bigger the R bigger the parameters to train.\n",
    "    lora_alpha=16, # a scaling factor that adjusts the magnitude of the weight matrix. It seems that as higher more weight have the new training.\n",
    "    target_modules=target_modules,\n",
    "    lora_dropout=0.05, #Helps to avoid Overfitting.\n",
    "    bias=\"none\", # this specifies if the bias parameter should be trained.\n",
    "    task_type=\"CAUSAL_LM\"\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "LUddynl0B1Ck"
   },
   "source": [
    "The most important parameter is **r**, it defines how many parameters will be trained. As bigger the value more parameters are trained, but it means that the model will be able to learn more complicated relations between inputs and outputs.\n",
    "\n",
    "Yo can find a list of the **target_modules** available on the [Hugging Face Documentation]( https://github.com/huggingface/peft/blob/39ef2546d5d9b8f5f8a7016ec10657887a867041/src/peft/utils/other.py#L220)\n",
    "\n",
    "**lora_alpha**. Ad bigger the number more weight have the LoRA activations, it means that the fine-tuning process will have more impac as bigger is this value.\n",
    "\n",
    "**lora_dropout** is like the commom dropout is used to avoid overfitting.\n",
    "\n",
    "**bias** I was hesitating if use *none* or *lora_only*. For text classification the most common value is none, and for chat or question answering, *all* or *lora_only*.\n",
    "\n",
    "**task_type**. Indicates the task the model is beign trained for. In this case, text generation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "id": "HArPQ_lvGUkY"
   },
   "outputs": [],
   "source": [
    "#Create a directory to contain the Model\n",
    "import os\n",
    "working_dir = './'\n",
    "\n",
    "output_directory = os.path.join(working_dir, \"peft_lab_outputs\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "rWalmqWm4STo"
   },
   "source": [
    "In the TrainingArgs we inform the number of epochs we want to train, the output directory and the learning_rate."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "id": "ND0aJ-t6ARqD"
   },
   "outputs": [],
   "source": [
    "#Creating the TrainingArgs\n",
    "import transformers\n",
    "from transformers import TrainingArguments # , Trainer\n",
    "training_args = TrainingArguments(\n",
    "    output_dir=output_directory,\n",
    "    auto_find_batch_size=True, # Find a correct bvatch size that fits the size of Data.\n",
    "    learning_rate= 2e-4, # Higher learning rate than full fine-tuning.\n",
    "    num_train_epochs=5\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "OgxsV-iy_J_o"
   },
   "source": [
    "Now we can train the model.\n",
    "To train the model we need:\n",
    "\n",
    "\n",
    "*   The Model.\n",
    "*   The training_args\n",
    "* The Dataset\n",
    "* The result of DataCollator, the Dataset ready to be procesed in blocks.\n",
    "* The LoRA config.\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 182
    },
    "id": "z5NYHqBnGZyF",
    "outputId": "9f065a2b-80c8-4696-9745-65b40179fe03"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.10/dist-packages/trl/trainer/sft_trainer.py:246: UserWarning: You didn't pass a `max_seq_length` argument to the SFTTrainer, this will default to 1024\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "\n",
       "    <div>\n",
       "      \n",
       "      <progress value='65' max='65' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
       "      [65/65 06:31, Epoch 5/5]\n",
       "    </div>\n",
       "    <table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       " <tr style=\"text-align: left;\">\n",
       "      <th>Step</th>\n",
       "      <th>Training Loss</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table><p>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "TrainOutput(global_step=65, training_loss=1.8513622577373798, metrics={'train_runtime': 398.1347, 'train_samples_per_second': 0.628, 'train_steps_per_second': 0.163, 'total_flos': 1178592071270400.0, 'train_loss': 1.8513622577373798, 'epoch': 5.0})"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tokenizer.pad_token = tokenizer.eos_token\n",
    "trainer = SFTTrainer(\n",
    "    model=foundation_model,\n",
    "    args=training_args,\n",
    "    train_dataset=train_sample,\n",
    "    peft_config = lora_config,\n",
    "    dataset_text_field=\"prompt\",\n",
    "    tokenizer=tokenizer,\n",
    "    data_collator=transformers.DataCollatorForLanguageModeling(tokenizer, mlm=False)\n",
    ")\n",
    "trainer.train()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "id": "kEKiFdpDGgOx"
   },
   "outputs": [],
   "source": [
    "#Save the model.\n",
    "peft_model_path = os.path.join(output_directory, f\"lora_model\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "id": "s6Hmy87-HViP"
   },
   "outputs": [],
   "source": [
    "#Save the model.\n",
    "trainer.model.save_pretrained(peft_model_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "glDE0Z4FOSe0",
    "outputId": "708d5014-9f84-46fa-e6dc-2e2042c6d927"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "265"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#In case you are having memory problems uncomment this lines to free some memory\n",
    "import gc\n",
    "import torch\n",
    "del foundation_model\n",
    "del trainer\n",
    "del train_sample\n",
    "torch.cuda.empty_cache()\n",
    "gc.collect()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "UOAqEg0mSjHW"
   },
   "source": [
    "## Inference with the pretrained model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "id": "dX5d8xMCSC6y"
   },
   "outputs": [],
   "source": [
    "#import peft\n",
    "from peft import AutoPeftModelForCausalLM, PeftConfig\n",
    "#import os\n",
    "\n",
    "device_map = {\"\": 0}\n",
    "working_dir = './'\n",
    "\n",
    "output_directory = os.path.join(working_dir, \"peft_lab_outputs\")\n",
    "peft_model_path = os.path.join(output_directory, f\"lora_model\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "id": "CA-E-io_Dfe1"
   },
   "outputs": [],
   "source": [
    "bnb_config2 = BitsAndBytesConfig(\n",
    "    load_in_4bit=True,\n",
    "    bnb_4bit_use_double_quant=True,\n",
    "    bnb_4bit_quant_type=\"nf4\",\n",
    "    bnb_4bit_compute_dtype=torch.bfloat16\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "colab": {
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    "#Load the Model.\n",
    "loaded_model = AutoPeftModelForCausalLM.from_pretrained(\n",
    "                                        peft_model_path,\n",
    "                                        #torch_dtype=torch.bfloat16,\n",
    "                                        is_trainable=False,\n",
    "                                        #load_in_4bit=True,\n",
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    "## Inference the fine-tuned model."
   ]
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      "Setting `pad_token_id` to `eos_token_id`:128001 for open-end generation.\n"
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      "['I want you to act as a motivational coach.  I will provide some information about an individual or group of people who need motivation, and your role is help them find the inspiration they require in order achieve their goals successfully! You can use techniques such as positive reinforcement, visualization exercises etc., depending on what']\n"
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    "foundational_outputs_sentence = get_outputs(loaded_model, input_sentences, max_new_tokens=50)\n",
    "\n",
    "print(tokenizer.batch_decode(foundational_outputs_sentence, skip_special_tokens=True))"
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    "The result is really good. Let's compare the answer of the pre-trained model with the fine-tuned one:\n",
    "\n",
    "* **Pretrained Model**: 'I want you to act as a motivational coach. \\xa0You are going on an adventure with me, and I need your help.\\nWe will be traveling through the land of “What If.” \\xa0 This is not some place that exists in reality; it’s more like one those places we see when watching'\n",
    "\n",
    "* **Fine-Tuned Model**: 'I want you to act as a motivational coach.  I will provide some information about an individual or group of people who need motivation, and your role is help them find the inspiration they require in order achieve their goals successfully! You can use techniques such as positive reinforcement, visualization exercises etc., depending on what'\n",
    "\n",
    "As you can see, the result is really similar to the samples contained in the dataset used to fine-tune the model. And we only trained the model for some epochs and with a really small number of rows.\n",
    "\n",
    "# Continue Learning\n",
    "Please, play with all the variables in the notebook and drive your own experiments and get your conclusions. Changes in the ***lora_alpha*** can have a big effect in the results.\n",
    "\n",
    "You can change the rest of **lora_config** values, maybe you can achieve a better result in less epochs, saving time and money in your projects.\n",
    "\n"
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