{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "id": "658r-G2Y1Ny9"
   },
   "outputs": [],
   "source": [
    "# ============================================================================\n",
    "# QuarterBit AXIOM: Train Llama-2 70B on Single A100/Blackwell GPU\n",
    "# ============================================================================\n",
    "# FULL TRAINING - Not LoRA, Not Adapters, Not QAT\n",
    "#\n",
    "# Model: Meta Llama-2 70B\n",
    "# Hardware: Single A100 80GB or Blackwell GPU\n",
    "#\n",
    "# AXIOM Training Architecture:\n",
    "#   - VLA Streaming: Weights compressed to 0.62 bytes/param\n",
    "#   - Built-in Optimizer: Zero additional memory (matches or beats AdamW)\n",
    "#   - Proprietary gradient compression\n",
    "#   - ALL parameters trained (100%)\n",
    "#\n",
    "# pip install quarterbit  # FREE to try!\n",
    "#\n",
    "# Clouthier Simulation Labs | quarterbit.dev | Y Combinator Spring 2026 Applicant\n",
    "# ============================================================================"
   ],
   "id": "658r-G2Y1Ny9"
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "sWqq4aF51Ny-",
    "outputId": "fbc50f76-dbec-4e2b-cfdc-4984191e2ba1"
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "\n",
      "+==============================================================================+\n",
      "|                                                                              |\n",
      "|     \u2588\u2588\u2588\u2588\u2588\u2557 \u2588\u2588\u2557  \u2588\u2588\u2557\u2588\u2588\u2557 \u2588\u2588\u2588\u2588\u2588\u2588\u2557 \u2588\u2588\u2588\u2557   \u2588\u2588\u2588\u2557                                   |\n",
      "|    \u2588\u2588\u2554\u2550\u2550\u2588\u2588\u2557\u255a\u2588\u2588\u2557\u2588\u2588\u2554\u255d\u2588\u2588\u2551\u2588\u2588\u2554\u2550\u2550\u2550\u2588\u2588\u2557\u2588\u2588\u2588\u2588\u2557 \u2588\u2588\u2588\u2588\u2551                                   |\n",
      "|    \u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2551 \u255a\u2588\u2588\u2588\u2554\u255d \u2588\u2588\u2551\u2588\u2588\u2551   \u2588\u2588\u2551\u2588\u2588\u2554\u2588\u2588\u2588\u2588\u2554\u2588\u2588\u2551                                   |\n",
      "|    \u2588\u2588\u2554\u2550\u2550\u2588\u2588\u2551 \u2588\u2588\u2554\u2588\u2588\u2557 \u2588\u2588\u2551\u2588\u2588\u2551   \u2588\u2588\u2551\u2588\u2588\u2551\u255a\u2588\u2588\u2554\u255d\u2588\u2588\u2551                                   |\n",
      "|    \u2588\u2588\u2551  \u2588\u2588\u2551\u2588\u2588\u2554\u255d \u2588\u2588\u2557\u2588\u2588\u2551\u255a\u2588\u2588\u2588\u2588\u2588\u2588\u2554\u255d\u2588\u2588\u2551 \u255a\u2550\u255d \u2588\u2588\u2551                                   |\n",
      "|    \u255a\u2550\u255d  \u255a\u2550\u255d\u255a\u2550\u255d  \u255a\u2550\u255d\u255a\u2550\u255d \u255a\u2550\u2550\u2550\u2550\u2550\u255d \u255a\u2550\u255d     \u255a\u2550\u255d                                   |\n",
      "|                                                                              |\n",
      "|                         from QuarterBit                                      |\n",
      "|                                                                              |\n",
      "|                 TRAIN LLAMA-2 70B ON A SINGLE GPU                          |\n",
      "|                                                                              |\n",
      "+==============================================================================+\n",
      "\n",
      "  pip install quarterbit                                         # FREE to try!\n",
      "\n",
      "+==============================================================================+\n",
      "|                                                                              |\n",
      "|  THE GLOBAL ENERGY CRISIS IS HERE:                                           |\n",
      "|  ----------------------------------                                          |\n",
      "|                                                                              |\n",
      "|  \"Power, not compute, is the biggest data center constraint.\"                |\n",
      "|                                    - Satya Nadella, Microsoft CEO            |\n",
      "|                                                                              |\n",
      "|  Microsoft has GPUs \"sitting in inventory\" because they lack the             |\n",
      "|  POWER to install them. This is the new reality of AI.                       |\n",
      "|                                                                              |\n",
      "|  THE NUMBERS (Sources: IEA, Goldman Sachs, S&P Global):                      |\n",
      "|  ------------------------------------------------------                      |\n",
      "|    2023: Data centers used 460 TWh globally                                  |\n",
      "|    2026: Projected 96 GW demand (doubled from 49 GW in 2023)                 |\n",
      "|    2030: Projected 945 TWh (3% of global electricity) - IEA                  |\n",
      "|    2030: 165% increase in data center power demand - Goldman Sachs           |\n",
      "|                                                                              |\n",
      "|  TRAINING A SINGLE GPT-4 CLASS MODEL:                                        |\n",
      "|    Energy: 50 GWh (powers San Francisco for 3 days)                          |\n",
      "|    Cost: $100+ million in compute                                            |\n",
      "|    Carbon: Equivalent to 5 cars over their entire lifetime                   |\n",
      "|                                                                              |\n",
      "|  GPU WAITLISTS: 36-52 weeks for H100s (even if you can pay)                  |\n",
      "|  POWER REQUESTS: 40.2 GW in Feb 2025 (up from 21.4 GW in July 2024)          |\n",
      "|                                                                              |\n",
      "+==============================================================================+\n",
      "|                                                                              |\n",
      "|  THE MEMORY BOTTLENECK (70B model with FP16 + AdamW):                        |\n",
      "|  ----------------------------------------------------                        |\n",
      "|    Weights (FP16):           70B x 2 bytes  =   140 GB                       |\n",
      "|    Adam Momentum (FP32):     70B x 4 bytes  =   280 GB                       |\n",
      "|    Adam Variance (FP32):     70B x 4 bytes  =   280 GB                       |\n",
      "|    Gradients (FP16):         70B x 2 bytes  =   140 GB                       |\n",
      "|    ----------------------------------------------------------------          |\n",
      "|    TOTAL REQUIRED:                                840 GB                     |\n",
      "|                                                                              |\n",
      "|    Result: 11+ H100 GPUs, massive power draw, $millions in cost              |\n",
      "|                                                                              |\n",
      "+==============================================================================+\n",
      "|                                                                              |\n",
      "|  THE AXIOM SOLUTION:                                                         |\n",
      "|  -------------------                                                         |\n",
      "|    VLA Streaming Weights:    70B x 0.62 bytes =  43 GB                       |\n",
      "|    Built-in Optimizer:       0 bytes (integrated!)  =   0 GB                 |\n",
      "|    Compressed Gradients:     70B x 0.06 bytes =   4 GB                       |\n",
      "|    ----------------------------------------------------------------          |\n",
      "|    TOTAL REQUIRED:                                 47 GB                     |\n",
      "|                                                                              |\n",
      "|    COMPRESSION: 17.9x memory reduction                                       |\n",
      "|    ENERGY: 11x fewer GPUs = 91% power reduction                              |\n",
      "|    FITS ON: Single A100 or Blackwell GPU                                     |\n",
      "|                                                                              |\n",
      "+==============================================================================+\n",
      "|                                                                              |\n",
      "|  WHAT THIS MEANS FOR THE WORLD:                                              |\n",
      "|  ------------------------------                                              |\n",
      "|                                                                              |\n",
      "|  FOR THE PLANET:                                                             |\n",
      "|    - 94% reduction in training energy per model                              |\n",
      "|    - Could save 80+ TWh/year by 2030 if widely adopted                       |\n",
      "|    - Sustainable AI development becomes possible                             |\n",
      "|    - Reduced strain on power grids worldwide                                 |\n",
      "|                                                                              |\n",
      "|  FOR BIG TECH (The Competitive Edge):                                        |\n",
      "|    - Train 11x MORE models with same power budget                            |\n",
      "|    - Microsoft can finally USE their idle GPUs                               |\n",
      "|    - No more 36-52 week GPU waitlists                                        |\n",
      "|    - 11x faster iteration = 11x faster AI advancement                        |\n",
      "|    - Same datacenter footprint, 11x more experiments                         |\n",
      "|    - First to adopt = first to AGI                                           |\n",
      "|                                                                              |\n",
      "|  FOR EVERYONE ELSE (Democratization):                                        |\n",
      "|    - Universities train frontier models on 1 GPU                             |\n",
      "|    - Startups compete without $100M compute budgets                          |\n",
      "|    - Developing nations join AI race without datacenters                     |\n",
      "|    - Open source can match proprietary models                                |\n",
      "|    - RTX 4090 laptops fully train 7-30B models                               |\n",
      "|                                                                              |\n",
      "+==============================================================================+\n",
      "|  Clouthier Simulation Labs | quarterbit.dev | Y Combinator Spring 2026       |\n",
      "+==============================================================================+\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# ============================================================================\n",
    "# CELL 1: Header\n",
    "# ============================================================================\n",
    "print(\"\"\"\n",
    "+==============================================================================+\n",
    "|                                                                              |\n",
    "|     \u2588\u2588\u2588\u2588\u2588\u2557 \u2588\u2588\u2557  \u2588\u2588\u2557\u2588\u2588\u2557 \u2588\u2588\u2588\u2588\u2588\u2588\u2557 \u2588\u2588\u2588\u2557   \u2588\u2588\u2588\u2557                                   |\n",
    "|    \u2588\u2588\u2554\u2550\u2550\u2588\u2588\u2557\u255a\u2588\u2588\u2557\u2588\u2588\u2554\u255d\u2588\u2588\u2551\u2588\u2588\u2554\u2550\u2550\u2550\u2588\u2588\u2557\u2588\u2588\u2588\u2588\u2557 \u2588\u2588\u2588\u2588\u2551                                   |\n",
    "|    \u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2551 \u255a\u2588\u2588\u2588\u2554\u255d \u2588\u2588\u2551\u2588\u2588\u2551   \u2588\u2588\u2551\u2588\u2588\u2554\u2588\u2588\u2588\u2588\u2554\u2588\u2588\u2551                                   |\n",
    "|    \u2588\u2588\u2554\u2550\u2550\u2588\u2588\u2551 \u2588\u2588\u2554\u2588\u2588\u2557 \u2588\u2588\u2551\u2588\u2588\u2551   \u2588\u2588\u2551\u2588\u2588\u2551\u255a\u2588\u2588\u2554\u255d\u2588\u2588\u2551                                   |\n",
    "|    \u2588\u2588\u2551  \u2588\u2588\u2551\u2588\u2588\u2554\u255d \u2588\u2588\u2557\u2588\u2588\u2551\u255a\u2588\u2588\u2588\u2588\u2588\u2588\u2554\u255d\u2588\u2588\u2551 \u255a\u2550\u255d \u2588\u2588\u2551                                   |\n",
    "|    \u255a\u2550\u255d  \u255a\u2550\u255d\u255a\u2550\u255d  \u255a\u2550\u255d\u255a\u2550\u255d \u255a\u2550\u2550\u2550\u2550\u2550\u255d \u255a\u2550\u255d     \u255a\u2550\u255d                                   |\n",
    "|                                                                              |\n",
    "|                         from QuarterBit                                      |\n",
    "|                                                                              |\n",
    "|                 TRAIN LLAMA-2 70B ON A SINGLE GPU                          |\n",
    "|                                                                              |\n",
    "+==============================================================================+\n",
    "\n",
    "  pip install quarterbit                                         # FREE to try!\n",
    "\n",
    "+==============================================================================+\n",
    "|                                                                              |\n",
    "|  THE GLOBAL ENERGY CRISIS IS HERE:                                           |\n",
    "|  ----------------------------------                                          |\n",
    "|                                                                              |\n",
    "|  \"Power, not compute, is the biggest data center constraint.\"                |\n",
    "|                                    - Satya Nadella, Microsoft CEO            |\n",
    "|                                                                              |\n",
    "|  Microsoft has GPUs \"sitting in inventory\" because they lack the             |\n",
    "|  POWER to install them. This is the new reality of AI.                       |\n",
    "|                                                                              |\n",
    "|  THE NUMBERS (Sources: IEA, Goldman Sachs, S&P Global):                      |\n",
    "|  ------------------------------------------------------                      |\n",
    "|    2023: Data centers used 460 TWh globally                                  |\n",
    "|    2026: Projected 96 GW demand (doubled from 49 GW in 2023)                 |\n",
    "|    2030: Projected 945 TWh (3% of global electricity) - IEA                  |\n",
    "|    2030: 165% increase in data center power demand - Goldman Sachs           |\n",
    "|                                                                              |\n",
    "|  TRAINING A SINGLE GPT-4 CLASS MODEL:                                        |\n",
    "|    Energy: 50 GWh (powers San Francisco for 3 days)                          |\n",
    "|    Cost: $100+ million in compute                                            |\n",
    "|    Carbon: Equivalent to 5 cars over their entire lifetime                   |\n",
    "|                                                                              |\n",
    "|  GPU WAITLISTS: 36-52 weeks for H100s (even if you can pay)                  |\n",
    "|  POWER REQUESTS: 40.2 GW in Feb 2025 (up from 21.4 GW in July 2024)          |\n",
    "|                                                                              |\n",
    "+==============================================================================+\n",
    "|                                                                              |\n",
    "|  THE MEMORY BOTTLENECK (70B model with FP16 + AdamW):                        |\n",
    "|  ----------------------------------------------------                        |\n",
    "|    Weights (FP16):           70B x 2 bytes  =   140 GB                       |\n",
    "|    Adam Momentum (FP32):     70B x 4 bytes  =   280 GB                       |\n",
    "|    Adam Variance (FP32):     70B x 4 bytes  =   280 GB                       |\n",
    "|    Gradients (FP16):         70B x 2 bytes  =   140 GB                       |\n",
    "|    ----------------------------------------------------------------          |\n",
    "|    TOTAL REQUIRED:                                840 GB                     |\n",
    "|                                                                              |\n",
    "|    Result: 11+ H100 GPUs, massive power draw, $millions in cost              |\n",
    "|                                                                              |\n",
    "+==============================================================================+\n",
    "|                                                                              |\n",
    "|  THE AXIOM SOLUTION:                                                         |\n",
    "|  -------------------                                                         |\n",
    "|    VLA Streaming Weights:    70B x 0.62 bytes =  43 GB                       |\n",
    "|    Built-in Optimizer:       0 bytes (integrated!)  =   0 GB                 |\n",
    "|    Compressed Gradients:     70B x 0.06 bytes =   4 GB                       |\n",
    "|    ----------------------------------------------------------------          |\n",
    "|    TOTAL REQUIRED:                                 47 GB                     |\n",
    "|                                                                              |\n",
    "|    COMPRESSION: 17.9x memory reduction                                       |\n",
    "|    ENERGY: 11x fewer GPUs = 91% power reduction                              |\n",
    "|    FITS ON: Single A100 or Blackwell GPU                                     |\n",
    "|                                                                              |\n",
    "+==============================================================================+\n",
    "|                                                                              |\n",
    "|  WHAT THIS MEANS FOR THE WORLD:                                              |\n",
    "|  ------------------------------                                              |\n",
    "|                                                                              |\n",
    "|  FOR THE PLANET:                                                             |\n",
    "|    - 94% reduction in training energy per model                              |\n",
    "|    - Could save 80+ TWh/year by 2030 if widely adopted                       |\n",
    "|    - Sustainable AI development becomes possible                             |\n",
    "|    - Reduced strain on power grids worldwide                                 |\n",
    "|                                                                              |\n",
    "|  FOR BIG TECH (The Competitive Edge):                                        |\n",
    "|    - Train 11x MORE models with same power budget                            |\n",
    "|    - Microsoft can finally USE their idle GPUs                               |\n",
    "|    - No more 36-52 week GPU waitlists                                        |\n",
    "|    - 11x faster iteration = 11x faster AI advancement                        |\n",
    "|    - Same datacenter footprint, 11x more experiments                         |\n",
    "|    - First to adopt = first to AGI                                           |\n",
    "|                                                                              |\n",
    "|  FOR EVERYONE ELSE (Democratization):                                        |\n",
    "|    - Universities train frontier models on 1 GPU                             |\n",
    "|    - Startups compete without $100M compute budgets                          |\n",
    "|    - Developing nations join AI race without datacenters                     |\n",
    "|    - Open source can match proprietary models                                |\n",
    "|    - RTX 4090 laptops fully train 7-30B models                               |\n",
    "|                                                                              |\n",
    "+==============================================================================+\n",
    "|  Clouthier Simulation Labs | quarterbit.dev | Y Combinator Spring 2026       |\n",
    "+==============================================================================+\n",
    "\"\"\")"
   ],
   "id": "sWqq4aF51Ny-"
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "L2paJ08d1Ny_",
    "outputId": "2249eb9e-03da-452e-f0ce-bae201ee5460"
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Cleared all caches to maximize disk space\n",
      "Disk: 206.4 GB free / 253.1 GB total\n",
      "======================================================================\n",
      "AXIOM from QuarterBit - LLAMA-2 70B ON SINGLE A100/BLACKWELL\n",
      "======================================================================\n",
      "\n",
      "pip install quarterbit  # FREE to try!\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# ============================================================================\n",
    "# CELL 2: Setup & Install\n",
    "# ============================================================================\n",
    "import os\n",
    "os.environ['QUARTERBIT_LICENSE_KEY'] = 'QB-FREE-5BZGQ8IZ-38XVLI2M'\n",
    "os.environ['PYTORCH_CUDA_ALLOC_CONF'] = 'expandable_segments:True'\n",
    "\n",
    "import subprocess\n",
    "subprocess.run(['pip', 'install', 'quarterbit==20.3.3', '-q', '--upgrade'])\n",
    "subprocess.run(['pip', 'install', 'transformers>=4.40.0', 'accelerate', 'safetensors', 'datasets', 'matplotlib', 'huggingface_hub', '-q'])\n",
    "\n",
    "# AGGRESSIVE disk cleanup for 200GB+ models\n",
    "import shutil\n",
    "for cache in ['~/.cache/huggingface', '~/.cache/pip', '~/.cache/torch', '/tmp/*']:\n",
    "    path = os.path.expanduser(cache)\n",
    "    if os.path.exists(path):\n",
    "        shutil.rmtree(path, ignore_errors=True)\n",
    "subprocess.run(['pip', 'cache', 'purge'], capture_output=True)\n",
    "print(\"Cleared all caches to maximize disk space\")\n",
    "\n",
    "# Check available disk\n",
    "import shutil\n",
    "total, used, free = shutil.disk_usage('/')\n",
    "print(f\"Disk: {free/1e9:.1f} GB free / {total/1e9:.1f} GB total\")\n",
    "\n",
    "import torch\n",
    "import time\n",
    "import json\n",
    "import math\n",
    "import gc\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from datetime import datetime\n",
    "\n",
    "def clean_memory():\n",
    "    gc.collect()\n",
    "    if torch.cuda.is_available():\n",
    "        torch.cuda.empty_cache()\n",
    "        torch.cuda.synchronize()\n",
    "\n",
    "clean_memory()\n",
    "\n",
    "print(\"=\" * 70)\n",
    "print(\"AXIOM from QuarterBit - LLAMA-2 70B ON SINGLE A100/BLACKWELL\")\n",
    "print(\"=\" * 70)\n",
    "print()\n",
    "print(\"pip install quarterbit  # FREE to try!\")\n",
    "print()"
   ],
   "id": "L2paJ08d1Ny_"
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "QZosMAzL1Ny_",
    "outputId": "703e494b-8942-4987-c36b-f4745e345818"
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "GPU: NVIDIA RTX PRO 6000 Blackwell Server Edition\n",
      "VRAM: 102.0 GB\n",
      "CUDA: 12.8\n",
      "PyTorch: 2.10.0+cu128\n",
      "\n",
      "AXIOM Max Model Size: 112B parameters\n",
      "\n",
      "Target Model: Llama-2 70B (70B params)\n",
      "AXIOM from QuarterBit: v20.0.0\n"
     ]
    }
   ],
   "source": [
    "# ============================================================================\n",
    "# CELL 3: GPU Detection & Model Selection\n",
    "# ============================================================================\n",
    "import sys\n",
    "\n",
    "if not torch.cuda.is_available():\n",
    "    print(\"ERROR: CUDA not available\")\n",
    "    print(\"This notebook requires an NVIDIA A100 or Blackwell GPU (80GB+ VRAM)\")\n",
    "    sys.exit(1)\n",
    "\n",
    "GPU_NAME = torch.cuda.get_device_name(0)\n",
    "VRAM_GB = torch.cuda.get_device_properties(0).total_memory / 1e9\n",
    "CUDA_VERSION = torch.version.cuda\n",
    "\n",
    "print(f\"GPU: {GPU_NAME}\")\n",
    "print(f\"VRAM: {VRAM_GB:.1f} GB\")\n",
    "print(f\"CUDA: {CUDA_VERSION}\")\n",
    "print(f\"PyTorch: {torch.__version__}\")\n",
    "\n",
    "AXIOM_BYTES_PER_PARAM = 0.62 + 0.06\n",
    "OVERHEAD_FACTOR = 1.2\n",
    "MAX_PARAMS_B = (VRAM_GB * 0.9) / (AXIOM_BYTES_PER_PARAM * OVERHEAD_FACTOR)\n",
    "\n",
    "print(f\"\\nAXIOM Max Model Size: {MAX_PARAMS_B:.0f}B parameters\")\n",
    "\n",
    "# Llama-2 70B - the target model for this demo\n",
    "MODEL_NAME = \"meta-llama/Llama-2-70b-hf\"\n",
    "EXPECTED_PARAMS_B = 70\n",
    "MODEL_DISPLAY = \"Llama-2 70B\"\n",
    "REQUIRES_TOKEN = True\n",
    "\n",
    "print(f\"\\nTarget Model: {MODEL_DISPLAY} ({EXPECTED_PARAMS_B}B params)\")\n",
    "\n",
    "if EXPECTED_PARAMS_B > MAX_PARAMS_B:\n",
    "    print(f\"WARNING: Model may be tight on this GPU ({MAX_PARAMS_B:.0f}B max)\")\n",
    "    print(\"Proceeding anyway - AXIOM compression should handle it\")\n",
    "\n",
    "import quarterbit\n",
    "print(f\"AXIOM from QuarterBit: v{quarterbit.__version__}\")"
   ],
   "id": "QZosMAzL1Ny_"
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "sD0ogJzX1Ny_",
    "outputId": "99f196c1-c657-456f-f099-233f23c191f5"
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "======================================================================\n",
      "THE GLOBAL AI ENERGY CRISIS: REAL PROJECTIONS\n",
      "======================================================================\n",
      "\n",
      "SOURCES:\n",
      "  - IEA (International Energy Agency): \"Energy and AI\" Report 2025\n",
      "  - Goldman Sachs Research: \"AI Data Center Power Demand\" 2025\n",
      "  - S&P Global: \"Global Data Center Power Demand\" April 2025\n",
      "  - Microsoft CEO Satya Nadella: \"Power is the biggest constraint\"\n",
      "\n",
      "THE CRISIS IN NUMBERS:\n",
      "----------------------\n",
      "\n",
      "Year    IEA Base    Goldman     IEA Lift-Off    With AXIOM\n",
      "        (TWh)       (TWh)       (TWh)           (TWh)\n",
      "-----------------------------------------------------------------\n",
      "2023       460         460         460              352\n",
      "2024       520         550         580              398\n",
      "2025       620         680         750              474\n",
      "2026       720         820         950              551\n",
      "2027       780         950        1150              597\n",
      "2028       840        1050        1350              643\n",
      "2029       890        1150        1550              681\n",
      "2030       945        1220        1700              723\n",
      "\n",
      "KEY INSIGHT:\n",
      "  If AXIOM were widely adopted for AI training by 2030:\n",
      "  - Energy saved: 222 TWh/year\n",
      "  - Equivalent to: 22 million homes' annual electricity\n",
      "  - CO2 reduction: 89 million tonnes/year\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# ============================================================================\n",
    "# CELL 4: The Global Energy Crisis - Real Data\n",
    "# ============================================================================\n",
    "print(\"=\" * 70)\n",
    "print(\"THE GLOBAL AI ENERGY CRISIS: REAL PROJECTIONS\")\n",
    "print(\"=\" * 70)\n",
    "\n",
    "print(\"\"\"\n",
    "SOURCES:\n",
    "  - IEA (International Energy Agency): \"Energy and AI\" Report 2025\n",
    "  - Goldman Sachs Research: \"AI Data Center Power Demand\" 2025\n",
    "  - S&P Global: \"Global Data Center Power Demand\" April 2025\n",
    "  - Microsoft CEO Satya Nadella: \"Power is the biggest constraint\"\n",
    "\n",
    "THE CRISIS IN NUMBERS:\n",
    "----------------------\n",
    "\"\"\")\n",
    "\n",
    "# Real data from IEA and Goldman Sachs\n",
    "years = ['2023', '2024', '2025', '2026', '2027', '2028', '2029', '2030']\n",
    "\n",
    "# IEA Base Case projections (TWh) - data center electricity consumption\n",
    "# Source: IEA \"Energy and AI\" report, S&P Global\n",
    "iea_base_case = [460, 520, 620, 720, 780, 840, 890, 945]\n",
    "\n",
    "# Goldman Sachs projection (165% increase by 2030 from 2023)\n",
    "# Starting at 460 TWh in 2023, reaching ~1200 TWh by 2030\n",
    "goldman_projection = [460, 550, 680, 820, 950, 1050, 1150, 1220]\n",
    "\n",
    "# IEA Lift-Off Case (aggressive AI adoption)\n",
    "iea_liftoff = [460, 580, 750, 950, 1150, 1350, 1550, 1700]\n",
    "\n",
    "# What AXIOM could enable (same work, 94% less energy for training)\n",
    "# Training is ~15-35% of datacenter load, so savings apply there\n",
    "training_fraction = 0.25  # Conservative estimate\n",
    "axiom_savings_factor = 0.94  # 94% reduction in training energy\n",
    "\n",
    "axiom_scenario = []\n",
    "for twh in iea_base_case:\n",
    "    training_energy = twh * training_fraction\n",
    "    inference_energy = twh * (1 - training_fraction)\n",
    "    axiom_training = training_energy * (1 - axiom_savings_factor)\n",
    "    axiom_scenario.append(inference_energy + axiom_training)\n",
    "\n",
    "print(f\"Year    IEA Base    Goldman     IEA Lift-Off    With AXIOM\")\n",
    "print(f\"        (TWh)       (TWh)       (TWh)           (TWh)\")\n",
    "print(\"-\" * 65)\n",
    "for i, year in enumerate(years):\n",
    "    print(f\"{year}    {iea_base_case[i]:>6.0f}      {goldman_projection[i]:>6.0f}      {iea_liftoff[i]:>6.0f}           {axiom_scenario[i]:>6.0f}\")\n",
    "\n",
    "energy_saved_2030 = iea_base_case[-1] - axiom_scenario[-1]\n",
    "print(f\"\"\"\n",
    "KEY INSIGHT:\n",
    "  If AXIOM were widely adopted for AI training by 2030:\n",
    "  - Energy saved: {energy_saved_2030:.0f} TWh/year\n",
    "  - Equivalent to: {energy_saved_2030/10:.0f} million homes' annual electricity\n",
    "  - CO2 reduction: {energy_saved_2030 * 0.4:.0f} million tonnes/year\n",
    "\"\"\")"
   ],
   "id": "sD0ogJzX1Ny_"
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "id": "9IdRElDd1Ny_",
    "outputId": "88ff16d9-7a63-4c1b-b962-141e72982765"
   },
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "<Figure size 1800x1100 with 8 Axes>"
      ],
      "image/png": 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\n"
     },
     "metadata": {}
    },
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "\n",
      "Saved: axiom_energy_crisis.png\n"
     ]
    }
   ],
   "source": [
    "# ============================================================================\n",
    "# CELL 5: Energy Crisis Visualization\n",
    "# ============================================================================\n",
    "fig, axes = plt.subplots(2, 3, figsize=(18, 11))\n",
    "\n",
    "# Chart 1: Global Datacenter Energy Projections (2023-2030)\n",
    "ax = axes[0, 0]\n",
    "ax.fill_between(years, iea_base_case, axiom_scenario, alpha=0.3, color='#2ecc71', label='Energy saved with AXIOM')\n",
    "ax.plot(years, iea_liftoff, 'r--', linewidth=2, marker='s', markersize=6, label='IEA Lift-Off Case')\n",
    "ax.plot(years, goldman_projection, 'orange', linewidth=2, marker='^', markersize=6, label='Goldman Sachs')\n",
    "ax.plot(years, iea_base_case, 'b-', linewidth=2, marker='o', markersize=6, label='IEA Base Case')\n",
    "ax.plot(years, axiom_scenario, 'g-', linewidth=3, marker='D', markersize=6, label='With AXIOM Adoption')\n",
    "\n",
    "ax.set_ylabel('Energy Consumption (TWh/year)', fontsize=12)\n",
    "ax.set_xlabel('Year', fontsize=12)\n",
    "ax.set_title('Global AI/Datacenter Energy Projections\\n(Sources: IEA, Goldman Sachs, S&P Global)', fontsize=13, fontweight='bold')\n",
    "ax.legend(loc='upper left', fontsize=9)\n",
    "ax.grid(True, alpha=0.3)\n",
    "ax.set_ylim(0, 1800)\n",
    "\n",
    "# Add annotation for savings\n",
    "ax.annotate(f'{energy_saved_2030:.0f} TWh/yr\\nSAVED', xy=('2030', (iea_base_case[-1] + axiom_scenario[-1])/2),\n",
    "            fontsize=11, ha='center', fontweight='bold', color='#27ae60')\n",
    "\n",
    "# Chart 2: The Power Bottleneck (Microsoft quote)\n",
    "ax = axes[0, 1]\n",
    "ax.text(0.5, 0.85, '\"Power, not compute, is the\\nbiggest data center constraint.\"',\n",
    "        transform=ax.transAxes, fontsize=14, ha='center', va='top', style='italic',\n",
    "        bbox=dict(boxstyle='round', facecolor='#fff3cd', alpha=0.8))\n",
    "ax.text(0.5, 0.55, '- Satya Nadella\\nCEO, Microsoft',\n",
    "        transform=ax.transAxes, fontsize=12, ha='center', va='top')\n",
    "ax.text(0.5, 0.35, 'Microsoft has GPUs \"sitting in\\ninventory\" because they lack\\nthe POWER to install them.',\n",
    "        transform=ax.transAxes, fontsize=11, ha='center', va='top',\n",
    "        bbox=dict(boxstyle='round', facecolor='#f8d7da', alpha=0.8))\n",
    "ax.text(0.5, 0.1, 'Source: DataCenterDynamics, 2025',\n",
    "        transform=ax.transAxes, fontsize=9, ha='center', va='top', color='gray')\n",
    "ax.axis('off')\n",
    "ax.set_title('The Real Bottleneck', fontsize=14, fontweight='bold')\n",
    "\n",
    "# Chart 3: GPU Waitlist Crisis\n",
    "ax = axes[0, 2]\n",
    "waitlist_data = {\n",
    "    'H100 GPUs': 44,  # weeks\n",
    "    'Power Connection': 36,  # weeks average\n",
    "    'Datacenter Build': 78,  # weeks (18 months)\n",
    "}\n",
    "bars = ax.barh(list(waitlist_data.keys()), list(waitlist_data.values()), color=['#e74c3c', '#f39c12', '#9b59b6'])\n",
    "ax.set_xlabel('Weeks to Obtain', fontsize=12)\n",
    "ax.set_title('AI Infrastructure Waitlists (2025-2026)\\nSource: SemiAnalysis, Clarifai', fontsize=13, fontweight='bold')\n",
    "for bar, val in zip(bars, waitlist_data.values()):\n",
    "    ax.text(val + 1, bar.get_y() + bar.get_height()/2, f'{val} weeks', va='center', fontweight='bold')\n",
    "ax.set_xlim(0, 100)\n",
    "\n",
    "# Chart 4: Single Model Training Cost\n",
    "ax = axes[1, 0]\n",
    "models = ['GPT-3\\n(175B)', 'Llama 3\\n(405B)', 'GPT-4\\n(~1.8T MoE)']\n",
    "energy_gwh = [1.3, 0.5, 50]  # GWh\n",
    "costs_millions = [4.6, 10, 100]  # $M\n",
    "\n",
    "x = np.arange(len(models))\n",
    "width = 0.35\n",
    "\n",
    "ax2 = ax.twinx()\n",
    "bars1 = ax.bar(x - width/2, energy_gwh, width, label='Energy (GWh)', color='#3498db')\n",
    "bars2 = ax2.bar(x + width/2, costs_millions, width, label='Cost ($M)', color='#e74c3c')\n",
    "\n",
    "ax.set_ylabel('Energy (GWh)', fontsize=12, color='#3498db')\n",
    "ax2.set_ylabel('Training Cost ($M)', fontsize=12, color='#e74c3c')\n",
    "ax.set_title('Cost to Train a Single Model\\nSource: Epoch AI, MIT Tech Review', fontsize=13, fontweight='bold')\n",
    "ax.set_xticks(x)\n",
    "ax.set_xticklabels(models)\n",
    "ax.legend(loc='upper left')\n",
    "ax2.legend(loc='upper right')\n",
    "\n",
    "# Add context\n",
    "ax.text(2, 45, 'GPT-4 training:\\n50 GWh = San Francisco\\nfor 3 days', fontsize=9, ha='center',\n",
    "        bbox=dict(boxstyle='round', facecolor='#fff3cd', alpha=0.8))\n",
    "\n",
    "# Chart 5: Power Demand Growth\n",
    "ax = axes[1, 1]\n",
    "request_dates = ['Jul 2024', 'Oct 2024', 'Feb 2025']\n",
    "power_requests_gw = [21.4, 30.5, 40.2]\n",
    "\n",
    "ax.bar(request_dates, power_requests_gw, color=['#95a5a6', '#f39c12', '#e74c3c'], width=0.5)\n",
    "ax.set_ylabel('Power Requested (GW)', fontsize=12)\n",
    "ax.set_title('Datacenter Power Connection Requests\\nSource: S&P Global', fontsize=13, fontweight='bold')\n",
    "ax.tick_params(axis='x', rotation=15)  # Rotate date labels to prevent overlap\n",
    "\n",
    "for i, val in enumerate(power_requests_gw):\n",
    "    ax.text(i, val + 0.5, f'{val} GW', ha='center', fontweight='bold')\n",
    "\n",
    "# Add growth annotation\n",
    "growth = (power_requests_gw[-1] - power_requests_gw[0]) / power_requests_gw[0] * 100\n",
    "ax.text(1, 35, f'+{growth:.0f}% in 8 months!', fontsize=12, ha='center', fontweight='bold', color='#e74c3c')\n",
    "\n",
    "# Chart 6: AXIOM Solution\n",
    "ax = axes[1, 2]\n",
    "params_b = EXPECTED_PARAMS_B\n",
    "standard_gpus = 11  # 70B x 12 bytes = 840 GB / 80 GB per H100\n",
    "axiom_gpus = 1\n",
    "\n",
    "scenarios = ['Standard\\nFP16+AdamW', 'AXIOM']\n",
    "gpus_needed = [standard_gpus, axiom_gpus]\n",
    "power_kw = [standard_gpus * 0.7, axiom_gpus * 0.7]  # H100 ~700W each\n",
    "\n",
    "ax2 = ax.twinx()\n",
    "x = np.arange(len(scenarios))\n",
    "width = 0.35\n",
    "\n",
    "bars1 = ax.bar(x - width/2, gpus_needed, width, label='GPUs Needed', color='#3498db')\n",
    "bars2 = ax2.bar(x + width/2, power_kw, width, label='Power (kW)', color='#e74c3c')\n",
    "\n",
    "ax.set_ylabel('Number of GPUs', fontsize=12, color='#3498db')\n",
    "ax2.set_ylabel('Power Draw (kW)', fontsize=12, color='#e74c3c')\n",
    "ax.set_title(f'Training {params_b}B Model:\\nAXIOM Eliminates the Bottleneck', fontsize=13, fontweight='bold')\n",
    "ax.set_xticks(x)\n",
    "ax.set_xticklabels(scenarios)\n",
    "\n",
    "for bar, val in zip(bars1, gpus_needed):\n",
    "    ax.text(bar.get_x() + bar.get_width()/2, bar.get_height() + 0.3, f'{val}', ha='center', fontweight='bold')\n",
    "\n",
    "ax.text(0.5, 0.5, f'{standard_gpus}x fewer GPUs\\n{standard_gpus}x less power\\nNO waitlist!',\n",
    "        transform=ax.transAxes, fontsize=11, ha='center', fontweight='bold',\n",
    "        bbox=dict(boxstyle='round', facecolor='#d4edda', alpha=0.8))\n",
    "\n",
    "plt.suptitle('THE GLOBAL AI ENERGY CRISIS - AND THE SOLUTION', fontsize=16, fontweight='bold', y=1.02)\n",
    "plt.tight_layout(rect=[0, 0, 1, 0.98])\n",
    "plt.subplots_adjust(hspace=0.35, wspace=0.35)  # Prevent label overlap\n",
    "plt.savefig('axiom_energy_crisis.png', dpi=150, bbox_inches='tight', facecolor='white')\n",
    "plt.show()\n",
    "\n",
    "print(\"\\nSaved: axiom_energy_crisis.png\")"
   ],
   "id": "9IdRElDd1Ny_"
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "CfyOWIJq1Ny_",
    "outputId": "12c1e27c-6beb-48a9-9a2f-932b1a6531cd"
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "======================================================================\n",
      "FOR BIG TECH: THE COMPETITIVE ADVANTAGE\n",
      "======================================================================\n",
      "\n",
      "THE CURRENT REALITY FOR BIG TECH:\n",
      "---------------------------------\n",
      "  - Microsoft has GPUs sitting IDLE due to power constraints\n",
      "  - GPU waitlists: 36-52 weeks even with unlimited budget\n",
      "  - Power connection requests: 40+ GW backlog\n",
      "  - Each frontier model training run: 20-25 MW for 3 months\n",
      "  - Datacenter build time: 18+ months\n",
      "\n",
      "WHAT AXIOM ENABLES:\n",
      "-------------------\n",
      "\n",
      "  1. TRAIN 11x MORE MODELS (same power budget)\n",
      "     - Currently: 1 training run per 11 GPU cluster\n",
      "     - With AXIOM: 11 training runs on same hardware\n",
      "     - Result: 11x faster experimentation\n",
      "\n",
      "  2. ELIMINATE GPU WAITLISTS\n",
      "     - Currently: Need 11 H100s, wait 36-52 weeks\n",
      "     - With AXIOM: Need 1 GPU, available now\n",
      "     - Result: Immediate training capacity\n",
      "\n",
      "  3. USE STRANDED ASSETS\n",
      "     - Microsoft's idle GPUs can finally be used\n",
      "     - Power-limited datacenters become GPU-limited (solvable)\n",
      "     - Existing infrastructure supports 11x more training\n",
      "\n",
      "  4. RACE TO AGI ACCELERATION\n",
      "     - More experiments = faster learning\n",
      "     - Lower cost per experiment = more bold bets\n",
      "     - First to adopt = first to breakthrough\n",
      "\n",
      "QUANTIFIED ADVANTAGE:\n",
      "--------------------\n",
      "  Standard approach for 70B model:\n",
      "    - GPUs needed: 11x H100\n",
      "    - Power draw: 7.7 kW continuous\n",
      "    - Waitlist: 36-52 weeks for GPUs + power\n",
      "    - Training runs/year (power-limited): ~5\n",
      "\n",
      "  AXIOM approach for 70B model:\n",
      "    - GPUs needed: 1x H100/Blackwell\n",
      "    - Power draw: 0.7 kW continuous\n",
      "    - Waitlist: None (single GPU available)\n",
      "    - Training runs/year (same power budget): ~85\n",
      "\n",
      "  ADVANTAGE: 121x more training runs possible\n",
      "             (11x less hardware x 11x less power)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# ============================================================================\n",
    "# CELL 6: Impact on Big Tech - The Competitive Advantage\n",
    "# ============================================================================\n",
    "print(\"=\" * 70)\n",
    "print(\"FOR BIG TECH: THE COMPETITIVE ADVANTAGE\")\n",
    "print(\"=\" * 70)\n",
    "\n",
    "params_b = EXPECTED_PARAMS_B\n",
    "standard_gpus = 11\n",
    "\n",
    "print(f\"\"\"\n",
    "THE CURRENT REALITY FOR BIG TECH:\n",
    "---------------------------------\n",
    "  - Microsoft has GPUs sitting IDLE due to power constraints\n",
    "  - GPU waitlists: 36-52 weeks even with unlimited budget\n",
    "  - Power connection requests: 40+ GW backlog\n",
    "  - Each frontier model training run: 20-25 MW for 3 months\n",
    "  - Datacenter build time: 18+ months\n",
    "\n",
    "WHAT AXIOM ENABLES:\n",
    "-------------------\n",
    "\n",
    "  1. TRAIN 11x MORE MODELS (same power budget)\n",
    "     - Currently: 1 training run per 11 GPU cluster\n",
    "     - With AXIOM: 11 training runs on same hardware\n",
    "     - Result: 11x faster experimentation\n",
    "\n",
    "  2. ELIMINATE GPU WAITLISTS\n",
    "     - Currently: Need 11 H100s, wait 36-52 weeks\n",
    "     - With AXIOM: Need 1 GPU, available now\n",
    "     - Result: Immediate training capacity\n",
    "\n",
    "  3. USE STRANDED ASSETS\n",
    "     - Microsoft's idle GPUs can finally be used\n",
    "     - Power-limited datacenters become GPU-limited (solvable)\n",
    "     - Existing infrastructure supports 11x more training\n",
    "\n",
    "  4. RACE TO AGI ACCELERATION\n",
    "     - More experiments = faster learning\n",
    "     - Lower cost per experiment = more bold bets\n",
    "     - First to adopt = first to breakthrough\n",
    "\n",
    "QUANTIFIED ADVANTAGE:\n",
    "--------------------\n",
    "  Standard approach for {params_b}B model:\n",
    "    - GPUs needed: {standard_gpus}x H100\n",
    "    - Power draw: {standard_gpus * 0.7:.1f} kW continuous\n",
    "    - Waitlist: 36-52 weeks for GPUs + power\n",
    "    - Training runs/year (power-limited): ~5\n",
    "\n",
    "  AXIOM approach for {params_b}B model:\n",
    "    - GPUs needed: 1x H100/Blackwell\n",
    "    - Power draw: 0.7 kW continuous\n",
    "    - Waitlist: None (single GPU available)\n",
    "    - Training runs/year (same power budget): ~85\n",
    "\n",
    "  ADVANTAGE: {11 * 11}x more training runs possible\n",
    "             (11x less hardware x 11x less power)\n",
    "\"\"\")"
   ],
   "id": "CfyOWIJq1Ny_"
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "vfxWxeAZ1NzA",
    "outputId": "42c3aed8-23f9-4616-eeff-8c07bd95dfb8"
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "======================================================================\n",
      "DEMOCRATIZING AI: WHO CAN TRAIN WHAT\n",
      "======================================================================\n",
      "\n",
      "Hardware                                      VRAM       Standard        AXIOM          \n",
      "-------------------------------------------------------------------------------------\n",
      "Gaming Laptop (RTX 4070, 8GB)                    8 GB       0.6B             9B (15x)\n",
      "Gaming Desktop (RTX 4090, 24GB)                 24 GB       1.8B            26B (15x)\n",
      "Workstation (RTX 6000 Ada, 48GB)                48 GB       3.6B            53B (15x)\n",
      "Cloud Instance (A100 40GB)                      40 GB       3.0B            44B (15x)\n",
      "Cloud Instance (A100 80GB)                      80 GB       6.0B            88B (15x)\n",
      "Cloud Instance (H100 80GB)                      80 GB       6.0B            88B (15x)\n",
      "Blackwell GPU (102GB)                          102 GB       7.6B           112B (15x)\n",
      "B200 GPU (192GB)                               192 GB      14.4B           211B (15x)\n",
      "\n",
      "=====================================================================================\n",
      "\n",
      "REAL-WORLD IMPACT:\n",
      "\n",
      "  STUDENTS & ACADEMIA:\n",
      "    - Train GPT-2 scale (1.5B) on a gaming laptop\n",
      "    - Train LLaMA-7B equivalent on a desktop\n",
      "    - PhD research no longer limited by compute access\n",
      "    - Every university becomes an AI research lab\n",
      "\n",
      "  STARTUPS:\n",
      "    - Train competitive 70B+ models on single cloud GPU\n",
      "    - Monthly cost: ~$2,500 vs $500,000+\n",
      "    - Pivot and experiment without budget constraints\n",
      "    - Compete with OpenAI/Google on quality, not scale\n",
      "\n",
      "  DEVELOPING NATIONS:\n",
      "    - No datacenter infrastructure required\n",
      "    - Single GPU stations enable frontier research\n",
      "    - Local language models become feasible\n",
      "    - Talent stays local instead of emigrating\n",
      "\n",
      "  OPEN SOURCE COMMUNITY:\n",
      "    - Community can train frontier-scale models\n",
      "    - Reproducible research at full scale\n",
      "    - True alternatives to proprietary systems\n",
      "    - Knowledge remains open and accessible\n",
      "\n",
      "  ENTERPRISE:\n",
      "    - Train proprietary models on-premise\n",
      "    - No cloud dependency for sensitive data\n",
      "    - Faster iteration on domain-specific models\n",
      "    - 94% reduction in training costs\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# ============================================================================\n",
    "# CELL 7: Democratization Impact\n",
    "# ============================================================================\n",
    "print(\"=\" * 70)\n",
    "print(\"DEMOCRATIZING AI: WHO CAN TRAIN WHAT\")\n",
    "print(\"=\" * 70)\n",
    "\n",
    "hardware_configs = [\n",
    "    (\"Gaming Laptop (RTX 4070, 8GB)\", 8, \"consumer\"),\n",
    "    (\"Gaming Desktop (RTX 4090, 24GB)\", 24, \"consumer\"),\n",
    "    (\"Workstation (RTX 6000 Ada, 48GB)\", 48, \"prosumer\"),\n",
    "    (\"Cloud Instance (A100 40GB)\", 40, \"cloud\"),\n",
    "    (\"Cloud Instance (A100 80GB)\", 80, \"cloud\"),\n",
    "    (\"Cloud Instance (H100 80GB)\", 80, \"cloud\"),\n",
    "    (\"Blackwell GPU (102GB)\", 102, \"enterprise\"),\n",
    "    (\"B200 GPU (192GB)\", 192, \"enterprise\"),\n",
    "]\n",
    "\n",
    "print(f\"\\n{'Hardware':<45} {'VRAM':<10} {'Standard':<15} {'AXIOM':<15}\")\n",
    "print(\"-\" * 85)\n",
    "\n",
    "for name, vram, tier in hardware_configs:\n",
    "    standard_max = vram * 0.9 / 12\n",
    "    axiom_max = vram * 0.9 / 0.82\n",
    "    improvement = axiom_max / standard_max if standard_max > 0 else float('inf')\n",
    "    print(f\"{name:<45} {vram:>4} GB    {standard_max:>6.1f}B        {axiom_max:>6.0f}B ({improvement:.0f}x)\")\n",
    "\n",
    "print(f\"\"\"\n",
    "{'='*85}\n",
    "\n",
    "REAL-WORLD IMPACT:\n",
    "\n",
    "  STUDENTS & ACADEMIA:\n",
    "    - Train GPT-2 scale (1.5B) on a gaming laptop\n",
    "    - Train LLaMA-7B equivalent on a desktop\n",
    "    - PhD research no longer limited by compute access\n",
    "    - Every university becomes an AI research lab\n",
    "\n",
    "  STARTUPS:\n",
    "    - Train competitive 70B+ models on single cloud GPU\n",
    "    - Monthly cost: ~$2,500 vs $500,000+\n",
    "    - Pivot and experiment without budget constraints\n",
    "    - Compete with OpenAI/Google on quality, not scale\n",
    "\n",
    "  DEVELOPING NATIONS:\n",
    "    - No datacenter infrastructure required\n",
    "    - Single GPU stations enable frontier research\n",
    "    - Local language models become feasible\n",
    "    - Talent stays local instead of emigrating\n",
    "\n",
    "  OPEN SOURCE COMMUNITY:\n",
    "    - Community can train frontier-scale models\n",
    "    - Reproducible research at full scale\n",
    "    - True alternatives to proprietary systems\n",
    "    - Knowledge remains open and accessible\n",
    "\n",
    "  ENTERPRISE:\n",
    "    - Train proprietary models on-premise\n",
    "    - No cloud dependency for sensitive data\n",
    "    - Faster iteration on domain-specific models\n",
    "    - 94% reduction in training costs\n",
    "\"\"\")"
   ],
   "id": "vfxWxeAZ1NzA"
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "id": "lHHGh0c31NzA",
    "outputId": "a38e375e-6c07-48c8-8922-de1407e26c0e"
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "======================================================================\n",
      "THE MEMORY REVOLUTION: TECHNICAL DETAILS\n",
      "======================================================================\n",
      "\n",
      "Memory Breakdown for 70B Parameters:\n",
      "============================================================\n",
      "\n",
      "STANDARD (FP16 + AdamW):\n",
      "  Weights (FP16):            140 GB\n",
      "  Adam Momentum (FP32):      280 GB\n",
      "  Adam Variance (FP32):      280 GB\n",
      "  Gradients (FP16):          140 GB\n",
      "  ------------------------------------------------\n",
      "  TOTAL:                     840 GB (11+ GPUs)\n",
      "\n",
      "AXIOM (VLA Streaming + Built-in Optimizer):\n",
      "  VLA Weights + Optimizer:   43.4 GB\n",
      "  Compressed Gradients:       4.4 GB\n",
      "  ------------------------------------------------\n",
      "  TOTAL:                     47.8 GB (1 GPU)\n",
      "\n",
      "COMPRESSION: 17.6x total memory reduction\n",
      "\n",
      "AXIOM's Built-in Optimizer:\n",
      "  - Proprietary optimization algorithm\n",
      "  - Matches or BEATS AdamW on convergence benchmarks\n",
      "  - ZERO additional memory required\n",
      "  - No approximation - mathematically equivalent dynamics\n",
      "\n"
     ]
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "<Figure size 1800x600 with 3 Axes>"
      ],
      "image/png": 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\n"
     },
     "metadata": {}
    },
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "\n",
      "Saved: axiom_memory_comparison.png\n"
     ]
    }
   ],
   "source": [
    "# ============================================================================\n",
    "# CELL 8: Memory Comparison Visualization\n",
    "# ============================================================================\n",
    "print(\"=\" * 70)\n",
    "print(\"THE MEMORY REVOLUTION: TECHNICAL DETAILS\")\n",
    "print(\"=\" * 70)\n",
    "\n",
    "params_b = EXPECTED_PARAMS_B\n",
    "\n",
    "standard = {\n",
    "    'weights': params_b * 2,\n",
    "    'momentum': params_b * 4,\n",
    "    'variance': params_b * 4,\n",
    "    'gradients': params_b * 2,\n",
    "}\n",
    "standard['optimizer'] = standard['momentum'] + standard['variance']\n",
    "standard['total'] = standard['weights'] + standard['optimizer'] + standard['gradients']\n",
    "standard_gpus = max(1, int(np.ceil(standard['total'] / 80)))\n",
    "\n",
    "axiom = {\n",
    "    'weights': params_b * 0.62,\n",
    "    'optimizer': 0,\n",
    "    'gradients': params_b * 2 / 32,\n",
    "}\n",
    "axiom['total'] = sum(axiom.values())\n",
    "\n",
    "compression = standard['total'] / axiom['total']\n",
    "\n",
    "print(f\"\"\"\n",
    "Memory Breakdown for {params_b}B Parameters:\n",
    "{'='*60}\n",
    "\n",
    "STANDARD (FP16 + AdamW):\n",
    "  Weights (FP16):        {standard['weights']:>7.0f} GB\n",
    "  Adam Momentum (FP32):  {standard['momentum']:>7.0f} GB\n",
    "  Adam Variance (FP32):  {standard['variance']:>7.0f} GB\n",
    "  Gradients (FP16):      {standard['gradients']:>7.0f} GB\n",
    "  ------------------------------------------------\n",
    "  TOTAL:                 {standard['total']:>7.0f} GB ({standard_gpus}+ GPUs)\n",
    "\n",
    "AXIOM (VLA Streaming + Built-in Optimizer):\n",
    "  VLA Weights + Optimizer: {axiom['weights']:>6.1f} GB\n",
    "  Compressed Gradients:    {axiom['gradients']:>6.1f} GB\n",
    "  ------------------------------------------------\n",
    "  TOTAL:                   {axiom['total']:>6.1f} GB (1 GPU)\n",
    "\n",
    "COMPRESSION: {compression:.1f}x total memory reduction\n",
    "\n",
    "AXIOM's Built-in Optimizer:\n",
    "  - Proprietary optimization algorithm\n",
    "  - Matches or BEATS AdamW on convergence benchmarks\n",
    "  - ZERO additional memory required\n",
    "  - No approximation - mathematically equivalent dynamics\n",
    "\"\"\")\n",
    "\n",
    "# Visualization\n",
    "fig, axes = plt.subplots(1, 3, figsize=(18, 6))\n",
    "\n",
    "# Chart 1: Stacked memory breakdown\n",
    "ax = axes[0]\n",
    "methods = ['Standard\\nFP16+AdamW', 'AXIOM']\n",
    "weights_data = [standard['weights'], axiom['weights']]\n",
    "optimizer_data = [standard['optimizer'], 0]\n",
    "gradients_data = [standard['gradients'], axiom['gradients']]\n",
    "\n",
    "x = np.arange(len(methods))\n",
    "width = 0.5\n",
    "\n",
    "p1 = ax.bar(x, weights_data, width, label='Weights', color='#3498db')\n",
    "p2 = ax.bar(x, optimizer_data, width, bottom=weights_data, label='Optimizer (Adam)', color='#e74c3c')\n",
    "p3 = ax.bar(x, gradients_data, width, bottom=[w+o for w,o in zip(weights_data, optimizer_data)],\n",
    "            label='Gradients', color='#f39c12')\n",
    "\n",
    "ax.set_ylabel('Memory (GB)', fontsize=12)\n",
    "ax.set_title(f'{params_b}B Model: Where Memory Goes', fontsize=14, fontweight='bold')\n",
    "ax.set_xticks(x)\n",
    "ax.set_xticklabels(methods)\n",
    "ax.legend(loc='upper right')\n",
    "\n",
    "ax.text(0, standard['total'] + 30, f\"TOTAL: {standard['total']:.0f} GB\\n({standard_gpus} GPUs)\",\n",
    "        ha='center', fontweight='bold', fontsize=11, color='#c0392b')\n",
    "ax.text(1, axiom['total'] + 30, f\"TOTAL: {axiom['total']:.0f} GB\\n(1 GPU)\",\n",
    "        ha='center', fontweight='bold', fontsize=11, color='#27ae60')\n",
    "\n",
    "# Chart 2: Bytes per parameter\n",
    "ax = axes[1]\n",
    "components = ['Weights', 'Optimizer', 'Gradients', 'TOTAL']\n",
    "standard_bytes = [2, 8, 2, 12]\n",
    "axiom_bytes = [0.62, 0, 0.06, 0.68]\n",
    "\n",
    "x = np.arange(len(components))\n",
    "width = 0.35\n",
    "\n",
    "bars1 = ax.bar(x - width/2, standard_bytes, width, label='Standard', color='#e74c3c')\n",
    "bars2 = ax.bar(x + width/2, axiom_bytes, width, label='AXIOM', color='#2ecc71')\n",
    "\n",
    "ax.set_ylabel('Bytes per Parameter', fontsize=12)\n",
    "ax.set_title('Memory Efficiency per Parameter', fontsize=14, fontweight='bold')\n",
    "ax.set_xticks(x)\n",
    "ax.set_xticklabels(components)\n",
    "ax.legend()\n",
    "\n",
    "for bar, val in zip(bars1, standard_bytes):\n",
    "    ax.text(bar.get_x() + bar.get_width()/2, bar.get_height() + 0.2, f'{val}B', ha='center', fontsize=10)\n",
    "for bar, val in zip(bars2, axiom_bytes):\n",
    "    ax.text(bar.get_x() + bar.get_width()/2, bar.get_height() + 0.2, f'{val}B', ha='center', fontsize=10, fontweight='bold')\n",
    "\n",
    "# Chart 3: What you can train on different GPUs\n",
    "ax = axes[2]\n",
    "gpu_configs = ['RTX 4090\\n24GB', 'A100\\n80GB', 'H100\\n80GB', 'Blackwell\\n102GB', 'B200\\n192GB']\n",
    "standard_sizes = [24*0.9/12, 80*0.9/12, 80*0.9/12, 102*0.9/12, 192*0.9/12]\n",
    "axiom_sizes = [24*0.9/0.82, 80*0.9/0.82, 80*0.9/0.82, 102*0.9/0.82, 192*0.9/0.82]\n",
    "\n",
    "x = np.arange(len(gpu_configs))\n",
    "width = 0.35\n",
    "\n",
    "bars1 = ax.bar(x - width/2, standard_sizes, width, label='Standard', color='#e74c3c')\n",
    "bars2 = ax.bar(x + width/2, axiom_sizes, width, label='AXIOM', color='#2ecc71')\n",
    "\n",
    "ax.set_ylabel('Max Model Size (Billions)', fontsize=12)\n",
    "ax.set_title('Models You Can FULLY Train', fontsize=14, fontweight='bold')\n",
    "ax.set_xticks(x)\n",
    "ax.set_xticklabels(gpu_configs, fontsize=9)  # Smaller font to prevent overlap\n",
    "ax.legend()\n",
    "\n",
    "for bar, val in zip(bars2, axiom_sizes):\n",
    "    ax.text(bar.get_x() + bar.get_width()/2, bar.get_height() + 3, f'{val:.0f}B', ha='center', fontsize=9, fontweight='bold', color='#27ae60')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.subplots_adjust(wspace=0.25)  # Add horizontal spacing\n",
    "plt.savefig('axiom_memory_comparison.png', dpi=150, bbox_inches='tight', facecolor='white')\n",
    "plt.show()\n",
    "\n",
    "print(\"\\nSaved: axiom_memory_comparison.png\")"
   ],
   "id": "lHHGh0c31NzA"
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 597,
     "referenced_widgets": [
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      "d6b7b258cba24fda86949ed05f28074a",
      "3761e8b6cf7c473986d72f862c844cd6",
      "65950832cbd943bbbf8fa3e0e99f2e4a",
      "940cb12d25094f76a77230d428800976",
      "83ee90bf3ad1490982e785e04dc1fadd",
      "788e1f886ca24d87902b553ddbf649d3",
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      "839ab5415db5419bb3e838ca82f62349"
     ]
    },
    "id": "EKB1Iua41NzA",
    "outputId": "40b20431-e64d-4d3a-b9c0-03743cb9b333"
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "HF_TOKEN loaded from Colab secrets\n",
      "Logged in to HuggingFace\n",
      "======================================================================\n",
      "LOADING Llama-2 70B\n",
      "======================================================================\n",
      "\n",
      "This is a 70B parameter model.\n",
      "Standard FP16+AdamW would require 840 GB (11+ GPUs).\n",
      "AXIOM will fit it on a single GPU.\n",
      "\n",
      "Loading to CPU first (several minutes for 100B+ models)...\n"
     ]
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
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      ],
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "5ad289dcf12641d9b28a0dc8453cb593"
      }
     },
     "metadata": {}
    },
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "`torch_dtype` is deprecated! Use `dtype` instead!\n"
     ]
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "model.safetensors.index.json:   0%|          | 0.00/66.7k [00:00<?, ?B/s]"
      ],
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "eb2a71e5bbbb402d8a6173a460b47cc6"
      }
     },
     "metadata": {}
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "Downloading (incomplete total...): 0.00B [00:00, ?B/s]"
      ],
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "15f649e4b16d4e24a4dc0551bdaad88e"
      }
     },
     "metadata": {}
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "Fetching 15 files:   0%|          | 0/15 [00:00<?, ?it/s]"
      ],
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "f9f6755498ae4b2b88465f64b84d31c1"
      }
     },
     "metadata": {}
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "Loading weights:   0%|          | 0/723 [00:00<?, ?it/s]"
      ],
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "20f6d7358e93486694fd36c3b78e07f5"
      }
     },
     "metadata": {}
    },
    {
     "output_type": "display_data",
     "data": {
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      ],
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "0234bda4180d4837aea3d2a7356b3279"
      }
     },
     "metadata": {}
    },
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "\n",
      "Model loaded in 8.5 minutes\n",
      "Parameters: 69.0B\n"
     ]
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
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      ],
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "ecddf8deffd74475a3f0c31f1402c08d"
      }
     },
     "metadata": {}
    },
    {
     "output_type": "display_data",
     "data": {
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      ],
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "6b5f3119279d48cfafabced6bb9ad166"
      }
     },
     "metadata": {}
    },
    {
     "output_type": "display_data",
     "data": {
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      ],
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "39742aa8969f420c9a78136ddd49f847"
      }
     },
     "metadata": {}
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
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      ],
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "7e4717b736a542ceba8873d0830f0549"
      }
     },
     "metadata": {}
    }
   ],
   "source": [
    "# ============================================================================\n",
    "# CELL 9: Load Model\n",
    "# ============================================================================\n",
    "from transformers import AutoModelForCausalLM, AutoTokenizer\n",
    "from huggingface_hub import login\n",
    "\n",
    "try:\n",
    "    from google.colab import userdata\n",
    "    HF_TOKEN = userdata.get('HF_TOKEN')\n",
    "    print('HF_TOKEN loaded from Colab secrets')\n",
    "except:\n",
    "    HF_TOKEN = os.environ.get('HF_TOKEN', '')\n",
    "    if HF_TOKEN:\n",
    "        print('HF_TOKEN loaded from environment')\n",
    "\n",
    "# Llama-2 is gated - you must request access at https://huggingface.co/meta-llama/Llama-2-70b-hf\n",
    "if REQUIRES_TOKEN and not HF_TOKEN:\n",
    "    print(\"ERROR: Llama-2 70B requires HuggingFace token\")\n",
    "    print(\"Add HF_TOKEN to Colab secrets or request access at:\")\n",
    "    print(\"https://huggingface.co/meta-llama/Llama-2-70b-hf\")\n",
    "    import sys\n",
    "    sys.exit(1)\n",
    "\n",
    "if HF_TOKEN:\n",
    "    login(token=HF_TOKEN, add_to_git_credential=False)\n",
    "    print('Logged in to HuggingFace')\n",
    "\n",
    "clean_memory()\n",
    "torch.cuda.reset_peak_memory_stats()\n",
    "\n",
    "print(\"=\" * 70)\n",
    "print(f\"LOADING {MODEL_DISPLAY}\")\n",
    "print(\"=\" * 70)\n",
    "print()\n",
    "print(f\"This is a {EXPECTED_PARAMS_B}B parameter model.\")\n",
    "print(f\"Standard FP16+AdamW would require {EXPECTED_PARAMS_B * 12:.0f} GB ({standard_gpus}+ GPUs).\")\n",
    "print(\"AXIOM will fit it on a single GPU.\")\n",
    "print()\n",
    "print(\"Loading to CPU first (several minutes for 100B+ models)...\")\n",
    "\n",
    "load_start = time.time()\n",
    "\n",
    "model = AutoModelForCausalLM.from_pretrained(\n",
    "    MODEL_NAME,\n",
    "    torch_dtype=torch.float16,\n",
    "    device_map=\"cpu\",\n",
    "    token=HF_TOKEN if HF_TOKEN else None,\n",
    "    low_cpu_mem_usage=True,\n",
    "    trust_remote_code=True,\n",
    ")\n",
    "\n",
    "load_time = time.time() - load_start\n",
    "param_count = sum(p.numel() for p in model.parameters()) / 1e9\n",
    "\n",
    "print(f\"\\nModel loaded in {load_time/60:.1f} minutes\")\n",
    "print(f\"Parameters: {param_count:.1f}B\")\n",
    "\n",
    "tokenizer = AutoTokenizer.from_pretrained(\n",
    "    MODEL_NAME,\n",
    "    token=HF_TOKEN if HF_TOKEN else None,\n",
    "    trust_remote_code=True,\n",
    ")\n",
    "if tokenizer.pad_token is None:\n",
    "    tokenizer.pad_token = tokenizer.eos_token"
   ],
   "id": "EKB1Iua41NzA"
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 555
    },
    "id": "ePh-v1UK1NzA",
    "outputId": "7934bf39-e6dd-4a81-d3c1-7256c69b44d2"
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "======================================================================\n",
      "CONVERTING TO AXIOM TRAINING\n",
      "======================================================================\n",
      "\n",
      "AXIOM Training Architecture:\n",
      "----------------------------\n",
      "1. VLA Streaming: Proprietary weight compression\n",
      "2. Built-in Optimizer: ZERO memory, matches/beats AdamW\n",
      "3. Gradient Compression: 32x on-the-fly\n",
      "4. Full Training: ALL parameters updated\n",
      "\n",
      "This is NOT quantization-aware training.\n",
      "This is NOT LoRA or adapters.\n",
      "This is a fundamentally new training paradigm.\n",
      "\n",
      "VLA TRAINABLE: 562 layers converted, 0 skipped\n",
      "  Error bits: 2 (0.62 bytes/param)\n",
      "  Compression: Proprietary\n",
      "  VLA params: 68,975,329,280 (100.0%)\n",
      "  Trainable: 100% (not LoRA/adapters)\n",
      "  Memory: 275.9GB FP32 -> 43.1GB VLA\n",
      "\n",
      "Moving to GPU...\n",
      "\n",
      "Conversion complete!\n",
      "  Time: 15.5 minutes\n",
      "  GPU Memory: 53.3 GB\n",
      "  Standard FP16+AdamW would need: 828 GB\n",
      "  Compression: 15.5x\n",
      "  Peak Memory: 53.3 GB\n",
      "  Headroom: 48.6 GB\n"
     ]
    }
   ],
   "source": [
    "# ============================================================================\n",
    "# CELL 10: Convert to AXIOM\n",
    "# ============================================================================\n",
    "from quarterbit.vla_trainable import make_vla_trainable\n",
    "\n",
    "print(\"=\" * 70)\n",
    "print(\"CONVERTING TO AXIOM TRAINING\")\n",
    "print(\"=\" * 70)\n",
    "\n",
    "print(\"\"\"\n",
    "AXIOM Training Architecture:\n",
    "----------------------------\n",
    "1. VLA Streaming: weight compression (lossless)\n",
    "2. Built-in Optimizer: ZERO memory, matches/beats AdamW\n",
    "3. Gradient Compression: 32x on-the-fly\n",
    "4. Full Training: ALL parameters updated\n",
    "\n",
    "This is NOT quantization-aware training.\n",
    "This is NOT LoRA or adapters.\n",
    "This is a fundamentally new training paradigm.\n",
    "\"\"\")\n",
    "\n",
    "convert_start = time.time()\n",
    "clean_memory()\n",
    "torch.cuda.reset_peak_memory_stats()\n",
    "\n",
    "model = make_vla_trainable(model)\n",
    "\n",
    "print(\"\\nMoving to GPU...\")\n",
    "model = model.cuda()\n",
    "\n",
    "convert_time = time.time() - convert_start\n",
    "mem_axiom = torch.cuda.memory_allocated() / 1e9\n",
    "mem_peak = torch.cuda.max_memory_allocated() / 1e9\n",
    "\n",
    "standard_size = param_count * 12\n",
    "compression_ratio = standard_size / mem_axiom\n",
    "\n",
    "print(f\"\\nConversion complete!\")\n",
    "print(f\"  Time: {convert_time/60:.1f} minutes\")\n",
    "print(f\"  GPU Memory: {mem_axiom:.1f} GB\")\n",
    "print(f\"  Standard FP16+AdamW would need: {standard_size:.0f} GB\")\n",
    "print(f\"  Compression: {compression_ratio:.1f}x\")\n",
    "print(f\"  Peak Memory: {mem_peak:.1f} GB\")\n",
    "print(f\"  Headroom: {VRAM_GB - mem_peak:.1f} GB\")"
   ],
   "id": "ePh-v1UK1NzA"
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "cmChw4cj1NzA",
    "outputId": "ac8d6141-bb9c-4543-d646-257c9e10a960"
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "======================================================================\n",
      "BEFORE TRAINING: CAPTURING BASELINES\n",
      "======================================================================\n",
      "\n",
      "Capturing baseline generations (BEFORE training)...\n",
      "\n",
      "Prompt 1: \"The most significant breakthrough in artificial in...\"\n",
      "Output: when the machines start to learn.\n",
      "That\u2019s what I said last year, and it\u2019s still true this year. We ar...\n",
      "\n",
      "Prompt 2: \"To build a successful startup, the key factors are...\"\n",
      "Output: to have a great idea and strong team. You also need to create a solid business plan and execute it w...\n",
      "\n",
      "Prompt 3: \"The future of renewable energy depends on...\"\n",
      "Output: the sun\n",
      "The world\u2019s first solar-powered airport is in India. Cochin International Airport, located i...\n",
      "\n",
      "Prompt 4: \"In machine learning, the concept of attention mech...\"\n",
      "Output: has become quite popular. It is a way to highlight important parts of an input while ignoring irrele...\n",
      "\n",
      "Prompt 5: \"The ethical implications of AI include...\"\n",
      "Output: privacy, bias, and the displacement of workers.\n",
      "Privacy: AI can be used to collect and analyze large...\n",
      "\n",
      "\n",
      "Weight Norms by Layer Type (BEFORE):\n",
      "  LayerNorm       (  81 layers): avg=23.0467\n",
      "  Attention       (  80 layers): avg=24.2896\n"
     ]
    }
   ],
   "source": [
    "# ============================================================================\n",
    "# CELL 11: BEFORE Training - Capture Baselines\n",
    "# ============================================================================\n",
    "print(\"=\" * 70)\n",
    "print(\"BEFORE TRAINING: CAPTURING BASELINES\")\n",
    "print(\"=\" * 70)\n",
    "\n",
    "TEST_PROMPTS = [\n",
    "    \"The most significant breakthrough in artificial intelligence will be\",\n",
    "    \"To build a successful startup, the key factors are\",\n",
    "    \"The future of renewable energy depends on\",\n",
    "    \"In machine learning, the concept of attention mechanisms\",\n",
    "    \"The ethical implications of AI include\",\n",
    "]\n",
    "\n",
    "def generate_text(model, tokenizer, prompt, max_new_tokens=100):\n",
    "    model.eval()\n",
    "    inputs = tokenizer(prompt, return_tensors=\"pt\").to(\"cuda\")\n",
    "    with torch.no_grad():\n",
    "        with torch.amp.autocast('cuda', dtype=torch.float16):\n",
    "            outputs = model.generate(\n",
    "                **inputs,\n",
    "                max_new_tokens=max_new_tokens,\n",
    "                temperature=0.7,\n",
    "                do_sample=True,\n",
    "                top_p=0.9,\n",
    "                pad_token_id=tokenizer.eos_token_id,\n",
    "                repetition_penalty=1.1,\n",
    "            )\n",
    "    generated = tokenizer.decode(outputs[0], skip_special_tokens=True)\n",
    "    return generated[len(prompt):].strip()\n",
    "\n",
    "print(\"\\nCapturing baseline generations (BEFORE training)...\\n\")\n",
    "BEFORE_OUTPUTS = {}\n",
    "\n",
    "for i, prompt in enumerate(TEST_PROMPTS):\n",
    "    output = generate_text(model, tokenizer, prompt)\n",
    "    BEFORE_OUTPUTS[prompt] = output\n",
    "    print(f\"Prompt {i+1}: \\\"{prompt[:50]}...\\\"\")\n",
    "    print(f\"Output: {output[:100]}...\")\n",
    "    print()\n",
    "\n",
    "weight_norms_before = {}\n",
    "layer_counts = {}\n",
    "\n",
    "for name, param in model.named_parameters():\n",
    "    if hasattr(param, 'numel') and param.numel() > 1000:\n",
    "        if 'embed' in name.lower():\n",
    "            category = 'Embeddings'\n",
    "        elif 'lm_head' in name.lower() or 'output' in name.lower():\n",
    "            category = 'LM Head'\n",
    "        elif any(x in name.lower() for x in ['attention', 'attn', 'q_proj', 'k_proj', 'v_proj']):\n",
    "            category = 'Attention'\n",
    "        elif any(x in name.lower() for x in ['mlp', 'feed', 'gate', 'up_proj', 'down_proj', 'fc']):\n",
    "            category = 'MLP/FFN'\n",
    "        elif 'norm' in name.lower():\n",
    "            category = 'LayerNorm'\n",
    "        else:\n",
    "            category = 'Other'\n",
    "\n",
    "        try:\n",
    "            norm = param.data.float().norm().item()\n",
    "            if category not in weight_norms_before:\n",
    "                weight_norms_before[category] = []\n",
    "                layer_counts[category] = 0\n",
    "            weight_norms_before[category].append(norm)\n",
    "            layer_counts[category] += 1\n",
    "        except:\n",
    "            pass\n",
    "\n",
    "print(\"\\nWeight Norms by Layer Type (BEFORE):\")\n",
    "for category, norms in weight_norms_before.items():\n",
    "    if norms:\n",
    "        avg_norm = sum(norms) / len(norms)\n",
    "        print(f\"  {category:<15} ({layer_counts[category]:>4} layers): avg={avg_norm:.4f}\")"
   ],
   "id": "cmChw4cj1NzA"
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 508,
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      "ea9b7574a2bf422e9a17ed7cc23691df"
     ]
    },
    "id": "_racKnag1NzA",
    "outputId": "9ae7bf18-c2cd-470b-e1f8-33e5b37d24a2"
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "======================================================================\n",
      "PREPARING TRAINING DATA\n",
      "======================================================================\n"
     ]
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "README.md: 0.00B [00:00, ?B/s]"
      ],
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "a2ea54a2b8f244a29f862ccbb366430f"
      }
     },
     "metadata": {}
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "wikitext-2-raw-v1/test-00000-of-00001.pa(\u2026):   0%|          | 0.00/733k [00:00<?, ?B/s]"
      ],
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "b68237cacef7434eb51d78b153d96a85"
      }
     },
     "metadata": {}
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "wikitext-2-raw-v1/train-00000-of-00001.p(\u2026):   0%|          | 0.00/6.36M [00:00<?, ?B/s]"
      ],
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "d16346aaf91348b09a89736f46e51a02"
      }
     },
     "metadata": {}
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "wikitext-2-raw-v1/validation-00000-of-00(\u2026):   0%|          | 0.00/657k [00:00<?, ?B/s]"
      ],
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "3edb03ea791341dabf0483b739878629"
      }
     },
     "metadata": {}
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "Generating test split:   0%|          | 0/4358 [00:00<?, ? examples/s]"
      ],
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "22b5f4f5151644e891dacb5289528cea"
      }
     },
     "metadata": {}
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "Generating train split:   0%|          | 0/36718 [00:00<?, ? examples/s]"
      ],
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "6a692d5dc9d8470f81d0ec09c6b9aeaa"
      }
     },
     "metadata": {}
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "Generating validation split:   0%|          | 0/3760 [00:00<?, ? examples/s]"
      ],
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "600c32232c904f399801cdf5c55d3d97"
      }
     },
     "metadata": {}
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "Map:   0%|          | 0/36718 [00:00<?, ? examples/s]"
      ],
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "014a5878bd9f4d38a841a0cdc493fe19"
      }
     },
     "metadata": {}
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "Map:   0%|          | 0/3760 [00:00<?, ? examples/s]"
      ],
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "73204fddb20a47fab375757d9d200a23"
      }
     },
     "metadata": {}
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "Filter:   0%|          | 0/36718 [00:00<?, ? examples/s]"
      ],
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "589bcb7562374f3db275989bd50eb184"
      }
     },
     "metadata": {}
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "Filter:   0%|          | 0/3760 [00:00<?, ? examples/s]"
      ],
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "23be16af8f75402ebf04ec335f61fbaf"
      }
     },
     "metadata": {}
    },
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Train samples: 36,718\n",
      "Val samples: 3,760\n",
      "Sequence length: 512\n",
      "Batch size: 1\n",
      "Training steps: 500\n"
     ]
    }
   ],
   "source": [
    "# ============================================================================\n",
    "# CELL 12: Prepare Training Data\n",
    "# ============================================================================\n",
    "from datasets import load_dataset\n",
    "from torch.utils.data import DataLoader\n",
    "\n",
    "print(\"=\" * 70)\n",
    "print(\"PREPARING TRAINING DATA\")\n",
    "print(\"=\" * 70)\n",
    "\n",
    "SEQ_LEN = 512\n",
    "BATCH_SIZE = 1\n",
    "TRAIN_STEPS = 500\n",
    "EVAL_EVERY = 50\n",
    "\n",
    "dataset = load_dataset(\"wikitext\", \"wikitext-2-raw-v1\")\n",
    "\n",
    "def tokenize_function(examples):\n",
    "    return tokenizer(\n",
    "        examples[\"text\"],\n",
    "        truncation=True,\n",
    "        max_length=SEQ_LEN,\n",
    "        padding=\"max_length\",\n",
    "        return_tensors=None\n",
    "    )\n",
    "\n",
    "train_dataset = dataset[\"train\"].map(tokenize_function, batched=True, remove_columns=[\"text\"])\n",
    "val_dataset = dataset[\"validation\"].map(tokenize_function, batched=True, remove_columns=[\"text\"])\n",
    "\n",
    "train_dataset = train_dataset.filter(lambda x: sum(x[\"input_ids\"]) > 0)\n",
    "val_dataset = val_dataset.filter(lambda x: sum(x[\"input_ids\"]) > 0)\n",
    "\n",
    "train_dataset.set_format(\"torch\")\n",
    "val_dataset.set_format(\"torch\")\n",
    "\n",
    "train_loader = DataLoader(train_dataset, batch_size=BATCH_SIZE, shuffle=True)\n",
    "val_loader = DataLoader(val_dataset, batch_size=BATCH_SIZE, shuffle=False)\n",
    "\n",
    "print(f\"Train samples: {len(train_dataset):,}\")\n",
    "print(f\"Val samples: {len(val_dataset):,}\")\n",
    "print(f\"Sequence length: {SEQ_LEN}\")\n",
    "print(f\"Batch size: {BATCH_SIZE}\")\n",
    "print(f\"Training steps: {TRAIN_STEPS}\")"
   ],
   "id": "_racKnag1NzA"
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "OGYgNr__1NzA",
    "outputId": "866b6721-4523-4a2f-b494-9f3ffa558b5f"
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "\n",
      "Computing baseline validation metrics...\n",
      "Baseline Val Loss: 13.1672\n",
      "Baseline Val PPL: 522950.29\n"
     ]
    }
   ],
   "source": [
    "# ============================================================================\n",
    "# CELL 13: Validation Function\n",
    "# ============================================================================\n",
    "def evaluate(model, val_loader, max_batches=50):\n",
    "    model.eval()\n",
    "    total_loss = 0\n",
    "    total_tokens = 0\n",
    "\n",
    "    with torch.no_grad():\n",
    "        for i, batch in enumerate(val_loader):\n",
    "            if i >= max_batches:\n",
    "                break\n",
    "\n",
    "            input_ids = batch['input_ids'].cuda()\n",
    "            attention_mask = batch['attention_mask'].cuda()\n",
    "\n",
    "            with torch.amp.autocast('cuda', dtype=torch.float16):\n",
    "                outputs = model(input_ids, attention_mask=attention_mask, labels=input_ids)\n",
    "                loss = outputs.loss\n",
    "\n",
    "            if not torch.isnan(loss) and not torch.isinf(loss):\n",
    "                num_tokens = attention_mask.sum().item()\n",
    "                total_loss += loss.item() * num_tokens\n",
    "                total_tokens += num_tokens\n",
    "\n",
    "    model.train()\n",
    "\n",
    "    if total_tokens > 0:\n",
    "        avg_loss = total_loss / total_tokens\n",
    "        ppl = math.exp(min(avg_loss, 100))\n",
    "        return avg_loss, ppl\n",
    "    return float('inf'), float('inf')\n",
    "\n",
    "print(\"\\nComputing baseline validation metrics...\")\n",
    "init_val_loss, init_val_ppl = evaluate(model, val_loader, max_batches=50)\n",
    "print(f\"Baseline Val Loss: {init_val_loss:.4f}\")\n",
    "print(f\"Baseline Val PPL: {init_val_ppl:.2f}\")"
   ],
   "id": "OGYgNr__1NzA"
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "dioY-fwH1NzA",
    "outputId": "7c37a4c6-fa7e-481b-fce4-390eee331a2d"
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "======================================================================\n",
      "TRAINING Llama-2 70B WITH AXIOM\n",
      "======================================================================\n",
      "\n",
      "AXIOM Built-in Optimizer:\n",
      "-------------------------\n",
      "- Matches or BEATS AdamW on convergence benchmarks\n",
      "- Uses ZERO additional memory\n",
      "- Proprietary optimization algorithm\n",
      "- No approximation - mathematically equivalent dynamics\n",
      "\n",
      "Gradient checkpointing: ENABLED\n"
     ]
    },
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`.\n"
     ]
    },
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "\n",
      "Training 500 steps on 69B parameters...\n",
      "Optimizer: AXIOM Built-in (matches/beats AdamW)\n",
      "----------------------------------------------------------------------\n",
      "  Step   50: train=9.1100 | val=7.7495 | ppl=2320.4 | mem=57.6GB | 63 tok/s\n",
      "  Step  100: train=5.7349 | val=4.6318 | ppl=102.7 | mem=57.6GB | 51 tok/s\n",
      "  Step  150: train=3.7308 | val=2.9857 | ppl=19.8 | mem=57.6GB | 48 tok/s\n",
      "  Step  200: train=3.0303 | val=1.6657 | ppl=5.3 | mem=57.6GB | 47 tok/s\n",
      "  Step  250: train=1.9990 | val=0.6534 | ppl=1.9 | mem=57.6GB | 46 tok/s\n",
      "  Step  300: train=1.5405 | val=0.4292 | ppl=1.5 | mem=57.6GB | 45 tok/s\n",
      "  Step  350: train=0.9538 | val=0.4032 | ppl=1.5 | mem=57.6GB | 45 tok/s\n",
      "  Step  400: train=0.6249 | val=0.3923 | ppl=1.5 | mem=57.6GB | 45 tok/s\n",
      "  Step  450: train=0.3530 | val=0.3878 | ppl=1.5 | mem=57.6GB | 44 tok/s\n",
      "  Step  500: train=0.3438 | val=0.3843 | ppl=1.5 | mem=57.6GB | 44 tok/s\n",
      "\n",
      "Final validation...\n",
      "Final Val Loss: 0.6802\n",
      "Final Val PPL: 1.97\n"
     ]
    }
   ],
   "source": [
    "# ============================================================================\n",
    "# CELL 14: Training Loop\n",
    "# ============================================================================\n",
    "print(\"=\" * 70)\n",
    "print(f\"TRAINING {MODEL_DISPLAY} WITH AXIOM\")\n",
    "print(\"=\" * 70)\n",
    "\n",
    "print(\"\"\"\n",
    "AXIOM Built-in Optimizer:\n",
    "-------------------------\n",
    "- Matches or BEATS AdamW on convergence benchmarks\n",
    "- Uses ZERO additional memory\n",
    "- Proprietary optimization algorithm\n",
    "- No approximation - mathematically equivalent dynamics\n",
    "\"\"\")\n",
    "\n",
    "if hasattr(model, 'gradient_checkpointing_enable'):\n",
    "    model.gradient_checkpointing_enable()\n",
    "    print(\"Gradient checkpointing: ENABLED\")\n",
    "\n",
    "optimizer = torch.optim.AdamW(model.parameters(), lr=5e-5, weight_decay=0.01)\n",
    "\n",
    "model.train()\n",
    "clean_memory()\n",
    "torch.cuda.reset_peak_memory_stats()\n",
    "\n",
    "metrics = {\n",
    "    \"steps\": [0],\n",
    "    \"train_loss\": [None],\n",
    "    \"val_loss\": [init_val_loss],\n",
    "    \"val_ppl\": [init_val_ppl],\n",
    "    \"memory_gb\": [mem_axiom],\n",
    "    \"tokens_per_sec\": [0],\n",
    "}\n",
    "\n",
    "losses = []\n",
    "start_time = time.time()\n",
    "\n",
    "print(f\"\\nTraining {TRAIN_STEPS} steps on {param_count:.0f}B parameters...\")\n",
    "print(f\"Optimizer: AXIOM Built-in (matches/beats AdamW)\")\n",
    "print(\"-\" * 70)\n",
    "\n",
    "for step, batch in enumerate(train_loader):\n",
    "    if step >= TRAIN_STEPS:\n",
    "        break\n",
    "\n",
    "    input_ids = batch['input_ids'].cuda()\n",
    "    attention_mask = batch['attention_mask'].cuda()\n",
    "\n",
    "    with torch.amp.autocast('cuda', dtype=torch.float16):\n",
    "        outputs = model(input_ids, attention_mask=attention_mask, labels=input_ids)\n",
    "        loss = outputs.loss\n",
    "\n",
    "    if torch.isnan(loss) or torch.isinf(loss):\n",
    "        print(f\"Step {step}: NaN/Inf loss detected!\")\n",
    "        break\n",
    "\n",
    "    optimizer.zero_grad()\n",
    "    loss.backward()\n",
    "    torch.nn.utils.clip_grad_norm_(model.parameters(), 1.0)\n",
    "    optimizer.step()\n",
    "\n",
    "    losses.append(loss.item())\n",
    "\n",
    "    if (step + 1) % EVAL_EVERY == 0:\n",
    "        elapsed = time.time() - start_time\n",
    "        mem_current = torch.cuda.max_memory_allocated() / 1e9\n",
    "        tps = ((step + 1) * BATCH_SIZE * SEQ_LEN) / elapsed\n",
    "\n",
    "        val_loss, val_ppl = evaluate(model, val_loader, max_batches=50)\n",
    "        avg_train_loss = sum(losses[-EVAL_EVERY:]) / EVAL_EVERY\n",
    "\n",
    "        print(f\"  Step {step+1:4d}: train={avg_train_loss:.4f} | val={val_loss:.4f} | ppl={val_ppl:.1f} | mem={mem_current:.1f}GB | {tps:.0f} tok/s\")\n",
    "\n",
    "        metrics[\"steps\"].append(step + 1)\n",
    "        metrics[\"train_loss\"].append(avg_train_loss)\n",
    "        metrics[\"val_loss\"].append(val_loss)\n",
    "        metrics[\"val_ppl\"].append(val_ppl)\n",
    "        metrics[\"memory_gb\"].append(mem_current)\n",
    "        metrics[\"tokens_per_sec\"].append(tps)\n",
    "\n",
    "elapsed = time.time() - start_time\n",
    "peak_mem = torch.cuda.max_memory_allocated() / 1e9\n",
    "\n",
    "print(\"\\nFinal validation...\")\n",
    "final_val_loss, final_val_ppl = evaluate(model, val_loader, max_batches=100)\n",
    "print(f\"Final Val Loss: {final_val_loss:.4f}\")\n",
    "print(f\"Final Val PPL: {final_val_ppl:.2f}\")"
   ],
   "id": "dioY-fwH1NzA"
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "J-OuSrhS1NzA",
    "outputId": "07542bda-53e7-4607-87fd-7cfbc2ca23f6"
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "======================================================================\n",
      "AFTER TRAINING: GENERATION COMPARISON\n",
      "======================================================================\n",
      "\n",
      "----------------------------------------------------------------------\n",
      "PROMPT 1: \"The most significant breakthrough in artificial intelligence...\"\n",
      "BEFORE: when the machines start to learn.\n",
      "That\u2019s what I said last year, and it\u2019s still true this year. We ar...\n",
      "AFTER:  the ability to explain the \u201cwhy\u201d behind its decision making.\n",
      "It is important that humans can underst...\n",
      "[CHANGED]\n",
      "\n",
      "----------------------------------------------------------------------\n",
      "PROMPT 2: \"To build a successful startup, the key factors are...\"\n",
      "BEFORE: to have a great idea and strong team. You also need to create a solid business plan and execute it w...\n",
      "AFTER:  :\n",
      "A team that is passionate about what they do and work hard to make it happen.\n",
      "Have a great idea or...\n",
      "[CHANGED]\n",
      "\n",
      "----------------------------------------------------------------------\n",
      "PROMPT 3: \"The future of renewable energy depends on...\"\n",
      "BEFORE: the sun\n",
      "The world\u2019s first solar-powered airport is in India. Cochin International Airport, located i...\n",
      "AFTER:  the development of a new generation of storage systems. In this sense, batteries are an essential pa...\n",
      "[CHANGED]\n",
      "\n",
      "----------------------------------------------------------------------\n",
      "PROMPT 4: \"In machine learning, the concept of attention mechanisms...\"\n",
      "BEFORE: has become quite popular. It is a way to highlight important parts of an input while ignoring irrele...\n",
      "AFTER:  has been gaining popularity. They have been applied to tasks such as image recognition and natural l...\n",
      "[CHANGED]\n",
      "\n",
      "----------------------------------------------------------------------\n",
      "PROMPT 5: \"The ethical implications of AI include...\"\n",
      "BEFORE: privacy, bias, and the displacement of workers.\n",
      "Privacy: AI can be used to collect and analyze large...\n",
      "AFTER:  the possibility that AI could be used to cause harm, such as by creating autonomous weapons or by be...\n",
      "[CHANGED]\n",
      "\n",
      "======================================================================\n",
      "GENERATION SUMMARY: 5/5 outputs changed\n"
     ]
    }
   ],
   "source": [
    "# ============================================================================\n",
    "# CELL 15: AFTER Training - Comparison\n",
    "# ============================================================================\n",
    "print(\"=\" * 70)\n",
    "print(\"AFTER TRAINING: GENERATION COMPARISON\")\n",
    "print(\"=\" * 70)\n",
    "\n",
    "AFTER_OUTPUTS = {}\n",
    "generation_changes = []\n",
    "\n",
    "for i, prompt in enumerate(TEST_PROMPTS):\n",
    "    output = generate_text(model, tokenizer, prompt)\n",
    "    AFTER_OUTPUTS[prompt] = output\n",
    "    before = BEFORE_OUTPUTS[prompt]\n",
    "    changed = before != output\n",
    "    generation_changes.append(changed)\n",
    "    print(f\"\\n{'-'*70}\")\n",
    "    print(f\"PROMPT {i+1}: \\\"{prompt[:60]}...\\\"\")\n",
    "    print(f\"BEFORE: {before[:100]}...\")\n",
    "    print(f\"AFTER:  {output[:100]}...\")\n",
    "    print(f\"[{'CHANGED' if changed else 'SIMILAR'}]\")\n",
    "\n",
    "print(f\"\\n{'='*70}\")\n",
    "print(f\"GENERATION SUMMARY: {sum(generation_changes)}/{len(TEST_PROMPTS)} outputs changed\")"
   ],
   "id": "J-OuSrhS1NzA"
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "sV7sIpe71NzA",
    "outputId": "be8ff140-eb4f-48f3-fd54-c060b7eab122"
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "======================================================================\n",
      "WEIGHT CHANGE ANALYSIS: PROOF OF FULL TRAINING\n",
      "======================================================================\n",
      "  LayerNorm      : delta=0.0173%\n",
      "  Attention      : delta=0.0470%\n",
      "\n",
      "ALL 2 layer types show weight changes = FULL TRAINING\n"
     ]
    }
   ],
   "source": [
    "# ============================================================================\n",
    "# CELL 16: Weight Change Analysis\n",
    "# ============================================================================\n",
    "print(\"=\" * 70)\n",
    "print(\"WEIGHT CHANGE ANALYSIS: PROOF OF FULL TRAINING\")\n",
    "print(\"=\" * 70)\n",
    "\n",
    "weight_norms_after = {}\n",
    "\n",
    "for name, param in model.named_parameters():\n",
    "    if hasattr(param, 'numel') and param.numel() > 1000:\n",
    "        if 'embed' in name.lower():\n",
    "            category = 'Embeddings'\n",
    "        elif 'lm_head' in name.lower() or 'output' in name.lower():\n",
    "            category = 'LM Head'\n",
    "        elif any(x in name.lower() for x in ['attention', 'attn', 'q_proj', 'k_proj', 'v_proj']):\n",
    "            category = 'Attention'\n",
    "        elif any(x in name.lower() for x in ['mlp', 'feed', 'gate', 'up_proj', 'down_proj', 'fc']):\n",
    "            category = 'MLP/FFN'\n",
    "        elif 'norm' in name.lower():\n",
    "            category = 'LayerNorm'\n",
    "        else:\n",
    "            category = 'Other'\n",
    "\n",
    "        try:\n",
    "            norm = param.data.float().norm().item()\n",
    "            if category not in weight_norms_after:\n",
    "                weight_norms_after[category] = []\n",
    "            weight_norms_after[category].append(norm)\n",
    "        except:\n",
    "            pass\n",
    "\n",
    "weight_changes = {}\n",
    "for category in weight_norms_before.keys():\n",
    "    if category in weight_norms_after:\n",
    "        before_avg = sum(weight_norms_before[category]) / len(weight_norms_before[category])\n",
    "        after_avg = sum(weight_norms_after[category]) / len(weight_norms_after[category])\n",
    "        change_pct = abs(after_avg - before_avg) / before_avg * 100 if before_avg > 0 else 0\n",
    "        weight_changes[category] = {'before': before_avg, 'after': after_avg, 'change_pct': change_pct}\n",
    "        print(f\"  {category:<15}: delta={change_pct:.4f}%\")\n",
    "\n",
    "print(f\"\\nALL {len(weight_changes)} layer types show weight changes = FULL TRAINING\")"
   ],
   "id": "sV7sIpe71NzA"
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 966
    },
    "id": "bCGwj37l1NzA",
    "outputId": "b4bd9011-8796-4441-8a96-d71469007430"
   },
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "<Figure size 1800x1000 with 6 Axes>"
      ],
      "image/png": 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wMFCSFBgYqB07djjUceHCBfNYyr8p++zL+Pn5qUCBAnJ3d5e7u3u6ZezryCyW9Hh7e8vb2zvNfjc3txy9uOnm5qZKAZW098JenbpySlZZ5eFG9zgnWCyWHP+8YEPb5g7aNffQtrkjN9qVzwgAAMD10aO7Tdzdpa5dbduxsdLatc6NBwCQdwQHB2vKlCnODgMu7Nq1a/rnn39UunRpNWrUSJ6enlq3bp15/OjRowoNDVXz5s0lSc2bN9eBAwccprNcu3at/Pz8VKtWLbOMfR0pZVLq8PLyUqNGjRzKWK1WrVu3ziyTlVicrWKRipKkJGuSzkSfcXI0AAAAAAAAN49bmW+j7t2lmTNt24sXS926OTMaAHBxoaFSeLiUnGy7s8JicTxevLgUFJSjp7SkPkcqY8eO1bhx47Jd786dO1WwYMGbjMqmTZs2uuuuu0ge3iFeeukldenSRRUqVNC5c+c0duxYubu7q0+fPvL399fAgQM1cuRIFS1aVH5+fnrxxRfVvHlzNWvWTJLUvn171apVS08++aTef/99hYWF6Y033tDgwYPNEXaDBg3SJ598opdfflkDBgzQb7/9pnnz5mnFihVmHCNHjlTfvn3VuHFjNWnSRFOmTFFMTIz69+8vSVmKxdkqFalkbp+IPKHggGDnBQMAAAAAAHALSPrdRvfdJxUqJF27Ji1bJiUlSR58AgCQfaGhUvXqssTFyTOjMj4+0tGjOZr4O3/+vLk9d+5cvfnmmzp69Ki5r1ChQua2YRhKTk6WRxZ+0JcoUSLHYsSd4cyZM+rTp48iIiJUokQJtWzZUtu3bze/S5MnT5abm5t69uyp+Ph4dejQQdOnTzdf7+7uruXLl+v5559X8+bNVbBgQfXt21cTJkwwy1SsWFErVqzQiBEjNHXqVJUrV05ffvmlOnToYJZ59NFHFR4erjfffFNhYWG66667tHr1apUqVcosk1kszlYxoKK5fSLyhO6reJ8TowEAAACAmxOddF3hidG6lBht/htrTVCSYVWSkaxkw6pkWZX8/88lycPiLg+Lm9wt7nKXmzwsbvKwuKuQu4+Ke/qphKef+a+ve9olGADkPaScbiMfH6ljR2n+fCkiQtqyRWrd2tlRAYALunRJiou7cZm4OFu5HEz62a9B5u/vL4vFYu7bsGGD2rZtq5UrV+qNN97QgQMHtGbNGpUvX14jR47U9u3bFRMTo5o1a2rixIlq166dWVdwcLCGDx+u4cOHS7KNKJw5c6ZWrFihX375RWXLltVHH32krinzRN+EhQsX6s0339Tx48dVunRpvfjiixo1apR5fPr06Zo8ebJOnz4tf39/tWjRQgsXLpQkLViwQOPHj9fx48fl6+urBg0aaMmSJbc8OhE376effrrhcR8fH3366af69NNPMyxToUIFrVy58ob1tGnTRn/++ecNywwZMkRDhgy5pVicKXXSDwAAAADykqvJsTofH+mQ0EvZtk/wxRtJuRpHQTdvh0RgcU8/lbTbLuHpp7LeReXtluHt2QBuA5J+t1n37rakn2Sb4pOkHwDkL6+88oo+/PBDVapUSUWKFNHp06fVqVMnvf322/L29ta3336rLl266OjRowq6QUJy/Pjxev/99/XBBx9o2rRpeuKJJ3Tq1CkVLVo02zHt3r1bvXv31rhx4/Too49q69ateuGFF1SsWDH169dPu3bt0tChQ/Xdd9/pnnvuUUREhDZu3CjJNrqxT58+ev/99/Xwww/r6tWr2rx5swzDuOk2AvKS1NN7AgAAAICzRCdd16HrZ3To+mkdvn5Gh6+f0en4CGeHJUmKscYrJj5cp+LDMyzjITdVLFBKNX3LqZZvOdXyLa/qvmXk4+Z1GyMF7mwk/W6zTp1sU3omJdmSfpMmpV2GCgDuWI0bS2FhmZdLSMhafQ8+KHlloWMZGCjt2pW1OjMxYcIEPfDAA+bzokWLqn79+ubzt956S4sWLdLSpUtvODqqX79+6tOnjyTpnXfe0ccff6wdO3bowQcfzHZMkyZN0v33368xY8ZIkqpVq6ZDhw7pgw8+UL9+/RQaGqqCBQvqoYceUuHChRUUFKS6detKsiX9kpKS1KNHD1WoUEGSzGNAflDBv4IsssiQQdIPAAAAwG1zJSnm/xN8Z3Q4xpboO5tw2dlh3ZIkWfV37Hn9HXteSyN2SpLc5aaKPiVVq2B5MxlY3besCpAIBHIFSb/bLCBAattWWrtWOnlS2r9fsrsWDAB3trAw6ezZnKsvPOO7z3JL48aNHZ5fu3ZN48aN04oVK8wEWmxsrEJDQ29YT7169cztggULys/PTxcvXrypmA4fPqxu3bo57GvRooWmTJmi5ORkPfDAA6pQoYIqVaqkBx98UB06dFCXLl3k5+en+vXr6/7771fdunXVoUMHtW/fXr169VKRIkVuKhYgr/H28FY5v3I6HX2apB8AAACAXJFsWLXv2kn9ee2Emeg75+IJvqxKllXH48J0PC7MIRFYwaeEavmWVy3fcmriV0VVC5RxcqRA/kDSzwm6d7cl/STbaD+SfgDw/+zWzLuhhISsJfRKlMj6SL8cknqdu5deeklr167Vhx9+qCpVqqhAgQLq1auXEjIZrejp6TgHvsVikdVqzbE47RUuXFh79uzRhg0btGbNGo0dO1bjx4/Xjh07VKRIEa1du1Zbt27VmjVrNG3aNL3++uv6448/VLFixcwrB1xApSKVdDr6tCJiI3Ql7or8ffydHRIAAAAAFxeTHKct0Ue0Ieqgtlw5oqjkGGeHlGcky6oTcRd0Iu6Cll+2zbxUxquoWvvXUpuAOmpUuLI8Le5OjhJwTW7ODiAzmzZtUpcuXVSmTBlZLBYtXrzYPJaYmKjRo0erbt26KliwoMqUKaOnnnpK586dc17AWdC167/bdm8HALBrl3TmTOaP1auzVt/q1VmrL4em9kzPli1b1K9fPz388MOqW7euAgMDdfLkyVw7X3pq1qypLVu2pImrWrVqcne3daI9PDzUrl07vf/++9q3b59Onjyp3377TZIt4diiRQuNHz9ef/75p7y8vLRo0aLb+h6A3GS/rl9IVIgTIwEAAADgys4nRGrOxc0a9Pfnar1vjP574lutuLybhF8WnEu4rDnhv+u5v2eozd7/b7uI3YpOuu7s0ACXkudH+sXExKh+/foaMGCAevTo4XDs+vXr2rNnj8aMGaP69esrMjJSw4YNU9euXbUrFy/g3qpy5aS775Z27pT27rVN8xkc7OSgAAC5omrVqvr555/VpUsXWSwWjRkzJtdG7IWHh2vv3r0O+0qXLq1Ro0bp7rvv1ltvvaVHH31U27Zt0yeffKLp06dLkpYvX64TJ07o3nvvVZEiRbRixQpZrVZVr15df/zxh9atW6f27durZMmS+uOPPxQeHq6aNWvmynsAnME+6Xci8oTuCrzLecEAAAAAcBmGYejg9dPaEHVQm64c1NHYvD0YxVVcs8ZpTeRerYncKw+56a5CFdU6oLba+NdWkE8JZ4cH5Gl5PunXsWNHdezYMd1j/v7+WpsyT+b/++STT9SkSROFhoYqKCjodoR4U7p3tyX9JGnJEmnYMKeGAwCupXhxycdHiovLuIyPj62ck02aNEkDBgzQPffco+LFi2v06NGKjo7OlXP9+OOP+vHHHx32vfXWW3rjjTc0b948vfnmm3rrrbdUunRpTZgwQf369ZMkBQQE6Oeff9a4ceMUFxenqlWr6rvvvlPt2rV15MgRbdq0SVOmTFF0dLQqVKigjz76KMPfzYArSp30AwAAAICMJBnJ2hZ9VOuj/tKmK4cUnpg7f+PDJklW7br2j3Zd+0cfnVmqij4l1dq/tu4PqKt6hYKdHR6Q5+T5pF92XblyRRaLRQEBAc4O5Ya6d5def922vXgxST8AyJagIOnoURnh4UpKTpaHu7ssFotjmeLFbeVySb9+/cykmSS1adNGhmGkKRccHGxOk5li8ODBDs9TT/eZXj1RUVE3jGfDhg03PN6zZ0/17Nkz3WMtW7Z0eL1hGEpKSpJkmxp0dVanUwVcFEk/AAAAAJk5Ex+hny9t19KInST6nCgk7qJC4i5q9oX1quRTSj2KN1OXYo0V4FHQ2aEBeUK+SvrFxcVp9OjR6tOnj/z8/DIsFx8fr/j4ePN5yogLq9WaY1OuWa1WGYaRYX3Vq0tVq1r0998WbdpkKDzcULFiOXLqfC+ztsXNoV1zD22bvpR2SXlkW/nyUvnyMhITJU9PpVvDzdQLSf8mHrP72aR8nun9TuX/APIqkn4AAAAA0pNoTdK6qAP6+dJ27bh6XEb6Vx/gJCfiLujDM0v08dkVuj+grnqUaKa7C1VJe2M4cAfJN0m/xMRE9e7dW4Zh6LPPPrth2YkTJ2r8+PFp9oeHhyvuRlPFZYPVatWVK1dkGIbc3NzSLfPAA4X099+FZLVaNGfOFfXunTPnzu+y0rbIPto199C26UtMTJTValVSUpI5qiy7DMNQcnKyJNGhy0G30q5JSUmyWq2KiIiQp6enw7GrV6/mWIxATirhW0IFPQsqJjGGpB8AAAAAnU+I1E8Xf9eSiB2KTIpxdjjIRIKRpFWRf2pV5J8K8i6uXiXu0cPFmsrPo4CzQwNuu3yR9EtJ+J06dUq//fbbDUf5SdKrr76qkSNHms+jo6NVvnx5lShRItPXZpXVapXFYlGJEiUyvMjfp480fbpt+7ff/DVkSM6cO7/LStsi+2jX3EPbpi8uLk5Xr16Vh4eHPDxu7ddR6uQScsbNtKuHh4fc3NxUrFgx+fj4OBxL/RzIKywWiyoVqaQDFw/oZNRJJVuT5e7m7uywAAAAANxmu6/+ox8ubtKGqINKFrPVuKLQ+EuadGapPju3Wl2L3a3HS7ZSsE9JZ4cF3DYun/RLSfj9/fffWr9+vYplYY5Mb29veXt7p9nv5uaWoxfkLRbLDets3lwqVUq6cEFas8aiuDiLfH1z7PT5WmZti5tDu+Ye2jYtNzc3WSwW83EzDMMwX8tIv5xzK+2a8nmm933n+4+8LCXpl2hN1NmrZxXkn3trggIAAADIOxKsSVp1eY9+uLhZR2PPOjsc5JBYa4Lmhm/RvPCtau5XTU+UvFct/Gpw/Qj5Xp5P+l27dk3Hjx83n4eEhGjv3r0qWrSoSpcurV69emnPnj1avny5kpOTFRYWJkkqWrSovLy8nBV2lri7S127SjNnSrGx0tq1Urduzo4KAADgzmO/rl9IZAhJPwAAACCfS7AmaW74Fn0dtk6Xk645OxzkEkOGtkYf1dboo6rkU0ovlHlQDxSp7+ywgFyT52+537Vrlxo0aKAGDRpIkkaOHKkGDRrozTff1NmzZ7V06VKdOXNGd911l0qXLm0+tm7d6uTIs6Z793+3Fy92VhQA4DxWK9Nl5Cd8nnBV9kk/1vUDAAAA8i+rYdXSiJ3qdnCiPjyzhITfHeRE3AW9dOIbPX54sv6I/tvZ4QC5Is+P9GvTpo0Mw8jw+I2OuYL77pMKFZKuXZOWLZOSkqRbXNoKAFyCl5eX3NzcdO7cOZUoUUJeXl7ZnmLBMAwlJSXJw8OD6Rly0M20q2EYSkhIUHh4uNzc3PL8aHsgNZJ+AAAAQP63Ieqgpp1doeNxYc4OBU508PppPfv3Z2ruV13DynZWTd9yzg4JyDGkl5zMx0fq2FGaP1+KiJC2bJFat3Z2VACQ+9zc3FSxYkWdP39e586du6k6DMOQ1Wo11wdEzriVdvX19VVQUBDr98HlOCT9okj6AQAAAPnJn9dCNOXMcu2NCXF2KMhDtkUf1fboY+pQ5C4NKdtR5b2LOzsk4JaR9MsDune3Jf0k2xSfJP0A3Cm8vLwUFBSkpKQkJScnZ/v1VqtVERERKlasGEmmHHSz7eru7s6oS7is4IBgc5uRfgAAAED+8HfseU07u1Ibrxx0dijIowwZWh35p36N2q+exZvpudLtVcyzsLPDAm4aSb88oFMn25SeSUm2pN+kSRLXSwHcKSwWizw9PeXp6Znt11qtVnl6esrHx4ekXw6iXXEn8vHwUdnCZXX26lmSfgAAAICLO58QqennVmt5xC5Z5drLQ+H2SDKSNTd8i5ZG7NR/St6rfoH3qZC7j7PDArKNK3l5QECA1LatbfvkSWn/fmdGAwAAcGdKmeLzYsxFXUu45uRoAAAAAGRXVFKMPji9RF3/mqilETtJ+CHbYq0Jmhn2qzofeFvfXtigRGuSs0MCsoWkXx7Rvfu/24sXOysKAACAO5f9un4hkaz1AQAAALiStZH71P3ge/r+4kYlGCRqcGuikmP00ZmlevTwJB2MOe3scIAsI+mXR3Tt+u82ST8AAIDbzz7pxxSfAAAAgGuITLqml098q5dOfKPIJGbsQM76Jy5MTx2Zqk/OrmTUH1wCSb88olw56e67bdt790oh3FwOAABwW5H0AwAAAFzLr5H71ePg+/olcq+zQ0E+liSrZob9qj5HJuvw9TPODge4IZJ+eYj9FJ9LljgtDAAAgDtSxYCK5jZJPwAAACDvikqK0egT32nUidm6zOg+3CZ/x57Xfw5P0SdnVzHqD3kWSb88hHX9AAAAnMdhpF8UST8AAAAgL/o1cr8ePvieVkf+6exQcAeyjfpby6g/5Fkk/fKQmjWlqlVt25s3S5cuOTceAACAO0lgoUD5ePhIYqQfAAAAkNdEJcXoFUb3IY9IGfX36blVSjSSnR0OYCLpl4dYLP+O9rNapeXLnRoOAADAHcVisZij/UIiQ2Q1rE6OCAAAAIAk/RZ1QD0Ovq9VjO5DHpIkq744v1aPH57EqD/kGST98him+AQAAHCelKRffHK8zl897+RoAAAAgDvb1eRYvRryvUb8M0sRSVedHQ6QrmP/P+pv+rnVSubmUTgZSb88pmlTqVQp2/aaNdL1686NBwAA4E5SKcBuXT+m+AQAAACc5mTcRT1xeIpWXt7j7FCATCXJqs/Pr9Hg4zMVnRTr7HBwByPpl8e4u0tdu9q2Y2NtiT8AAADcHikj/SSSfgAAAICzbLlyWP85MlWn4sOdHQqQLduij+qJI1MUEnfB2aHgDkXSLw96+OF/t5niEwAA4PYh6QcAAAA41zcXNujF41/pajKjpeCaQuPD9eSRqfr9ymFnh4I7EEm/POi++6RChWzby5ZJSUnOjQcAAOBO4ZD0iyLpBwAAANwuCdYkjTk5R5POLFWyWBcNru1qcpxePP6lZof95uxQcIch6ZcHeXtLnTrZti9fln7/3bnxAAAA3CkqFqlobjPSDwAAALg9LiVGa+CxT7U0YqezQwFyjFWGJp9drtdDflSClZE9uD1I+uVR3bv/u80UnwAAALeHr6evAgsFSiLpBwAAANwOB2NOq8/hydofc8rZoQC5YvnlXRpw7BOFJ0Y7OxTcAUj65VGdOkmenrbtxYslw3BqOAAAAHeMlCk+w66F6XridSdHAwAAAORfqy7v0YCjn+hi4hVnhwLkqgMxoepzeJL+igl1dijI50j65VH+/lLbtrbtU6ekffucGw8AAMCdwn5dv5DIECdGAgAAAORPVsOqqWeX65WQ7xVnJDo7HOC2CE+M1oCjn2hFxG5nh4J8jKRfHsYUnwAAALdfpYB/k35M8QkAAADkrOvJ8Rr+zyx9Hfabs0MBbrt4I0mvnfxBU84sl8H0fsgFJP3ysK5d/90m6QcAAHB7OIz0i2KkHwAAAJBTriXH6fm/v9DGKwedHQrgVLMu/Ka3QufLalidHQryGZJ+eVjZslKTJrbtffukEK45AQAA5Dr7pB8j/QAAAICcEZ10Xc8e+0x7Y7jICUjSwkvb9ebJn5RM4g85iKRfHmc/xeeSJU4LAwAA4I5B0g8AAADIWZFJ1/T0sc908PppZ4cC5CnLLu/SKyHfK8lIdnYoyCdI+uVxrOsHAABwe5UuXFre7t6SSPoBAAAAtyoi8aoGHp2uo7FnnR0KkCetidyrl058o0RrkrNDQT5A0i+Pq1FDqlbNtr15s3TpknPjAQAAyO/cLG6qWKSiJFvSj8XVAQAAgJtzMeGKBhz9VP/EhTk7FCBPWx/1l4b/87USSPzhFpH0y+Msln9H+1mt0vLlTg0HAADgjpAyxWdsUqwuxFxwcjQAAACA64lIvKpn//5MJ+MvOjsUwCX8Hn1Eo07MViJTfeIWkPRzAUzxCQAAcHtVCmBdPwAAAOBmRSXF6Lm/ZygkjoQfkB2brhzSKye+U7JhdXYocFEk/VxA06ZSqVK27TVrpOvXnRsPAABAfpcy0k8i6QcAAABkR3RSrAb9/bn+jj3v7FAAl/Rr1H69cfJHWUn84SaQ9HMBbm5St2627dhYW+IPAAAAuYekHwAAAJB9MclxeuH45zp8/YyzQwFc2srLezTu1DzWmEe2kfRzEUzxCQAAcPtULFLR3CbpBwAAAGQuzpqgIce/1IGYUGeHAuQLSyJ26J3TC50dBlwMST8Xcd99UqFCtu1ly6SkJOfGAwAAkJ9VDCDpBwAAAGTHmyd/0p5r9J2BnDQvfKu+ubDB2WHAhZD0cxHe3lKnTrbty5el3393bjwAAAD5WWHvwirhW0ISST8AAAAgMzPPr9UvkXudHQaQL009s1xbrhx2dhhwEST9XAhTfAIAANw+Kev6nb16VnFJcU6OBgAAAMibNkT9pU/PrXZ2GEC+lSyrRod8p5NxF50dClwAST8X0qmT5Olp2168WGINTwAAgNyTkvSTpJNRJ50XCAAAAJBH/R17Xq+F/CBDXKgEctPV5DgNPf6VopNinR0K8jiSfi7E319q29a2feqUtG+fc+MBAADIz+yTfkzxCQAAADiKSorR8ONfK8Ya7+xQgDvCqfhwvRLynZINq7NDQR5G0s/FMMUnAACw9+6778pisWj48OHmvri4OA0ePFjFihVToUKF1LNnT124cMHhdaGhoercubN8fX1VsmRJ/fe//1VSUpJDmQ0bNqhhw4by9vZWlSpVNHv27DTn//TTTxUcHCwfHx81bdpUO3bscDielVjyKpJ+AAAAQPqSjGS9dOIbnUmIcHYowB1lS/QRTT6zzNlhIA8j6ediunb9d5ukHwAAd7adO3fq888/V7169Rz2jxgxQsuWLdP8+fO1ceNGnTt3Tj169DCPJycnq3PnzkpISNDWrVv1zTffaPbs2XrzzTfNMiEhIercubPatm2rvXv3avjw4Xr66af1yy+/mGXmzp2rkSNHauzYsdqzZ4/q16+vDh066OLFi1mOJS8j6QcAAACk7/3Ti7Xz6nFnhwHckb67uFFLI3Y6OwzkUST9XEzZslKTJrbtffukkBDnxgMAAJzj2rVreuKJJzRz5kwVKVLE3H/lyhV99dVXmjRpku677z41atRIs2bN0tatW7V9+3ZJ0po1a3To0CF9//33uuuuu9SxY0e99dZb+vTTT5WQkCBJmjFjhipWrKiPPvpINWvW1JAhQ9SrVy9NnjzZPNekSZP0zDPPqH///qpVq5ZmzJghX19fff3111mOJS8j6QcAAACkNT98q+aGb3F2GMAd7a1T87X/2klnh4E8iKSfC7Kf4nPJEqeFAQAAnGjw4MHq3Lmz2rVr57B/9+7dSkxMdNhfo0YNBQUFadu2bZKkbdu2qW7duipVqpRZpkOHDoqOjtbBgwfNMqnr7tChg1lHQkKCdu/e7VDGzc1N7dq1M8tkJZa8rGzhsvJ085RE0g8AAACQpN1X/9G7pxc5OwzgjpdgJGnEP7N0ISHK2aEgj/FwdgCZ2bRpkz744APt3r1b58+f16JFi9TdLutlGIbGjh2rmTNnKioqSi1atNBnn32mqlWrOi/oXNa9u/Taa7btxYsluyV8AADAHeCnn37Snj17tHNn2uk8wsLC5OXlpYCAAIf9pUqVUlhYmFnGPuGXcjzl2I3KREdHKzY2VpGRkUpOTk63zJEjR7IcS3ri4+MVHx9vPo+OjpYkWa1WWa05s2C51WqVYRg3rM8ii4IDgvX35b91IvKEkpOTZbFYcuT8+VlW2hY3h7bNHbRr7qFtc0dutSufE4DMnI2/rFEnZivJSHZ2KAAkXUq6qhH/zNLX1QfLx83L2eEgj8jzSb+YmBjVr19fAwYMSHf9l/fff18ff/yxvvnmG1WsWFFjxoxRhw4ddOjQIfn4+Dgh4txXo4ZUrZp07Ji0ebN06ZJUvLizowIAALfD6dOnNWzYMK1duzbf9nUmTpyo8ePHp9kfHh6uuLi4HDmH1WrVlStXZBiG3NwynvyirG9Z/X35b8Ukxuhw6GEVL0CnKzNZbVtkH22bO2jX3EPb5o7caterV6/mWF0A8p84a4KG//OVIpNinB0KADsHr5/W+FPzNLHif5wdCvKIPJ/069ixozp27JjuMcMwNGXKFL3xxhvq1q2bJOnbb79VqVKltHjxYj322GO3M9TbxmKxjfZ7/33JapWWL5f69XN2VAAA4HbYvXu3Ll68qIYNG5r7kpOTtWnTJn3yySf65ZdflJCQoKioKIcRdhcuXFBgYKAkKTAwUDt27HCo98KFC+axlH9T9tmX8fPzU4ECBeTu7i53d/d0y9jXkVks6Xn11Vc1cuRI83l0dLTKly+vEiVKyM/PL7MmyhKr1SqLxaISJUrc8IJpjVI1tOHMBlsc7tGqVbJWjpw/P8tq2yL7aNvcQbvmHto2d+RWu+bXm4kA5IxpZ1fqWOx5Z4cBIB0rL+/Rvf611LFow8wLI9/L80m/GwkJCVFYWJjDOjH+/v5q2rSptm3blm+TftK/ST/JNsUnST8AAO4M999/vw4cOOCwr3///qpRo4ZGjx6t8uXLy9PTU+vWrVPPnj0lSUePHlVoaKiaN28uSWrevLnefvttXbx4USVLlpQkrV27Vn5+fqpVq5ZZZuXKlQ7nWbt2rVmHl5eXGjVqpHXr1plTr1utVq1bt05DhgyRJDVq1CjTWNLj7e0tb2/vNPvd3Nxy9OKmxWLJtM7KRSub2yevnNQ9Qffk2Pnzs6y0LW4ObZs7aNfcQ9vmjtxoVz4jABnZc+2Efry42dlhALiBd0MXqUnhqirmWdjZocDJXDrpl7IWTHprybjCOjG34u67pVKlLLpwwaI1awxdu2bI1zdXTpXnsC5E7qBdcw9tm3to29zBOjF5W+HChVWnTh2HfQULFlSxYsXM/QMHDtTIkSNVtGhR+fn56cUXX1Tz5s3VrFkzSVL79u1Vq1YtPfnkk3r//fcVFhamN954Q4MHDzaTbYMGDdInn3yil19+WQMGDNBvv/2mefPmacWKFeZ5R44cqb59+6px48Zq0qSJpkyZopiYGPXv31+S7WaszGLJ6yoVqWRuh0SGODESAAAA4PaLtSZo7MmfZJXh7FAA3EBUcozeCp2vKZUHODsUOJlLJ/1uVl5aJ+ZWPPCAn77/3lexsRbNnx+ljh3jM39RPsC6ELmDds09tG3uoW1zB+vEuL7JkyfLzc1NPXv2VHx8vDp06KDp06ebx93d3bV8+XI9//zzat68uQoWLKi+fftqwoQJZpmKFStqxYoVGjFihKZOnapy5crpyy+/VIcOHcwyjz76qMLDw/Xmm28qLCxMd911l1avXu1wQ1ZmseR19km/E5EnnBgJAAAAcPtNO7tCofGXnB0GgCxYH/WXVl7erU5FGzk7FDiRxTAMl7lNw2KxaNGiReYUUidOnFDlypX1559/6q677jLLtW7dWnfddZemTp2abj3pjfQrX768IiMjc3SdmPDw8Fxdu2DVKumhh2x1P/WUoVmzXOajvCW3o23vRLRr7qFtcw9tmztyq12jo6NVpEgRXblyJcd+3+LOEB0dLX9//xz97litVnN60xt9z6/EXVHAewGSpDbBbbS+7/ocOX9+ltW2RfbRtrmDds09tG3uyK12zY3ftwBc256rJzTg2KcyGOUHuAx/d1/9XPtlFffkd/mdyqVH+lWsWFGBgYFat26dmfSLjo7WH3/8oeeffz7D1+WldWJuRbt2UqFC0rVr0vLlFlmtFnm49CeadawLkTto19xD2+Ye2jZ3sE4MYOPv469iBYopIjaCkX4AAAC4Y8RaE/TmqTkk/AAXcyX5uv53aoGmVGGazztVnr/6du3aNe3du1d79+6VJIWEhGjv3r0KDQ2VxWLR8OHD9b///U9Lly7VgQMH9NRTT6lMmTLmaMD8zNtb6tTJtn35svT7786NBwAAID9KmeLz9JXTSkhOcHI0AAAAQO77+OwKnY6PcHYYAG7C+it/aUXEbmeHASfJ80m/Xbt2qUGDBmrQoIEkaeTIkWrQoIHefPNNSdLLL7+sF198Uc8++6zuvvtuXbt2TatXr5aPj48zw75t7HObixc7KwoAAID8KyXpZ8jQqahTTo4GAAAAyF27r/6jORcZXQC4svdOL9KlxGhnhwEnyPNJvzZt2sgwjDSP2bNnS7JNPzZhwgSFhYUpLi5Ov/76q6pVq+bcoG+jTp0kT0/b9uLFkuus0AgAAOAaUpJ+kpjiEwAAAPlarDVBY0/9xLSegIu7knxdb52a7+ww4AR5PumHG/P3l9q2tW2fOiXt2+fceAAAAPIbkn4AAAC4U0w9s5xpPYF8YsOVg1oescvZYeA2I+mXDzDFJwAAQO4h6QcAAIA7wa6rx/VT+BZnhwEgB713epHCmebzjkLSLx/o2vXfbZJ+AAAAOatiQEVz+0QUST8AAADkP4nWJI07NZdpPYF8Jjo5VhNDFzo7DNxGJP3ygbJlpSZNbNv79kkhIc6NBwAAID8p719e7hZ3SYz0AwAAQP40L3wr03oC+dS6qAPae42kwZ2CpF8+YT/F55IlTgsDAAAg3/Fw81CFgAqSbEk/w+DuZwAAAOQfMclxmhn2q7PDAJCLpp5d4ewQcJuQ9MsnWNcPAAAg96Ss6xcdH63LsZedHA0AAACQc765sEGRSdecHQaAXLTn2gltunLI2WHgNiDpl0/UqCFVq2bb3rxZunTJufEAAADkJ5UCKpnbTPEJAACA/CIi8aq+u7DR2WEAuA0+PrtCVsPq7DCQy0j65RMWy7+j/axWaflyp4YDAACQr6SM9JNI+gEAACD/+OL8Wl23xjs7DAC3wd+x57Xi8h5nh4FcRtIvH2GKTwAAgNxB0g8AAAD5zZn4CC28tM3ZYQC4jaafW61Ea5Kzw0AuIumXjzRtKpUqZdtes0a6ft258QAAAOQXJP0AAACQ33x6bpUSjWRnhwHgNjqXcFnzLm11dhjIRST98hE3N6lbN9t2bKwt8QcAAIBb55D0iyLpBwAAANd29PpZrbr8p7PDAOAEM8//qpjkOGeHgVzi4ewAkLO6d5e++MK2vXix45SfaYSGSpcuZXy8eHEpKCjnggMAAHBRRQoUUYBPgKLiohjpBwAAAJf38dkVMmQ4OwwAThCZdE3fXtig58s86OxQkAtI+uUz990nFSokXbsmLVsmJSVJHul9yqGhUvXqUtwNMvo+PtLRoyT+AAAAZBvtt+f8HoVeCVVicqI83T2dHRIAAACQbbuuHtfv0UecHQYAJ/r2wkb1LtFCxTwLOzsU5DCm98xnvL2lTp1s25cvS5s3Z1Dw0qUbJ/wk2/EbjQQEAAC4g6RM8Wk1rAq9EurkaAAAAICbM+XscmeHAMDJrlvjNfP8WmeHgVxA0i8fevjhf7cXL3ZaGAAAAPlKpQC7df2Y4hMAAAAuaF3kfh2I4QY2ANKCS9t0Jj7C2WEgh5H0y4c6dpQ8/3+2qcWLJYPpuQEAAG5Zykg/iaQfAAAAXNNXYeucHQKAPCLRSNa3FzY4OwzkMJJ++ZC/v21tP8m2dN/evU4NBwAAIF8g6QcAAABXtvdaiA5eP+3sMADkIUsjdupqcqyzw0AOIumXT3Xv/u82U3wCAADcOvukX0hUiBMjAQAAALLvh4ubnR0CgDwm1pqgRZf+cHYYyEEk/fKprl3/3SbpBwAAcOuC/IPkZrF1nxnpBwAAAFdyISFKv0Xud3YYAPKgny7+LqthdXYYyCEk/fKpMmWkpk1t2/v3Sye4LgUAAHBLPN09FeQfJImkHwAAAFzLT+G/K0lc1AeQ1tmEy9pw5aCzw0AOIemXj9lP8blkSaqDxYtLPj43rsDHx1YOAAAAkv6d4jMyLlKRsZFOjgYAAADIXJw1QQvDtzs7DAB52A8XNjk7BOQQkn752A3X9QsKko4elXbv/vfx0kv/Hm/b1nY8KOg2RAoAAOAaKgWwrh8AAABcy8rLe3Ql+bqzwwCQh+269o/+jj3n7DCQA0j65WM1akjVq9u2f/9dCg9PVSAoSGrY8N/HW29JpUvbjq1fL12+fFvjBQAAyOtSRvpJTPEJAAAA18AoPwBZsYCfFfkCSb98LmW0n9UqLV+eSWEfH+nVV/99PmFCboUFAADgkkj6AQAAwJUcu35Of10PdXYYAFzAisu7FWdNcHYYuEUk/fK5G07xmZ5nnpHKlLFtL1ok7d2b80EBAAC4KJJ+AAAAcCULLzFyB0DWXE2O1drI/c4OA7eIpF8+16SJFBho216zRoqJyeQFqUf7jR+fa7EBAAC4mopFKprbJP0AAACQl8VZE7Ti8m5nhwHAhfzMjQIuj6RfPufmJnXrZtuOi7Ml/jL19NNS2bK27cWLpT//zK3wAAAAXEqxAsVU2KuwJJJ+AAAAyNvWRu7T1eRYZ4cBwIXsuXZCIXEXnB0GbgFJvztAtqf4ZLQfAABAuiwWiznF56krp5RkTXJyRAAAAED6mNoTwM1YGM7PDldG0u8O0LatVNh2Q7qWLZOSsnJtauDAf0f7LVki7dmTa/EBAAC4kpSkX5I1SWeizzg5GgAAACCtsIRI/XktxNlhAHBBqyP/lGEYzg4DN4mk3x3A21vq1Mm2HRkpbd6chRf5+Eivvfbvc0b7AQAASPo36ScxxScAAADypg1RB50dAgAXFZ4YrYPXTzs7DNwkkn53iGxP8SnZRvuVK2fbXrpU2s3CvwAAACT9AAAAkNdtvELSD8DN42eI6yLpd4fo2FHy9LRtL14sZWl0rrc3o/0AAABSIekHAACAvCwmOU47rx53dhgAXNhGRgu7LJJ+dwh/f+m++2zboaHS3r1ZfOGAAVL58rbtZcsY7QcAAO54JP0AAACQl22NPqpEI9nZYQBwYUdjz+l8QqSzw8BNIOl3B7mpKT5Tj/YbNy7nAgIAAHBBFfwryCKLJJJ+AAAAyHsYoQMgJ/CzxDWR9LuDdO3673aWk36S1L//v6P9li+Xdu3KybAAAABcireHt8r52dY9JukHAACAvCTZsOr36MPODgNAPrCBdf1cEkm/O0iZMlLTprbt/fulE1m9RuXtLb3++r/PGe0HAADucClTfEbERuhK3BUnRwMAAADY7Lt2UpFJMc4OA0A+sOvqccUkxzk7DGQTSb87jP0Un0uWZOOF9qP9VqyQdu7MybAAAABciv26fiFRIU6MBAAAAPjXRkbmAMghiUaytkYfdXYYyCaSfneYm1rXT5K8vBjtBwAA8P/sk35M8QkAAIC8YgNrcAHIQfxMcT0k/e4wNWpI1avbtn//XQoPz8aL+/eXgoJs2ytXSjt25Hh8AAAAroCkHwAAAPKak3EXdTL+orPDAJCP/H7lsJINq7PDQDaQ9LsDpYz2s1ql5cuz8UJG+wEAAEgi6QcAAIC8Z+OVQ84OAUA+E5Uco33XTjo7DGSDyyf9kpOTNWbMGFWsWFEFChRQ5cqV9dZbb8kwDGeHlmfd9BSfktSvn1Shgm171Srpjz9yJigAAAAXQtIPAAAAec3GqL+cHQKAfGjDFX62uBKXT/q99957+uyzz/TJJ5/o8OHDeu+99/T+++9r2rRpzg4tz2rSRAoMtG2vWSPFxGTjxYz2AwAAUAnfEiroWVCSFBIV4uRoAAAAcKeLTorVXkbjAMgFm68cdnYIyAaXT/pt3bpV3bp1U+fOnRUcHKxevXqpffv22sF6cxlyc5O6dbNtx8XZEn/Z0revFBxs2169Wtq+PSfDAwAAyPMslv9j777jm6reOI5/knRDF3QBbaHIRvYSkSmKiAgiCoqCiAvBn4gTRYaIuAUVxY0LFRBRUVREQVQUZCgIVGVYVtltWZ3J749rU8psaW5v0n7fr9d9ccfJydOnMb3myTnH5h7ttyVtC3nOPIsjEhEREZHybN2RreShdbdExPM2Z+7mYN5Rq8OQIvL5ot/555/PwoUL+euvvwD4/fff+fHHH+nevbvFkXm3Ek3xefxov/HjPRCRiIiIiG/JL/pl52Wz4+AOi6MRERERkfJs3ZGtVocgImWUCxfrj2yzOgwpIj+rAyipBx54gIyMDOrVq4fD4SAvL4+JEycyYMCAUz4mKyuLrKws93FGRgYATqcTp9Mz34hxOp24XC6P9edpHTtCaKiNgwdtfP65i+xsF37FeTVcfz22iROxbdkCX32F86efoG1bs8ItxNtz66uUV/Mot+ZRbs1hVl71e5Ky5vh1/RLCEyyMRkRERETKs/VHtlsdgoiUYeuPbKd1aG2rw5Ai8Pmi38yZM3n//feZMWMGDRs2ZPXq1YwYMYKqVasyaNCgkz5m0qRJjD/J6LQ9e/aQmZnpkbicTifp6em4XC7sdu8cUNmlSziffhrMgQM2Pv/8AO3aZRfr8cF33EH43XcDkDN6NAc++MCMME/gC7n1RcqreZRb8yi35jArrwcPHvRYX77qrbfeol+/foSEhFgdinjA8UW/jjU6WhiNiIiIiJRn6zXST0RMtP6w3mN8hc8X/e69914eeOAB+vfvD0CjRo34999/mTRp0imLfqNGjWLkyJHu44yMDBISEoiOjiYsLMwjcTmdTmw2G9HR0V77QfTVV8Onnxr7778fSWamiypVoH17cDiK0MGwYbhefBHb5s0ELlpEzMaNpTLazxdy64uUV/Mot+ZRbs1hVl6DgoI81peveuCBB7jzzju56qqrGDJkCOeff77VIUkJHF/0ExERERGxQkbuUbZm7bM6DBEpw9Zpek+f4fNFvyNHjpzwgaTD4TjtFGKBgYEEBgaecN5ut3v0w02bzebxPj2pRw+juJeXB598YuOTT2wAxMfDlCnQp88ZOggMhNGjYcgQAOyPPAJff21y1AZvz62vUl7No9yaR7k1hxl51e8Itm/fzueff8706dPp1KkTNWvWZPDgwQwaNIi4uDirw5NiKlT0S1PRT0RERESsobW2RMRsKVl7OZSXSUWHvtDt7Xz+07eePXsyceJEvvjiC7Zs2cInn3zCs88+yxVXXGF1aF5v4UKj4He87duhb1+YM6cInVx/PdT87wOvb76Bn3/2aIwiIiJliZ+fH1dccQWffvopW7du5eabb+b9998nMTGRyy+/nE8//bRIax++/PLLNG7cmLCwMMLCwmjbti3z5893X8/MzGTYsGFUrlyZihUrcuWVV7Jr165CfaSkpNCjRw9CQkKIiYnh3nvvJTc3t1CbRYsW0bx5cwIDA6lVqxbTp08/IZapU6dSo0YNgoKCaNOmDcuWLSt0vSix+KoaETXc+xrpJyIiIiJW0dSeImI2Fy426AsGPsHni34vvPACffv25fbbb6d+/frcc8893HrrrUyYMMHq0LxaXh7ceefJr7lcxr8jRpy8KFiIv78x2i/fuHEeiE5ERKTsi42N5YILLqBt27bY7XbWrFnDoEGDOOecc1i0aNFpHxsfH8/jjz/OihUr+O233+jSpQu9evXizz//BOCuu+7i888/Z9asWSxevJgdO3bQ55gh/Hl5efTo0YPs7Gx+/vln3n77baZPn86YMWPcbTZv3kyPHj3o3Lmze83km266ia+PGdX/0UcfMXLkSMaOHcvKlStp0qQJ3bp1Y/fu3e42Z4rFlwX5BVE1tCqgop+IiIiIWEfT7olIadB7jW/w+aJfaGgokydP5t9//+Xo0aNs3LiRRx99lICAAKtD82pLlsC20/w36nLB1q1GuzO67rqC0X4LFsBPP3kkRhERkbJo165dPP300zRs2JBOnTqRkZHBvHnz2Lx5M9u3b+fqq68+5brE+Xr27Mmll15K7dq1qVOnDhMnTqRixYr88ssvpKen88Ybb/Dss8/SpUsXWrRowVtvvcXPP//ML7/8AsA333zDunXreO+992jatCndu3dnwoQJTJ06lezsbACmTZtGUlISzzzzDPXr12f48OH07duX5557zh3Hs88+y80338zgwYNp0KAB06ZNIyQkhDfffBOgSLH4uvwpPncf3s2h7EMWRyMiIiIi5ZGm9xSR0qD3Gt/g82v6ydnZudOD7fz94eGHYfBg43jcOKP4JyIiIoX07NmTr7/+mjp16nDzzTczcOBAKlWq5L5eoUIF7r77bp566qki95mXl8esWbM4fPgwbdu2ZcWKFeTk5NC1a1d3m3r16pGYmMjSpUs577zzWLp0KY0aNSI2Ntbdplu3bgwdOpQ///yTZs2asXTp0kJ95LcZMWIEANnZ2axYsYJRo0a5r9vtdrp27crSpUsBihTLyWRlZZGVleU+zsjIAMDpdBZp+tOicDqduFyuEveXFJHEjyk/ArBx30YaxTbyRHg+zVO5lRMpt+ZQXs2j3JrDrLzq9yTimw7mHWVr1j6rwxCRcmDdYRX9fIGKfuVUlSqebcd118Gjj8LGjfDtt/Djj3DBBWcdn4iISFkUExPD4sWLadu27SnbREdHs3nz5jP2tWbNGtq2bUtmZiYVK1bkk08+oUGDBqxevZqAgAAiIiIKtY+NjSU1NRWA1NTUQgW//Ov5107XJiMjg6NHj3LgwAHy8vJO2mbDhg3uPs4Uy8lMmjSJ8ePHn3B+z549ZGZmnvJxxeF0OklPT8flcmG3n/3kF7H+BT//qn9XEWuLPU3r8sFTuZUTKbfmUF7No9yaw6y8Hjx40GN9iUjpWX9kGy5cVochIuXAv1l7OJyXSQVHkNWhyGmo6FdOtW8P8fGwfXvBGn7HstmM6+3bF7FDPz9jtN8NNxjH48YZxT8RERFx69ixI82bNz/hfHZ2Nh9++CEDBw7EZrNRvXr1M/ZVt25dVq9eTXp6OrNnz2bQoEEsXrzYjLBL3ahRoxg5cqT7OCMjg4SEBKKjowkLC/PIczidTmw2G9HR0SX6wPTc+HNhhbG/37mfmJgYj8TnyzyVWzmRcmsO5dU8yq05zMprUJA+wBPxRes18kZESokLFxuObKdF6DlWhyKnoaJfOeVwwJQp0LevUeA7WeFv8mSjXZENGAATJhij/RYuNBYELHLVUEREpOwbPHgwl1xyyQmFoYMHDzJ48GAGDhxY5L4CAgKoVasWAC1atGD58uVMmTKFfv36kZ2dTVpaWqERdrt27SIuLg6AuLg4li1bVqi/Xbt2ua/l/5t/7tg2YWFhBAcH43A4cDgcJ21zbB9niuVkAgMDCQwMPOG83W736IebNputxH3WqlTLvb85bbM+1P6PJ3IrJ6fcmkN5NY9yaw4z8qrfkYhvWqc1tkSkFK07sk1FPy+nO7pyrE8fmD0bqlU78drVVxvXiyV/tF++ceNKEp6IiEiZ43K5sNlsJ5zftm0b4eHhJerb6XSSlZVFixYt8Pf3Z+HChe5rycnJpKSkuKcVbdu2LWvWrGH37t3uNgsWLCAsLIwGDRq42xzbR36b/D4CAgJo0aJFoTZOp5OFCxe62xQlFl9XM7Kme39T2iYLIxERERGR8uivozusDkFEyhG953g/jfQr5/r0gV69jEF5K1fCvfeC0wlffAG7d0OxZ6gaMMBY2++ff+C77+CHH6BDB1NiFxER8RXNmjXDZrNhs9m48MIL8fMruAXLy8tj8+bNXHLJJUXub9SoUXTv3p3ExEQOHjzIjBkzWLRoEV9//TXh4eEMGTKEkSNHUqlSJcLCwrjjjjto27Yt5513HgAXX3wxDRo04Prrr+fJJ58kNTWV0aNHM2zYMPcIu9tuu40XX3yR++67jxtvvJHvvvuOmTNn8sUXX7jjGDlyJIMGDaJly5a0bt2ayZMnc/jwYQYPHgxQpFh8XVzFOIL8gsjMzWTTARX9RERERKR07cpOszoEESlH9J7j/VT0ExwO6NTJ2P75B15+GQ4dgkcegRdfLGZn+aP9Bg0yjseNM4p/IiIi5Vjv3r0BWL16Nd26daNixYruawEBAdSoUYMrr7yyyP3t3r2bgQMHsnPnTsLDw2ncuDFff/01F110EQDPPfccdrudK6+8kqysLLp168ZLL73kfrzD4WDevHkMHTqUtm3bUqFCBQYNGsQjjzzibpOUlMQXX3zBXXfdxZQpU4iPj+f111+nW7du7jb9+vVjz549jBkzhtTUVJo2bcpXX31FbGysu82ZYvF1NpuNmpE1WbdnHZsPbMbpcmK3aTINERERETHfkbwsDjuzrA5DRMqRPTkZVocgZ2BzuU62mlv5kpGRQXh4OOnp6YSFhXmkT6fTye7du4mJifGpefF37YJzzoHDh4363bp1ULt2MTvJzYUGDeDvv43jRYugY0ePxeirufV2yqt5lFvzKLfmMCuvZvy99TVvv/02/fr1IygoyOpQfIq336v1/KAn8/6aB8C2u7ZRLewkc6eXI3pvNo9yaw7l1TzKrTl0ryYi+VIy99Dzz0lWhyEi5UiYI5glTSdaHYachu66pZDYWGOKTzBqdw89dBadaG0/ERGRkxo0aJAKfmVQzYhj1vXTFJ8iIiIiUkr25hy0OgQRKWcy8o6S5cyxOgw5DRX95AR3320U/wBmzYJly86ik2uugTp1jP1Fi4xNRESkHKpUqRJ79+4FIDIykkqVKp1yE99UM1JFPxEREREpfbtz0q0OQUTKIX3hwLtpTT85QcWKMHYs3H67cXzfffD992CzFaOT/NF+119vHI8bp8KfiIiUS8899xyhoaHufVux/qCKL1DRT0RERESssFdra4mIBfbkpFMtUF9c9lYq+slJ3XQTTJ4Mf/0FixfDF1/AZZcVs5NrroEJEwo6WbQIOnXyfLAiIiJebNCgQe79G264wbpAxDSFin5pKvqJiIiISOnYo6KfiFhAXzjwbpreU07K3x8ee6zg+IEHIC+vmJ04HDBmTMHx2LHgcnkkPhEREV80ffr0k57Pzc1l1KhRpRuMeExSZJJ7XyP9RERERKS0qOgnIlbQe493U9FPTqlPHzjvPGP/zz/h7bfPopP+/aFuXWP/hx80xaeIiJRr//vf/7jqqqs4cOCA+1xycjJt2rThgw8+sDAyKYkQ/xDiKsYBKvqJiIiISOnRaBsRsYLW9PNuphX9tm7dyrZt29zHy5YtY8SIEbz66qtmPaV4mM0GTz5ZcDxmDBw5UsxONNpPRETEbdWqVWzbto1GjRqxYMECpk6dSvPmzalXrx6///671eFJCeRP8Zl6KJUjOcW9YRIRERERKT6NthERK+i9x7uZVvS79tpr+f777wFITU3loosuYtmyZTz00EM88sgjZj2teFj79tCzp7G/fTs8//xZdNKvH9SrZ+wvWQL/vS5ERETKm3POOYeffvqJPn36cMkll3DXXXfx+uuv8/777xMeHm51eFICx67rtyVti3WBiIiIiEi5oZF+ImKFPTnpVocgp2Fa0W/t2rW0bt0agJkzZ3Luuefy888/8/77759yPRvxTo8/DnZ7wf6+fcXs4PjRfuPGabSfiIiUW1988QUffvghbdu2JSIigjfeeIMdO3ZYHZaUUM2IgqKfpvgUEREREbNlOXPIyDtqdRgiUg7pCwfezbSiX05ODoGBgQB8++23XH755QDUq1ePnTt3mvW0YoIGDWDwYGM/PR0mTjyLTq6+GurXN/aXLIHvvvNYfCIiIr7i1ltv5aqrruL+++9nyZIl/PHHHwQEBNCoUSNmzpxpdXhSAseO9FPRT0RERETMpun1RMQqe7Smn1czrejXsGFDpk2bxpIlS1iwYAGXXHIJADt27KBy5cpmPa2YZPx4CA429qdOhS1bitmBRvuJiIjw008/8euvv3L33Xdjs9mIi4vjyy+/5JFHHuHGG2+0OjwpARX9RERERKQ07c89ZHUIIlJOpeUeJs/ltDoMOQXTin5PPPEEr7zyCp06deKaa66hSZMmAHz22WfuaT/Fd1SrBiNGGPvZ2TB69Fl0ctVVBaP9fvwRFi70VHgiIiI+YcWKFe57omMNGzaMFStWWBCReIqKfiIiIiKlx2azMXfuXKvDOGvjxo2jadOmJeoj25njmWBERIrJhYtcV56pz+GJ9/kbbriB3r17u487derEiPwiRxlmWtGvU6dO7N27l7179/Lmm2+6z99yyy1MmzbNrKcVE91/P+QP0nz/fVi1qpgdOBwwdmzBsUb7iYhIORMYGMjGjRsZPXo011xzDbt37wZg/vz55ObmWhydlESV0CoEOoyp7VX0ExERESmZ1NRU7rjjDmrWrElgYCAJCQn07NmThfoCuZu3j7I5tHIbm++ay7pLXuWPls+RvuifQtddLhep035mXbdXWNPueTbdPpuslAOF2uSmZ5Iyej5rO05lbaeX2PrIN+QdyXZfz96RzsabZ7LmghfYePNMsnekF3r85hFzSV/4t3k/pJRLe2euZn3PN1hz/vP8PegDjqxNdV/b8exi/uzyEut7vMaB+esLPS7t27/YfNfcUo7WPCUt+u3Zs4ehQ4eSmJhIYGAgcXFxdOvWjZ9++gmAnTt30r179xI9x5QpU5g+fXqJ+vBFphX9jh49SlZWFpGRkQD8+++/TJ48meTkZGJiYsx6WjFReHjhEX73338WnfTtaywSCPDTT/Dttx6JTURExBcsXryYRo0a8euvvzJnzhwOHTKm5Pn9998Ze+wXY8Tn2G12akTUAIyin0tfbBIRERE5K1u2bKFFixZ89913PPXUU6xZs4avvvqKzp07M2zYMKvD8xpmj7IpKefRHIJrR1Pt/i4nvb7n7d/Y++Fqqo3qSq3p12AP8mfzHXNwZhV8GXLrw/PJ3LSPmlP7kDS5F4dXbWf7xILPEnc89wN+0RWpM+M6/KIqsHPyD+5rad8kg81G+IW1zfshpdxJ+yaZnc/9QOzN51H7vQEE14li8x1zyN1/hIwfNpL29QaSXuxD3B3t2fboAnLTjgKQdyiL1Jd+OuV/D76opF88uPLKK1m1ahVvv/02f/31F5999hmdOnVi3759AMTFxREYGFii5wgPDyciIqJEfRzL5XL5xBe2TSv69erVi3feeQeAtLQ02rRpwzPPPEPv3r15+eWXzXpaMdnQoVCjhrG/YIGxFYvW9hMRkXLsgQce4NFHH2XBggUEBAS4z3fp0oVffvnFwsjEE/Kn+Dyae5Rdh3dZHI2IiIiIb7r99tux2WwsW7aMK6+8kjp16tCwYUNGjhxZ6J557969XHHFFYSEhFC7dm0+++wz97W8vDyGDBlCUlISwcHB1K1blylTphR6nvxp355++mmqVKlC5cqVGTZsGDk5BdNm7ty5kx49ehAcHExSUhIzZsygRo0aTJ482d0mLS2Nm266iejoaMLCwujSpQu///57oed6/PHHiY2NJTQ0lCFDhpCZmVniPOV6+Ui/sHZJxN3ejvDOtU645nK52PvBSmKHtCa80zkE144m4ZFLyNlzmIxFGwHI3LyPgz9vIX50V0LOrUKFptWodm9n0r5JJmeP8eXJrC37qXRZAwITI4m8rAGZW/YDkHcwk9SXfy5TBRbxDnveX0ml3udS6fKGBNWsTLVRXbEF+bH/s7Vkbt5PhebxhDSII/KSejgqBJK93Rh9unPKEipf2YSAuDCLfwLPKcl7UFpaGkuWLOGJJ56gc+fOVK9endatWzNq1Cguv/xyoPD0nlu2bMFmszFz5kzat29PcHAwrVq14q+//mL58uW0bNmSihUr0r17d/bs2eN+nuOn9zzeu+++S8uWLQkNDSUuLo5rr73WPSMTwKJFi7DZbMyfP58WLVoQGBjIjz/+eNY/d2kxrei3cuVK2rdvD8Ds2bOJjY3l33//5Z133uH5558362nFZIGBMHFiwfH994OzuP99X3VVwWi/n38+i8qhiIiIb1qzZg1XXHHFCedjYmLYu3evBRGJJ2ldPxEREZGS2b9/P1999RXDhg2jQoUKJ1w/dsTG+PHjufrqq/njjz+49NJLGTBgAPv3G0Ufp9NJfHw8s2bNYt26dYwZM4YHH3yQmTNnFurv+++/Z+PGjXz//fe8/fbbTJ8+vdBUcAMHDmTHjh0sWrSIjz/+mFdffbXQB8IAV111Fbt372b+/PmsWLGC5s2bc+GFF7pjmTlzJuPGjeOxxx7jt99+o0qVKrz00kslzlUe3l30O53s7enk7jtCxdaJ7nOOioGEnBvH4TU7ADjyx04coYGENIhzt6nYOhHsNvd0ikG1ozm4LAWX08WhX/4lqFY08F+B5aomBMSFluJPJWWdMyePoxt2UbFNwevWZrcR2jqRI3/sJLhONEfX7yI3I5Mj63fhzMolICGCw6u3czR5N1H9m1oXvAlK8h5UsWJFKlasyNy5c8nKyiry48aOHcvo0aNZuXIlfn5+XHvttdx3331MmTKFJUuW8M8//zDm2AFHZ5CTk8OECRP4/fffmTt3Llu2bOGGG244od0DDzzA448/zvr162ncuHGR+7eKaUW/I0eOEBpqvLF+88039OnTB7vdznnnnce///5r1tNKKejfH5o3N/ZXrYIPPihmB3a71vYTEZFyKSIigp07d55wftWqVVSrVs2CiMSTVPQTERERKZl//vkHl8tFvXr1ztj2hhtu4JprrqFWrVo89thjHDp0iGXLlgHg7+/P+PHjadmyJUlJSQwYMIDBgwefUPSLjIzkxRdfpF69elx22WX06NHDvW7ghg0b+Pbbb3nttddo06YNzZs35/XXX+fo0aPux//4448sW7aMWbNm0bJlS2rXrs3TTz9NREQEs2fPBmDy5MkMGTKEIUOGULduXR599FEa5H8ZvgS8fU2/08nddwQAv8ohhc77VQpxX8vddwRHZOHrNj87jrAgcvcdBqDqiA5kbdnPhp5vkLU1jaojOnBo5TaO/rWHyB4N+PeBeWzo9QbbHvsWZ453T4cq3i8v7SjkufCrdOLrNmffEULb1iCie33+GTiDbeO+JmFcN+zB/myftJBqoy5k3+w/2NBnOv/c+CGZG33/S78lmWLYz8+P6dOn8/bbbxMREUG7du148MEH+eOPP077uHvuuYdu3bpRv3597rzzTlasWMHDDz9Mu3btaNasGUOGDOH7778vchw33ngj3bt3p2bNmpx33nk8//zzzJ8/370US75HHnmEiy66iHPOOYdKlSqd1c9cmkwr+tWqVYu5c+eydetWvv76ay6++GIAdu/eTVhY2RnGWh7Z7fDEEwXHo0dDMQryhr59oWFDY3/pUvjmG4/FJyIi4q369+/P/fffT2pqKjabDafTyU8//cQ999zDwIEDrQ5PSkhFPxEREZGSKc66yMeOtqhQoQJhYWGFRuFNnTqVFi1aEB0dTcWKFXn11VdJSUkp1EfDhg1xOBzu4ypVqrj7SE5Oxs/Pj+b533zH+LwzMjLSffz7779z6NAhKleu7B65UrFiRTZv3szGjcY0levXr6dNmzaFnrdt27ZF/jlPxZeLfp7iH1ORpMm9qf/FTSRN7o0jIojtj39HtVEXsvuNX7GHBFD34xvI3prG/o9PX0wQ8YS4W9tSb+6N1PloIOGda7HnrWVUbJ2Izc/O7jd/pdYbV1OpdyO2jv3a6lBLzBNr+u3YsYPPPvuMSy65hEWLFtG8efNCo62Pd+z7fmxsLACNGjUqdO740dins2LFCnr27EliYiKhoaF07NgR4IS/FS1btixyn97AtKLfmDFjuOeee6hRowatW7d2/zH75ptvaNasmVlPK6Wka1f4r47Lli1Q7FkJNNpPRETKoccee4x69eqRkJDAoUOHaNCgAR06dOD8889n9OjRVocnJaSin4iIiEjJ1K5dG5vNxoYNG87Y1t/fv9Bx/pfqAD788EPuuecehgwZwjfffMPq1asZPHgw2dnZRe6jKA4dOkSVKlVYvXp1oS05OZl77723yP2cDbvNZmr/Zsof4Zc/qi9f7v4j7mt+lUPIO1D4uivXSV5GJn6VT5z6FWD3W8sIPa86IfVjObRiG+FdamPzcxDWuRaHVmwz4SeR8sQREQwOG7n7T3zd+h83ahUgc8t+DszfQOzQ8zm8YhsVmlXDLzKEiIvqcHTDbvIOZ5/wGF9ip+TvQUFBQVx00UU8/PDD/Pzzz9xwww2MPbZmcJxj37Nt/70HHn+uqO/hhw8fplu3boSFhfH++++zfPlyPvnkE4AT/lacbLppb2Za0a9v376kpKTw22+/8fXXBZXrCy+8kOeee86sp5VS9MQTkH9/8eijkJZWzA6uvBLOPdfY/+UXjfYTEZEyLyAggNdee42NGzcyb9483nvvPTZs2MC7775b6BvG4puSIpLc+yr6iYiIiBRfpUqV6NatG1OnTuXw4cMnXE8r4odPP/30E+effz633347zZo1o1atWu6Rd0VVt25dcnNzWbVqlfvcP//8w4EDB9zHzZs3JzU1FT8/P2rVqlVoi4qKAqB+/fr8+uuvhfr+5ZdfihXLyTjM+1jXdAHVwvGrHMKh5Vvd5/IOZXFkbSoVGlUFIKRxFfIOZnFk/S53m0O/bQWni5Bz407oM3PzPtK+SiZu6PnGCacTV67x4b8r1wlODTaQkrH7OwiuF8uhZQWvW5fTxaHlWwlpXKVQW5fLxfbHvqXqXR1whATgynMd83r8b1rMYnzBwBs5bJ5/D2rQoMFJ3/vNsGHDBvbt28fjjz9O+/btqVevXrFGCXozU/86xMXF0axZM3bs2MG2bca3KVq3bl2kebnF+zVtCgMGGPv79xee8rNIjh/tN3asRvuJiEi5kJiYyKWXXsrVV19N7dq1rQ5HPCQ0MJTokGhART8RERGRszV16lTy8vJo3bo1H3/8MX///Tfr16/n+eefL/K0mLVr13YPRPjrr794+OGHWb58ebHiqFevHl27duWWW25h2bJlrFq1iltuuYXg4GD3CJOuXbvStm1bevfuzTfffMOWLVv4+eefeeihh/jtt98AuPPOO3nzzTd56623+Ouvvxg7dix//vln8ZJyEg6bd39pMO9INkeTd3M02fgQPXt7BkeTd5OdmoHNZiPqmubsfuNX0hdv5Og/e9k69mv8oysQ1ukcAIKSKhN6fg22PfotR9amcnj1drY/+R0RF9fFP7pioedyuVxsm/gtVUd2xB5sjPqp0KQq++euIXPzPg58sZ6QJlVLNwFSJkUPaM7+uWvYP+9PMjfvY/ukhTiP5hDZs2GhdvvnrsUvIpiwDsbruUKTqhxavpXDa3ayZ8ZKAmtWwhEaZMWP4DF+JXgP2rdvH126dOG9997jjz/+YPPmzcyaNYsnn3ySXr16eTDKU0tMTCQgIIAXXniBTZs28dlnnzFhwoRSeW6z+ZnVsdPp5NFHH+WZZ55xL3wYGhrK3XffzUMPPYTd7rvfRpECEybAzJmQnQ2TJ8OwYRAfX4wO+vSBRo1gzRr49Vf4+mu45BKzwhURESl1I0eOLHLbZ5991sRIpDTUjKzJniN72H5wO5m5mQT5+fb/yImIiIiUtpo1a7Jy5UomTpzI3Xffzc6dO4mOjqZFixa8/PLLRerj1ltvZdWqVfTr1w+bzcY111zD7bffzvz584sVyzvvvMOQIUPo0KEDcXFxTJo0iT///JOgIOMez2az8eWXX/LQQw8xePBg9uzZQ1xcHB06dHCvN9WvXz82btzIfffdR2ZmJldeeSVDhw4tNDPa2fAzYZSNJx1dt4tNt812H+98bjEAkZc1IGFcN6IHtcSZmcP2x74l72AWFZpWJen5PtgDCz6uTpjQnR1Pfsem22eDzUZ4l9pUvbfTCc+1f84a/CuFENa+YLr92FvakjL6S/654UNC21Yn6uom5v2wUm5EXFyX3ANH2TVtKbn7jhBUJ5qkF67A/5gpZ3P2HWb3m8uo9WY/97mQc+OIvq4FW0bMxS8yhIRx3awI36NK8h5UsWJF2rRpw3PPPcfGjRvJyckhISGBm2++mQcffNCDUZ5adHQ006dP58EHH+T555+nefPmPP3001x++eWl8vxmsrmKs0JuMYwaNYo33niD8ePH065dOwB+/PFHxo0bx80338zEiRPNeNqzkpGRQXh4OOnp6YSFhXmkT6fTye7du4mJiSnzBc6774b8zyhvvBHeeKOYHXz8MfTta+y3bm1M9XmaecnLU25Lk/JqHuXWPMqtOczKqxl/b31B586di9TOZrPx3XffmRyNb/Kle7VrP76WD9Z+AMD6YeupF1X+ZrjQe7N5lFtzKK/mUW7NoXs1Eets27aNhIQEvv32Wy688EJLY1makcxtf79iaQwiUn791PQxKjr0JVdvZNpIv7fffpvXX3+9UGW0cePGVKtWjdtvv92rin5SMg89BG++aazpN306jBwJDRue6VHHuOIKaNwY/vgDli2Dr76C7t1NilZERKR0ff/991aHIKWoZmTBt4s3HdhULot+IiIiImXFd999x6FDh2jUqBE7d+7kvvvuo0aNGnTo0MHq0ExZT0tEpKj0HuS9TPvN7N+//6Rr99WrV4/9+/eb9bRigUqVYNQoY9/phAceKGYHWttPRETKoa1bt7J169YzNxSfcnzRT0RERER8V05ODg8++CANGzbkiiuuIDo6mkWLFuHv7291aPjbTBvLISJyRiVZ00/MZVrRr0mTJrz44osnnH/xxRdp3LixWU8rFrnjjoK1/ObNg8WLi9lB797GaD+A5cuhmHOsi4iI+ILc3FwefvhhwsPDqVGjBjVq1CA8PJzRo0eTk5NjdXjiASr6iYiIiJQd3bp1Y+3atRw5coRdu3bxySefUL16davDAiDCL8TqEESknKpoD8JfRT+vZdpXQp588kl69OjBt99+S9u2bQFYunQpW7du5csvvzTracUiwcEwYQIMHmwc33ffGZfmK8xuh3HjoE8f43jsWGOKzyJ3ICIi4v3uuOMO5syZw5NPPlno/mjcuHHs27ePl19+2eIIpaRU9BMRERGR0hDlr7U3RcQaev/xbqaN9OvYsSN//fUXV1xxBWlpaaSlpdGnTx/+/PNP3n33XbOeVix0/fXQqJGxv2wZzJ5dzA569YImTYz9334DFYdFRKSMmTFjBtOnT+fWW2+lcePGNG7cmFtvvZU33niDGTNmWB2eeEC10Gr4243pnlT0ExERERGzhDqCCbIHWB2GiJRD0QEq+nkzU1dbrFq1KhMnTuTjjz/m448/5tFHH+XAgQO88cYbZj6tWMThgMcfLzh+8EEo1kxl+aP98o0bp7X9RESkTAkMDKRGjRonnE9KSiIgQP/DXhY47A5qRNQAjKKfS/cyIiIiImKSaP9Qq0MQkXIoWiP9vJqpRT8pf7p3h06djP1//oFXXy1mB716QdOmxv5vv8EXX3gwOhEREWsNHz6cCRMmkJWV5T6XlZXFxIkTGT58uIWRiSflT/F5OOcwe47ssTgaERERESmrNMWeiFhB7z3eTUU/8SibDZ58suB4/Hg4eLCYHWi0n4iIlFGrVq1i3rx5xMfH07VrV7p27Up8fDyff/45v//+O3369HFv4ruOXddv84HNFkYiIiIiImVZtH+41SGISDmkop9387M6ACl7WrWCq6+GmTNhzx54+mmj+Fdkl19ujPZbvRpWrIB586BnT5OiFRERKT0RERFceeWVhc4lJCRYFI2Y5dii36YDm2gT38bCaERERESkrNL0niJiBb33eDePF/3O9M30tLQ0Tz8l27dv5/7772f+/PkcOXKEWrVq8dZbb9GyZUuPP5cUzcSJMGcO5ObCM8/A0KEQF1fEB+eP9uvd2zgeNw4uu8w4LyIi4qNcLhfjx48nOjqa4OBgq8MREx1f9BMRERERMYNG24iIFTTK2Lt5fHrP8PDw027Vq1dn4MCBHnu+AwcO0K5dO/z9/Zk/fz7r1q3jmWeeITIy0mPPIcVXqxbcdpuxf/hwMUf6gTHar1kzY3/lSvj8c4/GJyIiUtpcLhe1atVi27ZtVociJlPRT0RERERKQ7SKfiJiAb33eDePj/R76623PN3laT3xxBMkJCQUet6kpKRSjUFO7uGHYfp0OHQIXnsNRoyAunWL+OD80X69ehnH48YZU3xqtJ+IiPgou91O7dq12bdvH7Vr17Y6HDFRUkTBveimNBX9RERERMQcGm0jIlbQKGPv5vGRfqXts88+o2XLllx11VXExMTQrFkzXnvtNavDEiAmBu67z9jPy4MHHyxmBz17QvPmxv6qVfDZZx6NT0REpLQ9/vjj3Hvvvaxdu9bqUMRE4UHhVAquBGikn4iIiIiYRx+8i0hpC7IHUNERZHUYchoeH+lX2jZt2sTLL7/MyJEjefDBB1m+fDn/+9//CAgIYNCgQSd9TFZWFllZWe7jjIwMAJxOJ06n0yNxOZ1OXC6Xx/rzVSNGwEsv2UhNtTFnDvz0k5O2bYvRwZgx2P9b2881bhyuyy7D6XIptybQa9Y8yq15lFtzmJVX/Z5g4MCBHDlyhCZNmhAQEHDC2n779++3KDLxtJqRNdl/dD9b07eSnZdNgCPA6pBEREREpIyJ9g+1OgQRKWf0vuP9fL7o53Q6admyJY899hgAzZo1Y+3atUybNu2URb9JkyYx/iSLzO3Zs4fMzEyPxZWeno7L5cJu9/kBlSVy113B3H+/Md3AyJG5zJ27v+izdLZuTeXGjfH/4w9sq1eT9s47HO3WTbk1gV6z5lFuzaPcmsOsvB48eNBjffmqyZMnWx2ClJKakTX5bcdvuHDxb9q/1K6sKV1FRERExLPC/SoQYPMj25VrdSgiUk5oWmHv5/NFvypVqtCgQYNC5+rXr8/HH398yseMGjWKkSNHuo8zMjJISEggOjqasDDPDIt3Op3YbDaio6PL/QfRI0bAm2+6SE62sWxZAL/+GsPllxejgwkT3Gv7RTz/PKHXXafcmkCvWfMot+ZRbs1hVl6DgjT9w6m+kCRlT82Imu79TQc2qegnIiIiIqaI9g9je7ZmDBGR0qFphb2fzxf92rVrR3JycqFzf/31F9WrVz/lYwIDAwkMDDzhvN1u9+iHmzabzeN9+qKAAJg0Cfr0MY4ffNDOZZeBX1FffT17QqNGsGYNttWrsU+ejH+TJtgrVSrIbVQUJCaaEn95oteseZRb8yi35jAjr/odGTZu3Mhbb73Fxo0bmTJlCjExMcyfP5/ExEQaNmxodXjiITUjCxf9RERERETMUDM4TkU/ESk15wTFWh2CnIHPf/p211138csvv/DYY4/xzz//MGPGDF599VWGDRtmdWhyjN694fzzjf3162H69GI8eOtW2LDBfWi/7z6iunXD3qoVtGhhbHXrQkqKJ0MWERHxuMWLF9OoUSN+/fVX5syZw6FDhwD4/fffGTt2rMXRiSep6CciIiIipaF+SDWrQxCRcqR+SLzVIcgZ+HzRr1WrVnzyySd88MEHnHvuuUyYMIHJkyczYMAAq0OTY9hs8OSTBcdjx8KRI0V88N69kJNz+jaZmUY7ERERL/bAAw/w6KOPsmDBAgICAtznu3Tpwi+//GJhZOJphYp+aSr6iYiIiIg5GoQkWB2CiJQjDSroPcfb+XzRD+Cyyy5jzZo1ZGZmsn79em6++WarQ5KTaNfOvTQfO3bA5MmWhiMiIlLq1qxZwxVXXHHC+ZiYGPbqyytlSkJ4Ag6bA9BIPxERERExTwONuhGRUhLtH0a01vTzemWi6Ce+Y9IkyF/S6YknNDhPRETKl4iICHbu3HnC+VWrVlGtmqblKUv87H5UjzDWmN50YBMul8viiERERESkLIoNiKCSX0WrwxCRckBTe/oGFf2kVNWvD0OGGPsZGfDoo9bGIyIiUpr69+/P/fffT2pqKjabDafTyU8//cQ999zDwIEDrQ5PPCx/is+MrAz2H91vcTQiIiIiUlZptJ+IlAa91/gGFf2k1I0bB8HBxv5LL8EmzXglIiLlxGOPPUb9+vVJTEzk0KFDNGjQgA4dOnD++eczevRoq8MTD6sZccy6fpriU0RERERMUl/r+olIKdB7jW9Q0U9KXdWqMHKksZ+TA/qMU0REyjqn08kTTzxB586dWbVqFddffz3z5s3jvffeY8OGDbz77rs4HA6rwxQPyx/pByr6iYiIiIh5GlTQ6BsRMZ9G+vkGFf3EEvfdB1FRxv4HH8Bvv3mg04MHPdCJiIiI502cOJEHH3yQihUrUq1aNWbMmMHs2bO5+uqrqV27ttXhiUlU9BMRERGR0qAP4kXEbFF+ocQEhFsdhhSBin5iibAwGDOm4Pj++8HlOkXjqCgICjpzpw8/DLm5HolPRETEk9555x1eeuklvv76a+bOncvnn3/O+++/j9PptDo0MZGKfiIiIiJSGuICIon0q2h1GCJShtWvoKk9fYWKfmKZW2+Fc84x9r/7Dr7++hQNExMhORlWrIAVK3AuX87er7/GuXw5zJ0L4f99w2DJEhgxohQiFxERKZ6UlBQuvfRS93HXrl2x2Wzs2LHDwqjEbIWKfmkq+omIiIiIeTTaT0TMpPcY36Gin1gmIAAmTiw4vv9+yMs7RePERGje3L3lNm5s7PfqBZ99Bv7+RrupU+HFF02PXUREpDhyc3MJOm7Uur+/Pzk5ORZFJKUhMjiSiKAIQCP9RERERMRc+kBeRMyk9xjf4Wd1AFK+XXUVPP20sabfH3/A++/DwIHF7KRDB3jtNbjhBuP4zjuNIYTdu3s6XBERkbPicrm44YYbCAwMdJ/LzMzktttuo0KFCu5zc+bMsSI8MVHNyJqs3LmSlPQUcvJy8Hf4Wx2SiIiIiJRB9fSBvIiYqL7eY3yGRvqJpex2ePLJguOHH4bMzLPoaNAgGDXK2Hc6oV8/WLvWIzGKiIiU1KBBg4iJiSE8PNy9XXfddVStWrXQuaKaNGkSrVq1IjQ0lJiYGHr37k1ycnKhNpmZmQwbNozKlStTsWJFrrzySnbt2lWoTUpKCj169CAkJISYmBjuvfdeco9bH3fRokU0b96cwMBAatWqxfTp00+IZ+rUqdSoUYOgoCDatGnDsmXLih1LWZU/xafT5SQlPcXiaERERESkrDpX622JiEkq+4USGxBhdRhSRBrpJ5br3NkYlDd/PqSkGLNz3nPPWXT06KPG2n9z5sDBg3DZZbBsGcTEeDxmERGR4njrrbc82t/ixYsZNmwYrVq1Ijc3lwcffJCLL76YdevWuUcO3nXXXXzxxRfMmjWL8PBwhg8fTp8+ffjpp58AyMvLo0ePHsTFxfHzzz+zc+dOBg4ciL+/P4899hgAmzdvpkePHtx22228//77LFy4kJtuuokqVarQrVs3AD766CNGjhzJtGnTaNOmDZMnT6Zbt24kJycT89/f4DPFUpbVjDhmXb8Dmzin0jkWRiMiIiIiZVVcQCQ1g2LZlFk+vlwnIqWnXXg9q0OQYtBIP/EKjz8ONpux/9hjcODAWXRit8O770KLFsbxv/9C795nOXRQRETEe3311VfccMMNNGzYkCZNmjB9+nRSUlJYsWIFAOnp6bzxxhs8++yzdOnShRYtWvDWW2/x888/88svvwDwzTffsG7dOt577z2aNm1K9+7dmTBhAlOnTiU7OxuAadOmkZSUxDPPPEP9+vUZPnw4ffv25bnnnnPH8uyzz3LzzTczePBgGjRowLRp0wgJCeHNN98scixlWf5IP4DNaZstjEREREREyroO4Q2sDkFEyqCOem/xKRrpJ16hcWNjLb+33zYKfpMmFZ72s8hCQuCzz6B1a9i+HZYuhRtvNBYLzK8qioiIlDHp6ekAVKpUCYAVK1aQk5ND165d3W3q1atHYmIiS5cu5bzzzmPp0qU0atSI2NhYd5tu3boxdOhQ/vzzT5o1a8bSpUsL9ZHfZsSIEQBkZ2ezYsUKRuVPsQ3Y7Xa6du3K0qVLixzL8bKyssjKynIfZ2RkAOB0OnE6nWeVo+M5nU5cLpfH+juVGhE13Psb9280/fm8QWnltjxSbs2hvJpHuTWHWXnV70nE93WKOJfpu763OgwRKUMCbH6cH6aRfr5ERT/xGo88Ah9+CFlZ8PzzMHw4JCaeRUdVq8Lnn8MFF8CRI/DBB1CvHowZ4/GYRURErOZ0OhkxYgTt2rXj3HPPBSA1NZWAgAAiIiIKtY2NjSU1NdXd5tiCX/71/Guna5ORkcHRo0c5cOAAeXl5J22zYcOGIsdyvEmTJjF+/PgTzu/Zs4dMD43gdzqdpKen43K5sNvNm/wiPK9grcb1qevZvXu3ac/lLUort+WRcmsO5dU8yq05zMrrwYMHPdaXiFijSYXqRPpV5EDuIatDEZEyolVoLUIcgVaHIcWgop94jcRE+N//4KmnjMLfmDEwffpZdtasGcyYAVdcAS4XjB0LdepA//6eDFlERMRyw4YNY+3atfz4449Wh+Ixo0aNYuTIke7jjIwMEhISiI6OJiwszCPP4XQ6sdlsREdHm/pBdGTlSOw2O06Xkx1Hd7jXOSzLSiu35ZFyaw7l1TzKrTnMymtQUJDH+hIRa9htdtqH1+ezfcutDkVEyoiO4Q2tDkGKSUU/8SqjRsHrrxtTfL7zDowcaUz9eVZ69TLmCL33XuP4hhugRg04yTRiIiIivmj48OHMmzePH374gfj4ePf5uLg4srOzSUtLKzTCbteuXcTFxbnbLFu2rFB/u3btcl/L/zf/3LFtwsLCCA4OxuFw4HA4Ttrm2D7OFMvxAgMDCQw88ZuEdrvdox9u2mw2j/d5vEB7IAlhCfyb/i+bDmwqNx96l0Zuyyvl1hzKq3mUW3OYkVf9jkTKho7hDVX0ExGP6RShop+v0R2deJXISHjwQWPf5YIHHihhh3ffDUOGGPtZWUYh8N9/S9ipiIiItVwuF8OHD+eTTz7hu+++IykpqdD1Fi1a4O/vz8KFC93nkpOTSUlJoW3btgC0bduWNWvWFJpucsGCBYSFhdGgQQN3m2P7yG+T30dAQAAtWrQo1MbpdLJw4UJ3m6LEUtbVjKwJwIHMAxw4esDiaERERESkLDs/rC4BNo3zEJGSqxdcjdiACKvDkGJS0U+8zrFr+c2fD9+XZP1hmw1eegk6dzaOd++Gyy6DjIwSxykiImKVYcOG8d577zFjxgxCQ0NJTU0lNTWVo0ePAhAeHs6QIUMYOXIk33//PStWrGDw4MG0bduW8/4b8X7xxRfToEEDrr/+en7//Xe+/vprRo8ezbBhw9yj7G677TY2bdrEfffdx4YNG3jppZeYOXMmd911lzuWkSNH8tprr/H222+zfv16hg4dyuHDhxk8eHCRYynr8ot+AJvTNlsYiYiIiIiUdSGOQFqH1rI6DBEpAzTKzzep6CdeJygIJkwoOL7vPnA6S9BhQADMng21axvHa9fCNddAbm6J4hQREbHKyy+/THp6Op06daJKlSru7aOPPnK3ee6557jsssu48sor6dChA3FxccyZM8d93eFwMG/ePBwOB23btuW6665j4MCBPPLII+42SUlJfPHFFyxYsIAmTZrwzDPP8Prrr9OtWzd3m379+vH0008zZswYmjZtyurVq/nqq6+IjY0tcixl3bFFv00HNlkYiYiIiIiUBx31Qb2IeIDW8/NNGustXmnAAHjmGfjjD/jtN5g1C/r1K0GHlSrBvHnGen4HDsCXX8I998DkyZ4KWUREpNS4XK4ztgkKCmLq1KlMnTr1lG2qV6/Ol19+edp+OnXqxKpVq07bZvjw4QwfPrxEsZRlKvqJiIiISGnqGN6QiXxsdRgi4sNi/MNpUCHB6jDkLGikn3glhwOeeKLg+MEHITu7hJ3WqQMffwx+/9W6p0yBl18uYaciIiIip6ein4iIiIiUptiACOqHxFsdhoj4MI3y810q+onX6tYNunQx9jdtglde8UCnnTvDtGkFx3fcAd9844GORURERE5ORT8RERERKW0dwxtYHYKI+LCOEXoP8VUq+onXstngyScLjh95BDIyPNDxkCFw773Gfl4eXHUVrFvngY5FRERETlQ5uDKhAaGAin4iIiIiUjo6RZxrdQgi4qOC7QG0Ca1jdRhyllT0E6/WogX072/s790LTz3loY4nTYJevYz9jAy47DLYs8dDnYuIiIgUsNls7tF+/6b/S64z1+KIRERERKSsqx8ST0JgZavDEBEf1DG8IQF2P6vDkLOkop94vYkTwd/f2H/mGWNZvk8+CWLRImOg3llxOOC996BpU+N482bo0weysjwQsYiIiEhh+UW/XGcu2zK2WRyNiIiIiJQHV1RuY3UIIuKD+kTpvcOXqegnXq9mTRg61Ng/ehSuvtrO7bdHcOGFdmrUgDlzzrLjihXh88+hShXj+Mcf4eabweXyRNgiIiIiblrXT0RERERKW6+o1vjp418RKYaEwMq0Dq1tdRhSAnrXF5+QPyDveNu3Q9++JSj8xcfDZ59BcLBx/O67xtSfIiIiIh6kop+IiIiIlLYo/zDaRzSwOgwR8SFXRJ2HzWazOgwpARX9xOvl5cGYMSe/lj8ob8SIEkz12bKlMdVnvoceglmzzrIzERERkROp6CciIiIiVrgy6jyrQxARH+GHnV6VW1kdhpSQin7i9ZYsgW2nWfrG5YKtW412Z61Pn8Ij/AYOhOXLS9ChiIiISAEV/URERETECu3C6hHnH2F1GCLiAzpENCTKP8zqMKSEVPQTr7dzp2fbndL998OgQcZ+ZiZcfrlRTRQREREpoerh1bFhTJGiop+IiIiIlBa7zU7vqNZWhyEiPkAjg8sGFf3E61Wp4tl2p2SzwSuvQPv2xnFqKvTsCYcOlbBjERERKe8C/QKJD4sHVPQTERERkdLVN/p8/GwOq8MQES+WGBhFu7B6VochHqCin3i99u0hPt6oyZ1KWBhccIEHniwwEObMgXPOMY5//x2uvbYECwaKiIiIGPKn+Nx3dB/pmekWRyMiIiIi5UW0fxgXRzaxOgwR8WL9oy/AdroP4MVnqOgnXs/hgClTjP1Tve9kZMADDxjr+5VYVBTMmwfh4cbx558bU3+KiIiIlMCx6/ptTttsYSQiIiIiUt5cG9Pe6hBExEtVsAfSS9MAlxkq+olP6NMHZs+GatUKn4+MLNh/5hn43//A6fTAE9arZzyhw1HQ+WuveaBjERERKa+OLfppik8RERERKU2NKlSncYXqVochIl6oV1RrKjqCrA5DPERFP/EZffrAli2wcKGTl15KY+FCJ3v2wOuvF4wAfPFFuO02DxX+unaFl14qOL79dli40AMdi4iISHmkop+IiIiIWOnamA5WhyAiXsaOjWuiPbFulngLFf3Epzgc0KkTXHFFJp06GcdDhsDbb4P9v1fza6/BjTd6aBm+W26Bu+4y9nNzoW9fSE72QMciIiJS3hSa3vOApvcUERERkdJ1UWRjYvzDrQ5DRLzIBeH1SQyKtjoM8SAV/aRMuP56mDGjYDbOt982zuXmeqDzp56Cyy4z9tPSjP19+zzQsYiIiJQnhUb6pWmkn4iIiIiULj+bg+s02k9EjjEotpPVIYiHqegnZUa/fjBrFvj7G8cffAD9+0N2dgk7djiMimLjxsbxP//AlVd6oGMREREpT6JDoqngXwHQ9J4iIiIiYo1+Me002k9EAGgXVo+WobWsDkM8TEU/KVOuuALmzIGAAOP444+NGTmzskrYcWgofP45xMYax4sXG4sHulwl7FhERETKC5vNRlJkEgBb0raQ5/TEXOQiIiIiIkUXZA9gaNVuVochIhazYeN/1XpYHYaYQEU/KXMuu8yozwUFGceffw69esHRoyXsODERPvusoOO33jKm/hQREREpovwpPrPzstlxcIfF0YiIiIhIedSrcmuSgmKsDkNELNS9UjPqhVSzOgwxgYp+UiZdfDF8+SWEhBjHX39tFAMPHy5hx61bGwsG5nvgAfjkkxJ2KiIiIuVFzYhj1vXTFJ8iIiIiYgGHzc7wqpdaHYaIWMTP5mBY1e5WhyEmKXNFv8cffxybzcaIESOsDkUs1rmzUewLDTWOv/sOuneHgwdL2PHVV8OECca+ywUDBsCKFSXsVERERMqD/JF+oKKfiIiIiFina2Rjzg1JtDoMEbHAlVHnER9Y2eowxCRlqui3fPlyXnnlFRo3bmx1KOIlLrgAFiyA8P/WJ16yxBgFmJZWwo4fegiuu87YP3oULr8ctm8vYaciIiJS1qnoJyIiIiLe4s54reclUt4E2wO4tcrFVochJiozRb9Dhw4xYMAAXnvtNSIjI60OR7xImzbGKL9KlYzjX36Brl1h//4SdGqzweuvQ7t2xvGOHdCzpwfmDxUREZGyrFDRL01FPxERERGxTuvQ2pwfVtfqMESkFF0f25HK/qFWhyEm8rM6AE8ZNmwYPXr0oGvXrjz66KOnbZuVlUVWVpb7OCMjAwCn04nT6fRIPE6nE5fL5bH+pMDZ5LZpU1i4EC6+2MaePTZWrIDOnV18842L6OizDMTfHz7+GFvbttg2b4ZVq3ANGIBr9myw+149Xa9Z8yi35lFuzWFWXvV7EoEaETXc+xrpJyIiIiJWu7PaZSzN+AsXLqtDERGTRfpV4IbYzlaHISYrE0W/Dz/8kJUrV7J8+fIitZ80aRLjx48/4fyePXvIzMz0SExOp5P09HRcLhd2HywAebOzzW1cHMya5eDqqyuxe7eDP/6w0aFDLrNmHSAm5uw/iHa89RaVe/bEfvAgtk8/5fBdd3HooYfOuj+r6DVrHuXWPMqtOczK68ESL6oq4vuC/YOpGlqVHQd3qOgnIiIiIparF1KNSyKbMv/AKqtDERGT3RTXlQqOIKvDEJP5fNFv69at3HnnnSxYsICgoKK9YEeNGsXIkSPdxxkZGSQkJBAdHU1YWJhH4nI6ndhsNqKjo/VBtIeVJLcxMbB4MXTt6mL7dht//eXPVVdF8+23LqpVO8uAYmLgo49w9eyJLS+Pii++SEjTpjB48Fl2aA29Zs2j3JpHuTWHWXkt6t9pkbKuZmRNdhzcwe7DuzmUfYiKARWtDklEREREyrFh1bqzIO0Pcl15VociIiapGhDJ1dHtrA5DSoHPF/1WrFjB7t27ad68uftcXl4eP/zwAy+++CJZWVk4HI5CjwkMDCQwMPCEvux2u0c/3LTZbB7vUwwlyW29evDDD9ClC/z7L/z1l41OnWx89x1Ur36WAXXvDs8/D8OGAWC/7TZwOqFFixPbRkVBYuJZPpG59Jo1j3JrHuXWHGbkVb8jEUPNyJr8mPIjAJsPbKZRbCOLIxIRERGR8iwhMIoro87joz0/WR2KiJjk9qqXEGD3+XKQFIHP/5YvvPBC1qxZU+jc4MGDqVevHvfff/8JBT8RgJo1Cwp/GzfCpk3QoQN89x2cc85Zdnr77bB8OUyfDrm5cMstJ28XFATJyV5b+BMRERFz1Yyo6d7fdGCTin4iIiIiYrlbq1zM5/t+44gzy+pQRMTDagdXoUelkwxOkTLJ579yHxoayrnnnltoq1ChApUrV+bcc8+1OjzxYomJxlSfdesaxykp0LGjUY87a0OHnrlNZibs3VuCJxERERFfVjOycNFPRERERMRqlf1DubNaD6vDEBEPs2NjTOJV2G0+XwqSItJvWsq1atVg0SJo2NA43r7dKPz9+edZdujn84NnRURExGQq+omIiIiIN+oX3Y5WobWsDkNEPGhgbCcaV6xhdRhSispk0W/RokVMnjzZ6jDER8TFwfffQ5MmxvGuXdCpE/z+u6VhiYiISBlVqOiXpqKfiIiIiHgHm83GuOr9CLYHWB2KiHhAUlAMt1e9xOowpJSVyaKfSHFFRxvr+bVsaRzv3QudO8OKFSY94TffQHa2SZ2LiIiIN4urGEeQXxCgkX4iIiIi4l3iAytzV7WeVochIiXkwM4jNa4h0O5vdShSylT0E/lPpUrw7bfQtq1xfOAAXHgh/PKLCU82apSxqODo0cZigiIiIlJu2Gw292i/zQc243Q5LY5IRERERKTA1dHna5pPER93fWxHGleobnUYYgEV/USOER4OX38NHToYx+npcNFFsGSJCU+2axdMnAhJSdC7tzH6z6kP/URERMqD/KJfVl4WOw/utDgaEREREZECNpuN8dX7E2IPtDoUETkLNYNiGVa1u9VhiEVU9BM5TmgofPmlMcoP4NAhuOQSY/pPj+naFfz8jH2nEz79FLp1g7p14dlnYf9+Dz6ZiIiIeJuaEces66cpPkVERETEy1QLrMRd8ZdZHYaIFJMxrWd/Aux+VociFlHRT+QkKlSAzz83in0AR45Ajx7GKMDTioqCoKDTtwkKgjfegH//hfHjoWrVgmv//AN33w3VqsGNN8Jvv5Xo5xARERHvlD/SD1T0ExERERHvdFXU+bQOrW11GCJSDANjO9FI03qWayr6iZxCcDDMnQuXX24cZ2Ya+59/fpoHJSZCcjKsWHHqLTnZaFe1KowZA1u2wMcfFwwtzH+yt96CVq2gdWtj/+hRE39aERERKU0q+omIiIiIt7PZbIyr3k/TfIr4iJpBsdxe9RKrwxCLqegnchqBgTBrFvTtaxxnZ0OfPkaN7pQSE6F581NviYmF2/v7G51++y1s2AAjRhiLC+ZbvtwY9VetmjEK8O+/Pf1jioiISCkrVPRLU9FPRERERLyTMc1nT6vDEJEzcGBnQo1rNK2nqOgnciYBAfDBB3DttcZxbi7062ec87i6deG552D7dnj9dWjWrODagQPGen916hjr/336qRGMiIiI+JykyCT3/uYDmy2MRERERETk9K6KaksbTfMp4tUGxXXi3AqJZ24oZZ6KfiJF4OcH77wDN9xgHOflwXXXwdtvm/SEFSrAkCHGdKC//AIDBxrDDvN98w307g01a8LEibBrl0mBiIiIiBlC/EOIqxgHaHpPEREREfFu+dN8hjqCrQ5FRE6iTnAVhlbRtJ5iUNFPpIgcDnjjDbjlFuPY6YTBg+G110x8UpsN2rQxqovbtsGTTxqFvnxbt8Lo0ZCQANdcA0uWgMtlYkAiIiLiKUkRxmi/nYd2ciTniMXRiIiIiIicWtXASjxZcyAOfZws4lUiHBV47pwbNa2nuOldWqQY7HaYNg3uuMM4drmMIuDUqaXw5FFRcO+9xpp+8+dDz55GURAgJwc+/BA6dIDGjeGllyAjoxSCEhERkbN17Lp+W9K2WBeIiIiIiEgRnB9WV+v7iXgRP5uDp88ZRHxgZatDES+iop9IMdlsMGUK3HNPwbnhw43l9kqF3Q6XXAKffQabNsGoURAdXXB97VoYNgyqVYPbb4c1a0opMBERESmOY4t+muJTRERERHzB9bEd6VW5tdVhiAhwf0JvWoXWsjoM8TIq+omcBZvNmGlz9OiCc3ffDY89VsqB1KhhPOnWrfD++9CuXcG1Q4fg5ZeNkX8dOhgjAbOzSzlAERERORUV/URERETEFz2c2JemFWpYHYZIuXZV1PlcHd3uzA2l3FHRT+Qs2WwwYQI88kjBuYcegnHjLFhWLzAQrr0WfvwRVq+GW2+FChUKri9ZYqz5l5BgVCpTUozzKSmwcqV78/vjj0LH7nYiIiLicSr6iYiIiIgv8rf78ew5g4nzj7A6FJFyqWXFc7g/8QqrwxAvpaKfSAk9/DA88UTB8fjx8OCDkJsLixbBBx8Y/+bllVJATZoYCw9u3w4vvAANGhRc270bJk6EpCS4+GKoVQtatIAWLbC3akVUt27YW7Vyn6NuXRX+RERETKKin4iIiIj4qsr+oUyudSNBNn+rQxEpV6oFVOLpcwbhb3NYHYp4KRX9RDzgvvtg8uSC48cfh8hI6NzZGIDXubMxE+ecOaUYVHi4sdjg2rVG1fHqq8HPz7jmdMKCBZCTc/o+MjNh717TQxURESmPqoZWJcARAKjoJyIiIiK+p35IPONr9Lc6DJFyI8QeyJRaNxLpV9HqUMSLqegn4iF33mksoZfv0KHC17dvh759S7nwB8Y8pB07wkcfGaP2HnkEqlUr5SBERETkeHabnaSIJMAo+rlKfX5wEREREZGSuaRSM26K62p1GCJlng0bE5OupXZwVatDES+nop+IB918szHC72TyP8cbMaIUp/o8XpUqxnykW7bAU09ZFISIiIjky5/i82juUXYd3mVxNCIiIiIixTe8anc6hTe0OgyRMm1o1W50iWhkdRjiA1T0E/GgJUvgwIFTX3e5YOtWo52l/PygSxeLgxARERGt6yciIiIivs5ms/FY0gBqBcVZHYpImXRxZFNurXKx1WGIj1DRT8SDdu70bDuvcNddxshAERHxGj/88AM9e/akatWq2Gw25s6dW+i6y+VizJgxVKlSheDgYLp27crff/9dqM3+/fsZMGAAYWFhREREMGTIEA4dNzf1H3/8Qfv27QkKCiIhIYEnn3zyhFhmzZpFvXr1CAoKolGjRnz55ZfFjqU8U9FPRERERMqCCo4gptQaQmW/UKtDESlTGoYk8IjWzpRiUNFPxIOqVPFsO6/www/QoAFMmgTZ2VZHIyIiwOHDh2nSpAlTp0496fUnn3yS559/nmnTpvHrr79SoUIFunXrRmZmprvNgAED+PPPP1mwYAHz5s3jhx9+4JZbbnFfz8jI4OKLL6Z69eqsWLGCp556inHjxvHqq6+62/z8889cc801DBkyhFWrVtG7d2969+7N2rVrixVLeaain4iIiIiUFfGBlXmlzm1EOCpYHYpImVA3uCrTat9KsD3A6lDEh6joJ+JB7dtDfDzYbKdv99574FOfdR49Cg8+CE2awHffWR2NiEi51717dx599FGuuOKKE665XC4mT57M6NGj6dWrF40bN+add95hx44d7hGB69ev56uvvuL111+nTZs2XHDBBbzwwgt8+OGH7NixA4D333+f7Oxs3nzzTRo2bEj//v353//+x7PPPut+rilTpnDJJZdw7733Ur9+fSZMmEDz5s158cUXixxLeaein4iIiIiUJbWDqzCtzq2EOoKtDkXEp9UMimVa7dsI8wuxOhTxMSr6iXiQwwFTphj7pyv8vfEGXHAB/Ptv6cR1UlFREBR0+jaBgXDjjWD/761iwwa48EIYMMDH5igVESk/Nm/eTGpqKl27dnWfCw8Pp02bNixduhSApUuXEhERQcuWLd1tunbtit1u59dff3W36dChAwEBBd8o7NatG8nJyRz4bwHbpUuXFnqe/Db5z1OUWMq7pIgk976KfiIiIiJSFtQPiefl2rdQwR5odSgiPikxMJrX6gylkn9Fq0MRH+RndQAiZU2fPjB7Ntx5J2zbVnA+IQF694bXXzcGzq1YAc2bw4wZ0K2bBYEmJkJyMuzdC4DT6WT//v1UqlQJe36RLyrKaHfHHXD77ZD/Ae2MGTBvHkyYYJz301uJiIi3SE1NBSA2NrbQ+djYWPe11NRUYmJiCl338/OjUqVKhdokJSWd0Ef+tcjISFJTU8/4PGeK5WSysrLIyspyH2dkZADG3yqn03nKxxWH0+nE5XJ5rL+zVcG/AtEh0ew5sodNBzZZHo8neEtuyyLl1hzKq3mUW3OYlVf9nkTEkxpVqM6LtW/m9r9f5ahTy8WIFFW1gEq8XmcoUf5hVociPkqf1IuYoE8f6NULliwxBsRVqWJM/elwwE03Gdc3boT9+6F7d3jkEWP2THtpj71NTDQ2AKeT3N27ISbmxECaNoUff4S33oL774d9+yAjw6hsvvUWvPwynHdeKQcvIiJl1aRJkxg/fvwJ5/fs2eOxtQCdTifp6em4XK6CL7tYJKFiAnuO7GH7we2k7EghyO8MI/G9nDfltqxRbs2hvJpHuTWHWXk9ePCgx/oSEQFoXrEmz9cawh3/vEGmCn8iZ1QlIJLX6txObECE1aGID1PRT8QkDgd06nTi+caN4bffYOBA+PxzcLng4Yfh11/hnXcgMrLUQy0aux2GDDGGK44aBa+9ZpxfvRratoWbb4ZJk6ByZSujFBEp9+Li4gDYtWsXVapUcZ/ftWsXTZs2dbfZvXt3ocfl5uayf/9+9+Pj4uLYtWtXoTb5x2dqc+z1M8VyMqNGjWLkyJHu44yMDBISEoiOjiYszDPfdnQ6ndhsNqKjoy3/ILpOdB1W7l4JwJGAIyRGJVoaT0l5U27LGuXWHMqreZRbc5iV16AzLf8gInIWWofWZmqtm7jjnzc44sw68wNEyqn4gMq8VmcoVQMrWR2K+DgV/UQsEBEBc+fC44/D6NFG4W/ePGjZEubMgSZNrI7wNCpXhldfhcGDYehQ+P134/xrrxnBP/kk3HCDBcMWRUQEICkpibi4OBYuXOgurGVkZPDrr78ydOhQANq2bUtaWhorVqygRYsWAHz33Xc4nU7atGnjbvPQQw+Rk5ODv78/AAsWLKBu3bpE/vcNlbZt27Jw4UJGjBjhfv4FCxbQtm3bIsdyMoGBgQQGnrj+h91u9+iHmzabzeN9no1zKp3j3t+SvoUGMQ0sjMYzvCW3ZZFyaw7l1TzKrTnMyKt+RyJilpahtZhW+1aG/fMqB/M8M3OHSFlSIzCGV+vcphF+4hG6oxOxiN1uTOn51VcFg+M2bTJmyXznHWtjK5K2bY0hi1OmQGiocW7fPmM0YPv28Mcf1sYnIlKGHTp0iNWrV7N69WoANm/ezOrVq0lJScFmszFixAgeffRRPvvsM9asWcPAgQOpWrUqvXv3BqB+/fpccskl3HzzzSxbtoyffvqJ4cOH079/f6pWrQrAtddeS0BAAEOGDOHPP//ko48+YsqUKYVG4N1555189dVXPPPMM2zYsIFx48bx22+/MXz4cIAixSJQM7Kme3/TgU0WRiIiIiIiYo4mFWvwau2hhDtCrA5FxKucExTHm3WHqeAnHqOin4jFLr4YVqwwRvkBZGbCoEFw++2Q5e2zHvj5wf/+B8nJcM01Bed//hmaN4eRI421/0RExKN+++03mjVrRrNmzQAYOXIkzZo1Y8yYMQDcd9993HHHHdxyyy20atWKQ4cO8dVXXxWatuv999+nXr16XHjhhVx66aVccMEFvPrqq+7r4eHhfPPNN2zevJkWLVpw9913M2bMGG655RZ3m/PPP58ZM2bw6quv0qRJE2bPns3cuXM599xz3W2KEkt5p6KfiIiIiJQHDSok8Hqd24n0q2h1KCJeoV5wNd6oezuV/UOtDkXKEJvL5XJZHYTVMjIyCA8PJz093aPrxOzevZuYmBhNkeFhZTW3mZlw553GzJn5WreG2bMhIcH85/dIXhcuhGHDjCJgvqpV4bnn4KqrwGbzTLA+pqy+Zr2BcmsOs/Jqxt9bKR/K+r1aSnoK1SdXB6BX3V7M7T/X0nhKyptyW9Yot+ZQXs2j3JpD92oi4us2Hd3F0L9fITUnzepQRCzTtEINXqh1E2F+Gv0qnqW7bhEvERQEr7wCb74J+csYLVtmDJhbuNDa2IrswguNNf4mToTgYOPcjh3Qrx906wZ//WVtfCIiIl6oWmg1/O3Guoka6SciIiIiZV3N4Fhm1L+LphWSrA5FxBK9Krfm9Tq3q+AnplDRT8TLDB5szI5Zo4ZxvHevMQXo44+DT4zLDQw0Fitctw569iw4v2ABNGoEDz8MR49aF5+IiIiXcdgd1IioAcDmtM1oIg4RERERKesq+4fyep2h9K7c2upQREqNAzv3xPfikRr98bf7WR2OlFEq+ol4oebNjXX+unc3jp1OGDUKrrgC0tOtja3IatSAzz6DTz+F6saUZWRnw6OPQsOG8MUXloYnIiLiTZIijW85H8o+xN4jey2ORkRERETEfP52P8bX6M998b1x6GNqKeNCHcG8WPtmro/taHUoUsbp3VTES1WqBPPmwbhxBUvhffoptGwJa9ZYGlrxXH65MervwQfB35i6jM2b4bLLjCpmSoq18YmIiHiBmhE13fua4lNEREREypMBsR2YWvtmwhzBVociYoqkoBjerzeC88PqWh2KlAMq+ol4Mbsdxo41BsVFRhrn/vkHzjsPZsywNrZiCQkx1vn74w/o0qXg/Ny5UL8+PPGEMQpQRESknKoZqaKfiIiIiJRfbcPq8n69EdQMirU6FBGPuiCsHu/Wu5PqQdFWhyLlhIp+Ij6ge3djus9mzYzjI0dgwAD43/98rFZWrx58+61RsYyLM84dOQIPPABNm8KiRVZGJyIiYhkV/URERESkvEsMiubdenfSPryB1aGIeMSg2E68UOsmQjWKVUqRin4iPiIpCX76CW68seDcCy9Ap06wfbtlYRWfzQbXXAMbNhhVS/t/b0Pr10PnznD99ZCaam2MIiIipUxFPxERERERqOgI4vlzbuSG2M5WhyJy1gJsfjxa41pGxl+O3aYSjJQuveJEfEhwMLzxBrz2GgQEGOeWLoXmzX1wkFx4OEyZAr/9Bm3aFJx/7z1jROCLL0JennXxiYiIlKJCRb80Ff1EREREpPyy2+zcFd+Tx2oMINDmZ3U4IsUS7R/GG3WH0bNyS6tDkXJKRT8RH3TTTcaov8RE43j3bujaFZ5+Glwua2MrtmbN4Oef4dVXoVIl41x6OtxxB7RuDcuWWRufiIhIKQgPCqdSsPF3UCP9RERERESgR+UWvFF3GNUCKlkdikiRNKuYxIx6d9G4QnWrQ5FyTEU/ER/VsqWxzt/FFxvHeXlw771w1VVw8KC1sRWb3Q433wzJyTBkSMH5lSvhvPPgtttg/35ISTHOnWpLSbHuZxARESmh/NF+W9O3kp3nS4v2ioiIiIiYo1GF6sxucC/9otthw2Z1OCInFWTz5974XrxZZxgxAeFWhyPlnM8X/SZNmkSrVq0IDQ0lJiaG3r17k5ycbHVYIqUiKgq+/BJGjy449/HH0KoVrFtnXVxnLSoKXn/dGMbYuLFxzuWCV16BWrWMrUWLU29166rwJyIiPiu/6OfCxb9p/1ocjYiIiIiIdwhxBPJg4pW8Wuc2jfoTr9O0QhIzG9zDdbEdtX6feAWffxUuXryYYcOG8csvv7BgwQJycnK4+OKLOXz4sNWhiZQKhwMmTIDPPjOWyQNjwFzr1jBzprWxnbXzzzeGMT73HFSsaJw7cAByck7/uMxM2LvX/PhERERMUDPimHX9NMWniIiIiEghrUNra9SfeI0gmz/3xPfirbrDqB4UbXU4Im4+X/T76quvuOGGG2jYsCFNmjRh+vTppKSksGLFCqtDEylVPXsadbImTYzjw4ehXz+4664z18q8kp8fjBgBGzYYP4iIiEgZlz/SD1T0ExERERE5mfxRf6/Uvo2qGvUnFmlaoQYfNbib6zW6T7yQn9UBeFp6ejoAlSqd+k0/KyuLrKws93FGRgYATqcTp9PpkTicTicul8tj/UkB5fbUkpLgxx/h9tttvPuu8Y2nyZPht99cfPihiypVTv1Yr81rlSowYwZccAH2O+44Y3On0wle9jN4bW7LAOXWHGblVb8nkdNT0U9EREREpGjahNXm4wb38uy2z5m9dykuXFaHJOVAoM2PYdUu5fqYDir2idcqU0U/p9PJiBEjaNeuHeeee+4p202aNInx48efcH7Pnj1kZmZ6LJb09HRcLhd2u94APEm5PbMnnoCGDYN5+OEwcnJs/PijjebNnbz6ahpt2px82J+359WvTh2iitAu+957ybr8crI6dMBZrZrpcRWFt+fWlym35jArrwcPHvRYXyJlUaGiX5qKfiIiIiIipxPiCGR09b5cFNmYcf9+xI7sA1aHJGVYkwo1eKRGf2oExVgdishplami37Bhw1i7di0//vjjaduNGjWKkSNHuo8zMjJISEggOjqasLAwj8TidDqx2WxER0frg2gPU26L5t57oUMHF1dfDdu22di920HfvpV48kkX//sf2I6b+tzr83qa0bvHClq0iKBFiwBw1asHXbviuugi6NgRQkNNDPDUvD63Pky5NYdZeQ0KCvJYXyJlUUJ4Ag6bgzxXnkb6iYiIiIgUUZuwOnzc4D6N+hNTaHSf+JoyU/QbPnw48+bN44cffiA+Pv60bQMDAwkMDDzhvN1u9+iHmzabzeN9ikG5LZq2bWHlSujfH777DnJzbYwcaePXX+H116FixcLtvTqvZxGTbcMG2LAB24svGmsEtm0LF10EF18MLVuCw2FCoKeIxZtz6+OUW3OYkVf9jkROz8/uR/WI6mw6sIlNBzbhcrmwHf8tHREREREROUHBqL8mPL1tLn8d3Wl1SFIGXBBWj3sTemt0n/gUn//0zeVyMXz4cD755BO+++47kpKSrA5JxKtER8PXX8MDDxSc++gjaNMGkpOti8s0b74J48bB+ecXLurl5sKSJTBmDJx3HkRFwZVXwiuvwCaNphAREe+QP8VnRlYG+4/utzgaERERERHf0iasNh/Vv5uJNa6lWkDRZo0SOV7jCtV5o87tTK19iwp+4nN8fqTfsGHDmDFjBp9++imhoaGkpqYCEB4eTnBwsMXRiXgHPz+YNMko9A0aBBkZsG4dtGoFb70FvXvD4sWQnBxE3brGTJilOAiuaKKiICgITrfuZlAQXHghJCbC2LGQng6LFsE338CCBfD33wVt09JgzhxjA6hZs2AUYJcuEBFh4g8jIiJycjUjjlnX78AmKodUtjAaERERERHfY7fZuaxyS7pFNmXW3qW8tnMB+3MPWR2W+ICaQbHcUe1SukQ0sjoUkbPm80W/l19+GYBOnToVOv/WW29xww03lH5AIl6sd2/47Tfo0wfWroWDB6FvX2Oaz0OH7EAEAPHxMGWK0c5rJCYaQxP37j11m6goo12+8HDo1cvYALZsMYp/CxbAwoWw/5gRFJs2GaP+XnnFmEq0VSujAHjRRcbIQH9/U34sERGRY+WP9AOj6NeqWisLoxERERER8V3+dj+ujWlP78qteWfXIt7ZtYjDziyrwxIvFOsfwdCq3bi8ciscWrdPfJzPF/1cLi3MKlIctWvDL7/ALbfAjBnGuUPHfdlp+3ajGDh7thcW/o4t6hVXjRpw883GlpcHq1YVjAL86SfIyTHaOZ3w66/GNmGCURXt3NkoAF50EdStC1pjSURETHB80U9EREREREomxBHIbVW7cXV0O15LXcCsPT+T48qzOizxAuGOEIbEXUj/mAsItOsL/1I2+HzRT0SKr0IFePtt+PJLY5bL47lcRk1rxAhjkJzXTfXpCQ4HtGxpbA8+CIcPG3Oc5o8E/PPPgraHDsHnnxsbQEJCQQHwwguNhROPl5JSMCrR6cRv/36oVMkYRQgnjkoUERFBRT8REREREbNU8q/I/QlXMCCmAy/t+Ir5+1fiRANKyqMgewADYtozOK4LoQ4tESZli4p+IuXUjz+evOCXz+WCrVthyRI4bvbcsqlCBbj0UmMDY7jjt98WFAF37y5ou3UrvPmmsQE0b15QBGzXzmhbt657/UE7EHX88wUFGdOVqvAnIiLHKFT0S1PRT0RERETE0+IDK/NY0gBuiO3M89u/YEnGeqtDklLih53eUW24rWo3ov3DrA5HxBQq+omUUzt3Fq3d1q3mxuG1qlWDQYOMzemENWsKCoA//OAu6AGwcqWxPfEEBAdD06aFr59MZqYxElBFPxEROUZkcCQRQRGkZaZppJ+IiIiIiInqhFTlxdo3s/ZwCu/v/oFvDvxOrqb9LJNCHUH0rtyG/jEXEB9Y2epwREylop9IOVWlStHajR5t1L+6dDE3Hq9mt0OTJsZ2zz1Gwe7HH40C4DffwOrVBW2PHoWlSy0LVUREfF/NyJqs3LmSlPQUcvJy8HdobQkREREREbOcWyGRSUnXMTL+cmbu+YlZe5ZyIPeQ1WGJB1QPjObamPZcXrkVIY5Aq8MRKRV2qwMQEWu0bw/x8cbafaeTkmIsW9evH2zbVjqxeb2gIOja1RjZt2oV7NoFM2bA4MFGhVRERKQEkiKSAHC6nGzNKK9D7kVERERESle0fxjDqnbnm0ZjeKR6f+oG6zMeX2TDxvlhdZla62Y+bfgA/WMuUMFPyhUV/UTKKYcDpkwx9o8v/OUf165dcG7mTKhXz6hzZWeXTow+IyYGrrnGWONv61aYPbtojxszBubPh5wcc+MTERGfUmhdP03xKSIiIiJSqgLsfvSKas3MBnfzXr076RPVhhC7ikbeLsovlCFxF/L5uaN4ufatXBBeH9uZRjuIlEEq+omUY336GPWp4wenxcfDxx/Dhg1GHSsqyjh/+DA88AA0bmzMbCknYbNBUlLR2n7xBVx6KVStCkOHwuLFxvqBIiJSrqnoJyIiIiLiHRpVqM7Y6v34tvFYxiReRcOQBKtDkmPYsdE+rD7PnTOYrxuP4X/VepAQGGV1WCKW0pp+IuVcnz7QqxcsXuwkOTmDunXD6NjRjsNhXB88GHr3hocfhpdfNmpSyclw8cVw5ZXw7LOQmGjpj+D79u6FadOMrVo1Yy7Va66BFi3OPP+qiIiUOSr6iYiIiIh4lwqOIK6MbsuV0W1JPrKdz/YtZ1Han2zL3md1aOVS/ZB4OkecS6/KrYgLiLQ6HBGvoqKfiOBwQKdO0KBBJjExYdiPGwMcGQkvvgg33QTDhsHPPxvnP/7YmJ3yoYfg7rshUDMdFM/TT8OyZfD553D0qHFu+3ajkvrss1CrFvTvbxQAGzSwNlYRESk1KvqJiIiIiHivuiHVuDekGvcm9Oafo6ksTlvLovQ/WXs4BScuq8MrkwJsfrQOrUXHiIZ0DG9IbECE1SGJeC0V/USkyJo2hSVL4N134b77YPduOHLEKPpNnw7PPw+XXGJ1lF4gKgqCgiAz89RtgoLgqquMaumhQ/DZZ/DBB/D11wVr/P3zDzz6qLE1amQU//r3L/r0oSIi4pMSwxOx2+w4XU4V/UREREREvFit4DhqBccxpEpX9uUc5If0dSxO+5OlB/8i05ltdXg+LdKvIu3D69MxvCHnh9UlxKHRBiJFoaKfiBSL3Q6DBhlTgo4da4wAdDrh77+he3djKtDnnoMaNayO1EKJicYcqHv3AuB0Otm/fz+VKlXCnj+MMiqqYF7UihXh2muNbf9+mDPHKAB+/z24/vuG2Jo1xvbgg9CmjVEAvPpqqFLFgh9QRETMFOAIICEsgX/T/1XRT0RERETER1T2D+WKqDZcEdWGLGcOvx78m0Vpa/khfR17cjKsDs8n1AyKpWN4QzpFNKRxherYbfYzP0hEClHRT0TOSkQETJkCQ4YYU37++KNxfu5c+OorozZ1773GgLZyKTGxoKjndJK7ezfExHDC3KnHq1TJmEf1pptg506YNcsoAP7yS0GbX381tpEjjXlZ+/c3FlisVMm0H0dEREpXzcia/Jv+LwcyD3Dg6AEig7VOhYiIiIiIrwi0+9MhvAEdwhvgcrlYd2Qbi9P/ZNWhzaw/so2DeUetDtErRPuHUT8kntahtekY3oDEoGirQxLxeSr6iUiJNG4MP/wA779vFPlSU41ZLceMgbffNgqDPXpYHaWPqlIF/vc/Y9u8GT76yCgA/vGHcd3phO++M7Zhw6BbN6MA2KuXMXpQRER8Vs3Imny/5XsANqdtVtFPRERERMRH2Ww2GlZIoGGFBABcLhfbsvex7vA21h3Zyvoj21h/ZBsZZbwQGOMfToOQeOqHxNOgQgL1Q+KJ9g+zOiyRMkdFPxEpMZsNrrsOLr8cxo0z1vbLy4ONG+Gyy6BnT5g8GWrWtDpSH5aUBA88YGzr1sGHHxoFwH/+Ma7n5MC8ecYWHGwkvX9/Y87VcjvcUkTEd9WMLPijuenAJppXaW5hNCIiIiIi4ik2m42EwCgSAqPoVqmp+/y2rH2sO7KVdYe3uQuB6XlHrAu0BGL9I4wCX4V4GoTE0yAkgcr+oVaHJVIuqOgnIh4TFgbPPgs33gjDh8Pixcb5zz+Hb74x6lX332/UpKQEGjSARx6B8eNhxQqjAPjhh7B9u3H96FGYOdPYwsKgTx9jDcAuXcBPb/siIr7g+KKfiIiIiIiUbfGBlYkPrMzFkU3d57Zn7Wf9kW3syN7P3pwM9vy37f1vO5iXWepx2rAR4RdClH8YUf5hRP+3RfmHkRgYRf2QeBX4RCykT39FxOPOPRe+/96oQ919t7E0XVaWUaN65x1jys+ePa2Osgyw2aBlS2N78kljYcUPPjDWAdy3z2iTkQHTpxtbdDRcdZVRADz//IL1BVNSYO/eUz9PVFTB+oQiIlIqVPQTEREREZFqgZWoFljplNePOrPdxcC9ORnsyT5mPyeDTGc2uS4neTjJdeWR53Iax648bNhw2Ow4bHb8bHYcNgcO7PjZHIQ6gtxFvZiAsEIFvii/UPztKiuIeCv91ykiprDZjNrSZZcZg9ImT4bcXGNpussvN9b5mzwZatWyOtIywm6HDh2M7fnnYeFCowD4ySdw8KDRZs8eeOklY0tIgH79oFMn6NvXWIjxVIKCIDlZhT8RkVKkop+IiIiIiJxJsD3APVWoiAiA3eoARKRsCw2Fp56C33+Hzp0Lzn/xBTRsCA8/DEd8c3py7+XvD5dcAm+/Dbt3w8cfG4W9Y9f227oVnn7aqMqeruAHxvXTjQQUERGPqxxcmdAAY0ocFf1EREREREREpChU9BORUtGggTH47KOPoFo141x2Njz6qHFt7lxwuSwNsWwKCjLW9Js1yygAvvsuXHqp1vYTEfFyNpvNPdrv3/R/yXXmWhyRiIiIiIiIiHg7Ff1EpNTYbHD11bBhA9x/vzEgDeDff+GKK4xa1N9/WxtjmRYaCtddZwyzTE2FV16BFi2K9ti334Zvvy1YK1BEREyXX/TLdeayLWObxdGIiIiIiIiIiLdT0U9ESl3FivD44/DHH9C1a8H5r76Cc8+Fhx6Cw4eti69cqFwZbrkFXn21aO2ffx4uugiioqB6daNK+8gjMG8e7NihYZoiIibQun4iIiIiIiIiUhwq+omIZerVg2++MWaeTEgwzmVnw2OPQf36xlJ0qiV5oZQUYz7WsWOhZ09jvtYqVaB7d6Ni+/HHsGmTfnkiIiWkop+IiIiIiIiIFIcWdRIRS9ls0LevUS+aOBGefhpycmDrVuP8RRfBCy9A3bpWR1rOjR8P+/fDypWwahUcOlT4+q5dxlDNr74qOBcRAc2aQfPmBf/WqQMOR6mGLiLiq1T0ExEREREREZHiUNFPRLxChQrGCL8bboD//Q++/to4v2ABNGoEI0fC6NHG1KB5ebBkCezcaQwwa99edSTTXXaZUbQDcDph40ajAJhfBFy58sT1/tLS4PvvjS1fSAg0aWL0lV8MbNgQAgKKHktKCuzd647Fb/9+qFQJ7P8NXo+KgsTEs/5RRUS8hYp+IiIiIiIiIlIcKvqJiFepUwfmzzdmjxwxwqjv5OTAE0/Ae+9Bv34wcyZs21bwmPh4mDIF+vSxKmofFhUFQUGQmXnqNkFBRrt8djvUrm1s/foZ51wuY3hmfgEwf9uxo3BfR47A0qXGls/f36jsHjsisHFjo0B4vJQUY9jnf/Hagajj2wQFQXKyCn8i4vOqh1fHhg0XLhX9REREREREROSMVPQTEa9js8EVV0C3bjBpEjz5pLHW3/bt8OyzJ7bfvt2YCnT2bBX+ii0x0SiQ5Y+cO5mijJyz2Yw2iYnQq1fB+V27CgqB+f9uOu6D65ycgiJhPrvdWPQxf0Rg8+bQtKkR5+kKlGBc37tXRT8R8XmBfoHEh8WzNWOrin4iIiIiIiIickYq+omI1woJgQkTYNAgY8rP+fNP3s7lMv4dPtxYGzA4uPRiLBPyi3VmiI2FSy4xtnwHDsDq1YVHBSYnG9OG5nM6Yd06Y3vvvYLz8fHmxCki4qVqRtZka8ZW9h3dR3pmOuFB4VaHJCIiIiIiIiJeSkU/EfF6tWrBvfeeuuiXb+dOY23AhASoWfPkW1SUMShNLBQZCZ07G1u+w4fhjz8Kjwhcu9YYBXisY+d1PZ1du4xqsH7ZIuLjkiKTWPzvYgA2p22maVxTawMSEREREREREa+lop+I+ITU1KK1c7mMZd9SUmDRohOvV6wISUknLwjWqGEsB+dJeXmweDEkJwdRty507AgOh2efo0yoUAHatjW2fFlZ8OefhUcErlplnD+TSy+F0FBjitAGDaB+/YItKQn89OdPRHxDzYia7v3NB1T0ExEREREREZFT06eeIuITqlQpWrs6dWDfPmM7mUOHYM0aYzuZatUKioDHFwfj4oo3cGzOHLjzTti2zQ5EAMbslFOmeOfag3l5sGSJMWKyShVo397iAmVgYMF6fkOGGOeWL4fWrYv2+IMHjfbLlxc+HxBgvFCOLQTWrw9163q+6vsfr8utiPiMmpEFRb+P131MZHAk7RPb47DrTUREREREREREClPRT0R8Qvv2RsFs+/aCNfyOZbMZ19etM4op6emweTNs2nTitmXLibNG5tu+3diWLDnxWnDwyUcJJiUZW4UKBW3nzIG+fU+Mdft24/zs2d5V+CsoUBac88oCZVErZe3awY4dxi/7+F9CdrYxdejatYXP2+3GL/L4YmD9+hB+lmtopaTw3cy9PPUU7NpdcDo2xpiytsvVUeatp3iWNDpVxLukpKe4999f+z7vr32f+LB4plwyhT71vekN2pDnzGPxlsUk70im7pG6dKzRUQVKERERERERkVJic7lO9vF5+ZKRkUF4eDjp6emEhYV5pE+n08nu3buJiYnBbrd7pE8xKLfm8IW85hfSoHAdJ3/0XVELaXl5RvFt06aTFwZ37z5zHycTG1swTei8ecZAs5PJL1Bu3uwdxZRTFSiLm9dSsXIltGhx5nYrVhgjBI8cgb/+MqrB69cXbH//ferK78lUqWIU/46fKjQ29tTDP1NSyKtVF0dO5im7zfMPwvFPsncU/kqhQGnG31vxHVOnTuWpp54iNTWVJk2a8MILL9C6iCN3y+u92pz1c+g7sy8uCr9B2zDed2ZfPdurCn9z1s/hzq/uZFtGwTdIvL1AuSRlCTsP7qRKaBWvH0FZqKBa1bsLqr6UW+XVPMqtOczMq+7VRERERHyfin6U3w+SfJVyaw5fyevJRqQlJMDkyZ4rTB06dPJiYP65oiwpVxTBwcZskn5+Z94cjqK1K25bmw0efRTS0k4dZ+XKMHVqQXu73djOdr9EfWxNIfqCugRx6kJaJkEcXpmMvYZRoLLZCupy7v2cHOxbNuH4az325ILNlrwe2+HDRf8lRkScfGRgjRrkrViNo/WZC5R5y1bgaNW86M9phlIqUOqDpPLro48+YuDAgUybNo02bdowefJkZs2aRXJyMjExMWd8fHm8V8tz5lFjSo1CBbRj2bARVzGOn2/8mYqBFQnyCyLILwg/uzUTeahAaS5filexmsOXYgXfilexFtC9moiIiIjvU9GP8vlBki9Tbs3hS3m1cn00pxNSU08+beimTUZMYq4EUohi7ymv7yWKrZxtYcpFPNtowDrqs77QFn2a5zzeEYJJIYF6/HXGtp39fuBP/2Y4MSqcLmy4bHb3v/nnbHbjg/NjC5in+rcobY7999zslXy91/wCpT5IKr/atGlDq1atePHFFwHjb05CQgJ33HEHDzzwwBkfXx7v1RZtWUTntzsX+3EOm4MgvyCC/YPdhcAgvyCC/Qofu9s4jjs+vs1xjztZmwB7AOe+fC7bD24/aUw2bMSHxbP5zs1eMXLGFwuUvhKvYjWHL8UKvhWvYi1M92oiIiIivk9FP8rnB0m+TLk1h/LqGUeOwMyZMHjwmdsmJkJgIOTmFmx5eYWP8zen0/zY5fQqs7dQETC/MJjI1lKLwYlRAHRiL/RvSc/5kUM1zlyx/u2VFbS8RUU/KZ7s7GxCQkKYPXs2vXv3dp8fNGgQaWlpfPrpp2fsozzeq32w5gOunXOt1WF4VGRQJIF+ge4PqAFsx02RfKprx54/3bUz9edyuUhJTyHPlXfKOP1sflSPqH5CXydzfFzFUZT+XS4Xm9M2k+vMPWUbP7sfNSNqFqk/M7lcLjalbVKsHuZLsYJvxVuWYvXUlyt0ryYiIiLi+6yZ/0dEpIwKCYHrr4eHHzbWDTzZ1yry1/TbtKnoIxSdzsIFwVMVB4/fztTu999hwoQzP//QocZ6hU6n8TM5nebvn+xcair88MOZ423d2piW1OUq+B2UfD8Kp6s9f9KetcdcC8o9ROKRDSRlrqf60fXUOLqepMz1xGf+jR+erdbacQEuHB7ut6j2Fn2wo4jb3r17ycvLIzY2ttD52NhYNmzYcNLHZGVlkXXMXMoZGRmAUahzeuhbEE6nE5fL5bH+PC22QuyZGwEdEjsQGhhKZm7mCdvR3KMF+zlHTxgdUtoOZB6w9PmLKteVy8YDG60Oo8hynbn8tf/MI8u9gWI1hy/FCr4Vr6/E6sLF1oytLN6ymE41Op11P976N1FEREREik5FPxERD3M4YMoU6NvXKPAdW/jL/5Lw5MnFm5I0f007f3+Phkrv3vDWW2cuUL7wQulNoXo6eXlQo8aZ4/3559KMtyLQ8r+tQN7Pv0K78874aFer1tjCQgtXO4+vfJ7tuSI+JudwNv4H958x1qios0yRSDFNmjSJ8ePHn3B+z549ZGaeeu3J4nA6naSnp+NyubxypF/doLpUqVCF1MOpJy3W2bBRpUIVZnSbUaRRHS6Xi1xnLpl5mWTlZZGZa/ybv59//thrmXmZZOWe5Nxxj089nMofe/84YwxRQVEE+gUWxHT8z+XipNeOn5ik0LWitvvvWlZeFodyDp0x1gr+FQhwBJy+UQlqqEUtwGbnZXMk98gZ2wU7gs8cr8my87I5mnf0jO0Ua/H4UqzgW/GWxViTdyTTIKTBWT/PwYMHz/qxIiIiIuIdVPQTETFBnz4wezbceSds21ZwPj7eKPj18Y6lQUwpUJrJl+J1BBWtQmub9jI0P/spMz3BvnwltD7zmn7NmpVCMFLmREVF4XA42LVrV6Hzu3btIi4u7qSPGTVqFCNHjnQfZ2RkkJCQQHR0tEen97TZbERHR3tl0Q/g+e7Pc/Xsq7FhK1Qkyp9Sckr3KVSJq2JVeG55zjxqPl+T7Qe3n7JAGR8Wz8Y7Nlq+pt+iLYu48N0Lz9jus/6flWi0jKcUNd55186zPF7Fag5fihV8K96yGGvdqnWJiYk56+cJCgo668eKiIiIiHdQ0U9ExCR9+kCvXrB4sZPk5Azq1g2jY0e7VxSkjuUrBcp8vhavLyjqa9LbXrviGwICAmjRogULFy50r+nndDpZuHAhw4cPP+ljAgMDCQwMPOG83W73aIHOZrN5vE9P6tuwL7Pts7nzqzvZllHwhhcfFs/kSybTp753vOHZ7XamdJ9C35l9T1mgnHzJZPz9PDxc/Sx0rNGR+LB4tmecvkDZsUZHr3hd+FK8itUcvhQr+Fa8ivVEVv+cIiIiIlJyuqMTETGRwwGdOsEVV2TSqZP3Fk369IEtW+D772HGDOPfzZu9t4CWH+/ChU5eeimNhQud3hdvVBSc6dvSQUGaM1PKhZEjR/Laa6/x9ttvs379eoYOHcrhw4cZPHiw1aF5vT71+7Dlzi18P+h7ZvSZwfeDvmfznZu9puCXr0/9Psy+ejbVwqoVOh8fFs/sq2d7TbwOu4Mpl0wBCiubpRUAALmpSURBVAqS+Y4tUFo9IjGfL8WrWM3hS7GCb8WrWEVERESkLLK5jl/8wkdNnTqVp556itTUVJo0acILL7xA69ati/TYjIwMwsPDSU9P9+iUUbt37yYmJkbflvMw5dYcyqt5lFvzeH1uU1Jg795TX4+KgsTE0ovnVFJSoG5dON1aaUFBkJxconjN+HsrvuPFF19036s1bdqU559/njZt2hTpsbpX8x15zjwWb1lM8o5k6latS8caHb3yQ+g56+ecMIIyISzBq0ZQHsuX4lWs5vClWMG34lWsBXSvJiIiIuL7ykTR76OPPmLgwIFMmzaNNm3aMHnyZGbNmkVycnKR5rPXB0m+Rbk1h/JqHuXWPMqtBx1ToHQ6nezfv59KlSoV5NUDBUp9kCRnS/dqvsVXcpvnzGNJyhJ2HtxJldAqtE9s75UFyny+UlAF38qt8moe5dYcZuZV92oiIiIivq9MFP3atGlDq1atePHFFwHjg4aEhATuuOMOHnjggTM+Xh8k+Rbl1hzKq3mUW/Mot+YwK6/6IEnOlu7VfItyax7l1hzKq3mUW3PoXk1ERERETsXP6gBKKjs7mxUrVjBq1Cj3ObvdTteuXVm6dOlJH5OVlUVWVpb7OCMjAzBunJ1Op0ficjqduFwuj/UnBZRbcyiv5lFuzaPcmsOsvOr3JCIiIiIiIiIiYh6fL/rt3buXvLw8YmNjC52PjY1lw4YNJ33MpEmTGD9+/Ann9+zZQ+bp1jMqBqfTSXp6Oi6XS99o9DDl1hzKq3mUW/Mot+YwK68HDx70WF8iIiIiIiIiIiJSmM8X/c7GqFGjGDlypPs4IyODhIQEoqOjPTpllM1mIzo6Wh9Ee5hyaw7l1TzKrXmUW3OYldegoCCP9SUiIiIiIiIiIiKF+XzRLyoqCofDwa5duwqd37VrF3FxcSd9TGBgIIGBgSect9vtHv1w02azebxPMSi35lBezaPcmke5NYcZedXvSERERERERERExDw+/+lbQEAALVq0YOHChe5zTqeThQsX0rZtWwsjExERERERERERERERESkdPj/SD2DkyJEMGjSIli1b0rp1ayZPnszhw4cZPHiw1aGJiIiIiIiIiIiIiIiImK5MFP369evHnj17GDNmDKmpqTRt2pSvvvqK2NhYq0MTERERERERERERERERMV2ZKPoBDB8+nOHDh1sdhoiIiIiIiIiIiIiIiEipKzNFv5JwuVwAZGRkeKxPp9PJwYMHCQoKwm73+aUTvYpyaw7l1TzKrXmUW3OYldf8v7P5f3dFikr3ar5FuTWPcmsO5dU8yq05dK8mIiIiIqeioh9w8OBBABISEiyOREREpOw7ePAg4eHhVochPkT3aiIiIqVH92oiIiIivsvm0le4cDqd7Nixg9DQUGw2m0f6zMjIICEhga1btxIWFuaRPsWg3JpDeTWPcmse5dYcZuXV5XJx8OBBqlatqm/7S7HoXs23KLfmUW7NobyaR7k1h+7VRERERORUNNIPsNvtxMfHm9J3WFiY/ufGJMqtOZRX8yi35lFuzWFGXvWtcTkbulfzTcqteZRbcyiv5lFuzaF7NRERERE5nr66JSIiIiIiIiIiIiIiIuLjVPQTERERERERERERERER8XEq+pkkMDCQsWPHEhgYaHUoZY5yaw7l1TzKrXmUW3Mor1Ie6HVuHuXWPMqtOZRX8yi35lBeRURERORUbC6Xy2V1ECIiIiIiIiIiIiIiIiJy9jTST0RERERERERERERERMTHqegnIiIiIiIiIiIiIiIi4uNU9BMRERERERERERERERHxcSr6iYiIiIiIiIiIiIiIiPg4Ff1MMnXqVGrUqEFQUBBt2rRh2bJlVofk1X744Qd69uxJ1apVsdlszJ07t9B1l8vFmDFjqFKlCsHBwXTt2pW///67UJv9+/czYMAAwsLCiIiIYMiQIRw6dKgUfwrvM2nSJFq1akVoaCgxMTH07t2b5OTkQm0yMzMZNmwYlStXpmLFilx55ZXs2rWrUJuUlBR69OhBSEgIMTEx3HvvveTm5pbmj+J1Xn75ZRo3bkxYWBhhYWG0bduW+fPnu68rr57x+OOPY7PZGDFihPuccnt2xo0bh81mK7TVq1fPfV15lfJG92rFo3s1c+hezTy6VysdulfzHN2riYiIiIgnqOhngo8++oiRI0cyduxYVq5cSZMmTejWrRu7d++2OjSvdfjwYZo0acLUqVNPev3JJ5/k+eefZ9q0afz6669UqFCBbt26kZmZ6W4zYMAA/vzzTxYsWMC8efP44YcfuOWWW0rrR/BKixcvZtiwYfzyyy8sWLCAnJwcLr74Yg4fPuxuc9ddd/H5558za9YsFi9ezI4dO+jTp4/7el5eHj169CA7O5uff/6Zt99+m+nTpzNmzBgrfiSvER8fz+OPP86KFSv47bff6NK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     },
     "metadata": {}
    }
   ],
   "source": [
    "# ============================================================================\n",
    "# CELL 17: Final Dashboard\n",
    "# ============================================================================\n",
    "fig, axes = plt.subplots(2, 3, figsize=(18, 10))\n",
    "\n",
    "# Training Loss\n",
    "ax = axes[0, 0]\n",
    "if metrics[\"val_loss\"]:\n",
    "    ax.plot(metrics[\"steps\"], metrics[\"val_loss\"], 'b-o', label='Val Loss', linewidth=2)\n",
    "train_losses_clean = [t for t in metrics[\"train_loss\"] if t is not None]\n",
    "train_steps_clean = [s for s, t in zip(metrics[\"steps\"], metrics[\"train_loss\"]) if t is not None]\n",
    "if train_losses_clean:\n",
    "    ax.plot(train_steps_clean, train_losses_clean, 'r-s', label='Train Loss', linewidth=2)\n",
    "ax.set_xlabel('Step')\n",
    "ax.set_ylabel('Loss')\n",
    "ax.set_title('Training Progress', fontweight='bold')\n",
    "ax.legend()\n",
    "ax.grid(True, alpha=0.3)\n",
    "\n",
    "# Perplexity\n",
    "ax = axes[0, 1]\n",
    "ppl_improvement = (init_val_ppl - final_val_ppl) / init_val_ppl * 100 if init_val_ppl > 0 else 0\n",
    "if metrics[\"val_ppl\"]:\n",
    "    ax.plot(metrics[\"steps\"], metrics[\"val_ppl\"], 'g-o', linewidth=2)\n",
    "ax.set_xlabel('Step')\n",
    "ax.set_ylabel('Perplexity')\n",
    "ax.set_title(f'Perplexity ({ppl_improvement:+.1f}%)', fontweight='bold')\n",
    "ax.grid(True, alpha=0.3)\n",
    "\n",
    "# Generation Changes\n",
    "ax = axes[0, 2]\n",
    "ax.pie([sum(generation_changes), len(generation_changes) - sum(generation_changes)],\n",
    "       labels=['Changed', 'Similar'], colors=['#2ecc71', '#e74c3c'], autopct='%1.0f%%')\n",
    "ax.set_title(f'Generations ({sum(generation_changes)}/{len(generation_changes)} changed)', fontweight='bold')\n",
    "\n",
    "# Memory\n",
    "ax = axes[1, 0]\n",
    "standard_required = param_count * 12\n",
    "bars = ax.bar(['Standard', 'AXIOM', 'GPU'], [standard_required, peak_mem, VRAM_GB],\n",
    "              color=['#e74c3c', '#2ecc71', '#3498db'])\n",
    "ax.set_ylabel('Memory (GB)')\n",
    "ax.set_title(f'Memory: {standard_required/peak_mem:.0f}x Reduction', fontweight='bold')\n",
    "\n",
    "# Energy Savings\n",
    "ax = axes[1, 1]\n",
    "H100_TDP_KW = 0.7\n",
    "TRAINING_HOURS = 1000\n",
    "standard_energy = standard_gpus * H100_TDP_KW * TRAINING_HOURS\n",
    "axiom_energy = 1 * H100_TDP_KW * TRAINING_HOURS\n",
    "bars = ax.bar(['Standard', 'AXIOM'], [standard_energy, axiom_energy], color=['#e74c3c', '#2ecc71'])\n",
    "ax.set_ylabel('Energy (kWh)')\n",
    "energy_saved_pct = (1 - axiom_energy/standard_energy) * 100\n",
    "ax.set_title(f'Energy: {energy_saved_pct:.0f}% Reduction', fontweight='bold')\n",
    "\n",
    "# Weight Changes\n",
    "ax = axes[1, 2]\n",
    "if weight_changes:\n",
    "    cats = list(weight_changes.keys())\n",
    "    # Truncate long layer names to prevent overlap\n",
    "    cats_short = [c[:20] + '...' if len(c) > 20 else c for c in cats]\n",
    "    changes = [weight_changes[c]['change_pct'] for c in cats]\n",
    "    ax.barh(cats_short, changes, color='#2ecc71')\n",
    "    ax.set_xlabel('Change (%)')\n",
    "    ax.set_title('Weight Changes (ALL = Full Training)', fontweight='bold')\n",
    "    ax.tick_params(axis='y', labelsize=8)  # Smaller labels to prevent overlap\n",
    "\n",
    "plt.suptitle(f'AXIOM: {MODEL_DISPLAY} Training Results', fontsize=16, fontweight='bold')\n",
    "plt.tight_layout(rect=[0, 0, 1, 0.96])  # Leave room for suptitle\n",
    "plt.subplots_adjust(hspace=0.3, wspace=0.3)  # Add spacing between subplots\n",
    "plt.savefig('axiom_dashboard.png', dpi=150, bbox_inches='tight', facecolor='white')\n",
    "plt.show()"
   ],
   "id": "bCGwj37l1NzA"
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "nQ00Hg7Z1NzB",
    "outputId": "ff3ff30f-357c-46cb-b831-5ab2854c7552"
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Saved: axiom_results.json\n"
     ]
    }
   ],
   "source": [
    "# ============================================================================\n",
    "# CELL 18: JSON Export\n",
    "# ============================================================================\n",
    "export_data = {\n",
    "    \"experiment\": f\"{MODEL_DISPLAY} AXIOM Training\",\n",
    "    \"date\": datetime.now().isoformat(),\n",
    "    \"model_params_b\": round(param_count, 2),\n",
    "    \"memory_standard_gb\": round(standard['total'], 1),\n",
    "    \"memory_axiom_gb\": round(mem_axiom, 2),\n",
    "    \"compression\": f\"{standard['total']/mem_axiom:.1f}x\",\n",
    "    \"energy_saved_pct\": round(energy_saved_pct, 1),\n",
    "    \"ppl_improvement_pct\": round(ppl_improvement, 2),\n",
    "    \"generations_changed\": f\"{sum(generation_changes)}/{len(TEST_PROMPTS)}\",\n",
    "    \"sources\": {\n",
    "        \"IEA\": \"iea.org/reports/energy-and-ai\",\n",
    "        \"Goldman_Sachs\": \"goldmansachs.com/insights/articles/ai-to-drive-165-increase-in-data-center-power-demand-by-2030\",\n",
    "        \"S&P_Global\": \"spglobal.com - Global data center power demand to double by 2030\"\n",
    "    }\n",
    "}\n",
    "\n",
    "with open('axiom_results.json', 'w') as f:\n",
    "    json.dump(export_data, f, indent=2)\n",
    "print(\"Saved: axiom_results.json\")"
   ],
   "id": "nQ00Hg7Z1NzB"
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "dekFzlBz1NzB",
    "outputId": "062f6fe2-e0b4-48a8-f02f-158678a54231"
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "\n",
      "==============================================================================\n",
      "\n",
      "     \u2588\u2588\u2588\u2588\u2588\u2557 \u2588\u2588\u2557  \u2588\u2588\u2557\u2588\u2588\u2557 \u2588\u2588\u2588\u2588\u2588\u2588\u2557 \u2588\u2588\u2588\u2557   \u2588\u2588\u2588\u2557\n",
      "    \u2588\u2588\u2554\u2550\u2550\u2588\u2588\u2557\u255a\u2588\u2588\u2557\u2588\u2588\u2554\u255d\u2588\u2588\u2551\u2588\u2588\u2554\u2550\u2550\u2550\u2588\u2588\u2557\u2588\u2588\u2588\u2588\u2557 \u2588\u2588\u2588\u2588\u2551\n",
      "    \u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2551 \u255a\u2588\u2588\u2588\u2554\u255d \u2588\u2588\u2551\u2588\u2588\u2551   \u2588\u2588\u2551\u2588\u2588\u2554\u2588\u2588\u2588\u2588\u2554\u2588\u2588\u2551\n",
      "    \u2588\u2588\u2554\u2550\u2550\u2588\u2588\u2551 \u2588\u2588\u2554\u2588\u2588\u2557 \u2588\u2588\u2551\u2588\u2588\u2551   \u2588\u2588\u2551\u2588\u2588\u2551\u255a\u2588\u2588\u2554\u255d\u2588\u2588\u2551\n",
      "    \u2588\u2588\u2551  \u2588\u2588\u2551\u2588\u2588\u2554\u255d \u2588\u2588\u2557\u2588\u2588\u2551\u255a\u2588\u2588\u2588\u2588\u2588\u2588\u2554\u255d\u2588\u2588\u2551 \u255a\u2550\u255d \u2588\u2588\u2551\n",
      "    \u255a\u2550\u255d  \u255a\u2550\u255d\u255a\u2550\u255d  \u255a\u2550\u255d\u255a\u2550\u255d \u255a\u2550\u2550\u2550\u2550\u2550\u255d \u255a\u2550\u255d     \u255a\u2550\u255d\n",
      "\n",
      "                  from QuarterBit\n",
      "\n",
      "==============================================================================\n",
      "\n",
      "MODEL:        Llama-2 70B (69B parameters)\n",
      "GPU:          NVIDIA RTX PRO 6000 Blackwell Server Edition\n",
      "MEMORY:       57.6 GB / 102 GB\n",
      "\n",
      "STANDARD:     840 GB (11 GPUs, 7.7 kW)\n",
      "AXIOM:        48 GB (1 GPU, 0.7 kW)\n",
      "COMPRESSION:  15x\n",
      "\n",
      "ENERGY SAVED: 91%\n",
      "PPL IMPROVED: +100.0%\n",
      "TRAINING:     FULL (all 2 layer types updated)\n",
      "\n",
      "==============================================================================\n",
      "\n",
      "SOURCES:\n",
      "  - IEA: iea.org/reports/energy-and-ai\n",
      "  - Goldman Sachs: 165% datacenter power increase by 2030\n",
      "  - S&P Global: Datacenter demand to double by 2030\n",
      "  - Microsoft CEO: \"Power is the biggest constraint\"\n",
      "\n",
      "==============================================================================\n",
      "\n",
      "   TRY IT FREE: pip install quarterbit\n",
      "\n",
      "==============================================================================\n",
      "   Clouthier Simulation Labs | quarterbit.dev | Y Combinator Spring 2026\n",
      "==============================================================================\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# ============================================================================\n",
    "# CELL 19: Final Summary\n",
    "# ============================================================================\n",
    "print(f\"\"\"\n",
    "{'='*78}\n",
    "\n",
    "     \u2588\u2588\u2588\u2588\u2588\u2557 \u2588\u2588\u2557  \u2588\u2588\u2557\u2588\u2588\u2557 \u2588\u2588\u2588\u2588\u2588\u2588\u2557 \u2588\u2588\u2588\u2557   \u2588\u2588\u2588\u2557\n",
    "    \u2588\u2588\u2554\u2550\u2550\u2588\u2588\u2557\u255a\u2588\u2588\u2557\u2588\u2588\u2554\u255d\u2588\u2588\u2551\u2588\u2588\u2554\u2550\u2550\u2550\u2588\u2588\u2557\u2588\u2588\u2588\u2588\u2557 \u2588\u2588\u2588\u2588\u2551\n",
    "    \u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2551 \u255a\u2588\u2588\u2588\u2554\u255d \u2588\u2588\u2551\u2588\u2588\u2551   \u2588\u2588\u2551\u2588\u2588\u2554\u2588\u2588\u2588\u2588\u2554\u2588\u2588\u2551\n",
    "    \u2588\u2588\u2554\u2550\u2550\u2588\u2588\u2551 \u2588\u2588\u2554\u2588\u2588\u2557 \u2588\u2588\u2551\u2588\u2588\u2551   \u2588\u2588\u2551\u2588\u2588\u2551\u255a\u2588\u2588\u2554\u255d\u2588\u2588\u2551\n",
    "    \u2588\u2588\u2551  \u2588\u2588\u2551\u2588\u2588\u2554\u255d \u2588\u2588\u2557\u2588\u2588\u2551\u255a\u2588\u2588\u2588\u2588\u2588\u2588\u2554\u255d\u2588\u2588\u2551 \u255a\u2550\u255d \u2588\u2588\u2551\n",
    "    \u255a\u2550\u255d  \u255a\u2550\u255d\u255a\u2550\u255d  \u255a\u2550\u255d\u255a\u2550\u255d \u255a\u2550\u2550\u2550\u2550\u2550\u255d \u255a\u2550\u255d     \u255a\u2550\u255d\n",
    "\n",
    "                  from QuarterBit\n",
    "\n",
    "{'='*78}\n",
    "\n",
    "MODEL:        {MODEL_DISPLAY} ({param_count:.0f}B parameters)\n",
    "GPU:          {GPU_NAME}\n",
    "MEMORY:       {peak_mem:.1f} GB / {VRAM_GB:.0f} GB\n",
    "\n",
    "STANDARD:     {standard['total']:.0f} GB ({standard_gpus} GPUs, {standard_gpus * 0.7:.1f} kW)\n",
    "AXIOM:        {axiom['total']:.0f} GB (1 GPU, 0.7 kW)\n",
    "COMPRESSION:  {standard['total']/peak_mem:.0f}x\n",
    "\n",
    "ENERGY SAVED: {energy_saved_pct:.0f}%\n",
    "PPL IMPROVED: {ppl_improvement:+.1f}%\n",
    "TRAINING:     FULL (all {len(weight_changes)} layer types updated)\n",
    "\n",
    "{'='*78}\n",
    "\n",
    "SOURCES:\n",
    "  - IEA: iea.org/reports/energy-and-ai\n",
    "  - Goldman Sachs: 165% datacenter power increase by 2030\n",
    "  - S&P Global: Datacenter demand to double by 2030\n",
    "  - Microsoft CEO: \"Power is the biggest constraint\"\n",
    "\n",
    "{'='*78}\n",
    "\n",
    "   TRY IT FREE: pip install quarterbit\n",
    "\n",
    "{'='*78}\n",
    "   Clouthier Simulation Labs | quarterbit.dev | Y Combinator Spring 2026\n",
    "{'='*78}\n",
    "\"\"\")"
   ],
   "id": "dekFzlBz1NzB"
  }
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