train_v6.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "id": "BHZnXywjxGeq"
   },
   "outputs": [],
   "source": [
    "#https://pytorch.org/tutorials/beginner/transformer_tutorial.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "id": "kY_j8RUDP5y4"
   },
   "outputs": [],
   "source": [
    "from torch import nn, Tensor\n",
    "import torch.nn.functional as F\n",
    "import torch\n",
    "import math\n",
    "from torch.autograd.variable import Variable\n",
    "import typing\n",
    "import random\n",
    "import tqdm\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "id": "y4lRdOSZNpe2"
   },
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "ph2WO5JpKukk",
    "outputId": "ab00d895-307a-4ade-e705-1c6dd8ae6926"
   },
   "outputs": [],
   "source": [
    "import os\n",
    "\n",
    "modelname=\"4_30_23_m1\"\n",
    "\n",
    "prefix_models=\"models/\"+modelname+\"/\"\n",
    "\n",
    "if not os.path.exists(prefix_models):\n",
    "    os.makedirs(prefix_models)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "id": "Oe3wtt5oADl6"
   },
   "outputs": [],
   "source": [
    "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "id": "xGOoJdNdRSaO"
   },
   "outputs": [],
   "source": [
    "class PositionalEncoding(nn.Module):\n",
    "    def __init__(self, d_model: int, dropout: float = 0.1, max_len: int = 5000):\n",
    "        super().__init__()\n",
    "        self.dropout = nn.Dropout(p=dropout)\n",
    "\n",
    "        position = torch.arange(max_len).unsqueeze(1)\n",
    "        div_term = torch.exp(torch.arange(0, d_model, 2) * (-math.log(10000.0) / d_model))\n",
    "        pe = torch.zeros(max_len, 1, d_model)\n",
    "        pe[:, 0, 0::2] = torch.sin(position * div_term)\n",
    "        pe[:, 0, 1::2] = torch.cos(position * div_term)\n",
    "        self.register_buffer('pe', pe)\n",
    "\n",
    "    def forward(self, x: Tensor) -> Tensor:\n",
    "        \"\"\"\n",
    "        Arguments:\n",
    "            x: Tensor, shape ``[seq_len, batch_size, embedding_dim]``\n",
    "        \"\"\"\n",
    "        #mine is [batch, seq, embed]\n",
    "        x = x.permute((1,0,2))\n",
    "        x = x + self.pe[:x.size(0)]\n",
    "        dropout = self.dropout(x)\n",
    "        return dropout.permute((1,0,2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "id": "XakHZ4HfFdYM"
   },
   "outputs": [],
   "source": [
    "class ScaledDotProductAttention(nn.Module):\n",
    "    def forward(self, query, key, value, mask = None):\n",
    "        key_tp = key.transpose(-2, -1)\n",
    "\n",
    "        scores = query.matmul(key_tp) / math.sqrt(query.size()[-1])\n",
    "\n",
    "        if mask is not None:\n",
    "            scores = scores.masked_fill(mask == 0, float(\"-inf\"))\n",
    "            \n",
    "        attention = F.softmax(scores, dim = -1)\n",
    "\n",
    "        return attention.matmul(value)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "id": "G-sb6gd7L5bR"
   },
   "outputs": [],
   "source": [
    "class MultiHeadAttention(nn.Module):\n",
    "    def __init__(self,\n",
    "                 in_features,\n",
    "                 head_num,\n",
    "                 bias=True,\n",
    "                 activation=F.relu):\n",
    "        super(MultiHeadAttention, self).__init__()\n",
    "        if in_features % head_num != 0:\n",
    "            raise ValueError('`in_features`({}) should be divisible by \\\n",
    "                `head_num`({})'.format(in_features, head_num))\n",
    "        self.in_features = in_features\n",
    "        self.head_num = head_num\n",
    "        self.activation = activation\n",
    "        self.bias = bias\n",
    "        self.linear_q = nn.Linear(in_features, in_features, bias)\n",
    "        self.linear_k = nn.Linear(in_features, in_features, bias)\n",
    "        self.linear_v = nn.Linear(in_features, in_features, bias)\n",
    "        self.linear_o = nn.Linear(in_features, in_features, bias)\n",
    "\n",
    "    def forward(self, q, k, v, mask=None):\n",
    "        q, k, v = self.linear_q(q), self.linear_k(k), self.linear_v(v)\n",
    "        if self.activation is not None:\n",
    "            q = self.activation(q)\n",
    "            k = self.activation(k)\n",
    "            v = self.activation(v)\n",
    "\n",
    "        q = self._reshape_to_batches(q)\n",
    "        k = self._reshape_to_batches(k)\n",
    "        v = self._reshape_to_batches(v)\n",
    "        \n",
    "        if mask is not None:\n",
    "            mask = mask.repeat(self.head_num, 1, 1)   \n",
    "        \n",
    "        y = ScaledDotProductAttention()(q, k, v, mask)        \n",
    "        \n",
    "        y = self._reshape_from_batches(y)      \n",
    "\n",
    "        y = self.linear_o(y)\n",
    "        if self.activation is not None:\n",
    "            y = self.activation(y)\n",
    "        return y\n",
    "\n",
    "    @staticmethod\n",
    "    def gen_causal_mask(x):\n",
    "        batch_size, seq_len, _ = x.size()\n",
    "        return torch.tril(torch.ones(seq_len, seq_len)).view(1, seq_len, seq_len).repeat(batch_size, 1, 1)\n",
    "\n",
    "    def _reshape_to_batches(self, x):\n",
    "        batch_size, seq_len, in_feature = x.size()\n",
    "        sub_dim = in_feature // self.head_num\n",
    "        return x.reshape(batch_size, seq_len, self.head_num, sub_dim)\\\n",
    "                .permute(0, 2, 1, 3)\\\n",
    "                .reshape(batch_size * self.head_num, seq_len, sub_dim)\n",
    "\n",
    "    def _reshape_from_batches(self, x):\n",
    "        batch_size, seq_len, in_feature = x.size()\n",
    "        batch_size //= self.head_num\n",
    "        out_dim = in_feature * self.head_num\n",
    "        return x.reshape(batch_size, self.head_num, seq_len, in_feature)\\\n",
    "                .permute(0, 2, 1, 3)\\\n",
    "                .reshape(batch_size, seq_len, out_dim)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "id": "NDpTASiaTxTX"
   },
   "outputs": [],
   "source": [
    "class Transformer(nn.Module):\n",
    "    def __init__(self, \n",
    "                 embedding_dim: int,\n",
    "                 n_self_heads: int,\n",
    "                 n_features: int,\n",
    "                 n_layers: int,\n",
    "                 n_classes: int):\n",
    "        super().__init__()\n",
    "\n",
    "        #Embedding layer\n",
    "        self.embedding = nn.Embedding(n_features, embedding_dim)\n",
    "        #Positional encoding\n",
    "        self.pos_encode = PositionalEncoding(embedding_dim)\n",
    "\n",
    "        self.decoder_layers = []\n",
    "\n",
    "        for _ in range(n_layers):\n",
    "            layer = []\n",
    "            #Add multihead, which will be cross or self attention\n",
    "            layer.append(MultiHeadAttention(embedding_dim, n_self_heads)) #self attention first, masked\n",
    "            #Now add layer norm\n",
    "            layer.append(nn.LayerNorm(embedding_dim))\n",
    "            #Add a feed forward\n",
    "            layer.append(nn.Linear(embedding_dim, embedding_dim))\n",
    "            #Now add layer norm\n",
    "            layer.append(nn.LayerNorm(embedding_dim))\n",
    "\n",
    "            self.decoder_layers.append(nn.ModuleList(layer))\n",
    "        self.decoder_layers=nn.ModuleList(self.decoder_layers)\n",
    "\n",
    "        self.to_out = nn.Linear(embedding_dim, n_classes)\n",
    "            \n",
    "    def forward(self, x: torch.Tensor, calculate_loss: bool = False):\n",
    "        \"\"\"\n",
    "        Expect tensor of [batch_size, n_features]\n",
    "        \"\"\"\n",
    "        if calculate_loss:\n",
    "            #If give model that accepts ?x?x4 abcd, expect bcd0\n",
    "            \n",
    "            target_logits=torch.cat([x[:,1:], torch.zeros((x.shape[0],1)).to(device)], dim=-1) ## if x is abcd, then target_logits is bcd0\n",
    "\n",
    "        x=x.long().to(device)\n",
    "        embed = self.embedding(x)\n",
    "        pos_encode = self.pos_encode(embed)\n",
    "\n",
    "        res = embed+pos_encode\n",
    "        \n",
    "        for decoder_layer in self.decoder_layers:\n",
    "            d_self_attention = decoder_layer[0]\n",
    "            d_layer_norm_1 = decoder_layer[1]\n",
    "            d_ff = decoder_layer[2]\n",
    "            d_layer_norm_2 = decoder_layer[3]\n",
    "            \n",
    "            ## Run the decoder\n",
    "            #do masked self attention\n",
    "            mask = MultiHeadAttention.gen_causal_mask(res).to(device)\n",
    "            res = res + d_self_attention(res,res,res, mask = mask)\n",
    "            self_res = res\n",
    "            #layer norm\n",
    "            res = d_layer_norm_1(res)\n",
    "\n",
    "            #do ff\n",
    "            res = self_res + d_ff(res)\n",
    "            #layer norm\n",
    "            res = d_layer_norm_2(res)\n",
    "\n",
    "        out = self.to_out(res)\n",
    "        if calculate_loss:\n",
    "            loss = nn.functional.cross_entropy(out.permute(0, 2, 1), target_logits.long())\n",
    "            return out,loss\n",
    "        return out"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "id": "HqGGEpvCLbP8"
   },
   "outputs": [],
   "source": [
    "def tokenize_multi(text_seq: str, features: int, encoding = \"utf8\") -> torch.Tensor:\n",
    "    # tokenize the input text\n",
    "    sentences = []\n",
    "    for sentence in filter(lambda x: x!=\"\", text_seq.split(\"\\n\")):\n",
    "        base = list(bytes(sentence, \"utf8\"))\n",
    "        if len(base) < features:\n",
    "            base.extend([0] * (features - len(base)))\n",
    "        tensor = torch.Tensor(base)\n",
    "        tensor = tensor.unsqueeze(0)\n",
    "        sentences.append(tensor)\n",
    "\n",
    "    return torch.cat(sentences, dim = 0)\n",
    "\n",
    "def tokenize_single(sentence: str, features: int, encoding = \"utf8\") -> torch.Tensor:\n",
    "    base = list(bytes(sentence, \"utf8\"))\n",
    "    if len(base) < features:\n",
    "        base.extend([0] * (features - len(base)))\n",
    "    tensor = torch.Tensor(base)\n",
    "    return tensor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "id": "pB-Xcf4TkKNQ"
   },
   "outputs": [],
   "source": [
    "def generate(seed: str, cutoff: int = 1024) -> str:\n",
    "    output = torch.tensor([list(bytes(seed,\"utf8\"))]).to(device)\n",
    "    \n",
    "    res=output\n",
    "    last = -1\n",
    "    i=0\n",
    "    while last != 0 and i<cutoff:\n",
    "        res = model(output)\n",
    "        argmax=res.argmax(-1)\n",
    "        \n",
    "        out = list(output[0])\n",
    "        out.append(list(argmax.to(device)[0])[-1])\n",
    "        last = list(argmax.to(device)[0])[-1]\n",
    "        output = torch.tensor([out])\n",
    "        i+=1\n",
    "    \n",
    "    if last == 0:\n",
    "        return convert_to_str(output)\n",
    "    return convert_to_str(output)+\"<CUTOFF>\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "id": "R81mAag8mHEG"
   },
   "outputs": [],
   "source": [
    "class TextDataset(torch.utils.data.Dataset):\n",
    "    def __init__(self, data: typing.List[str], features):\n",
    "        self.raw_data = data\n",
    "        self.features = features\n",
    "        \n",
    "    def __len__(self):\n",
    "        return len(self.raw_data)\n",
    "\n",
    "    def __getitem__(self,index):\n",
    "        return tokenize_single(self.raw_data[index], self.features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "id": "xQidsLjVaFUi"
   },
   "outputs": [],
   "source": [
    "def convert_to_str(x: torch.Tensor) -> str:\n",
    "    #Expects [1, 256] tensor\n",
    "    bts = []\n",
    "    i=0\n",
    "    while len(bts)<x.shape[1] and x[0][i] != 0:\n",
    "        bts.append(int(x[0][i]))\n",
    "        i+=1\n",
    "    return bytes(bts).decode()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "id": "97rbDfS4gO8C"
   },
   "outputs": [],
   "source": [
    "n_features = 256 # No. of tokens\n",
    "n_pad = 512 # Max line length\n",
    "embedding_dim = 512\n",
    "batch_size = 48\n",
    "head_factor = 64\n",
    "assert embedding_dim%head_factor == 0\n",
    "head_size = embedding_dim//head_factor\n",
    "n_layers = 14"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "IvvenMeUKfaj",
    "outputId": "fb38b6f5-0eaf-4d89-83ad-bee41604b8ef"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "92590\n"
     ]
    }
   ],
   "source": [
    "path_to_data = \"data/reddit_scrape_v6.txt\"\n",
    "data_raw = open(path_to_data, encoding=\"utf-8\").read()\n",
    "\n",
    "data_split = list(filter(lambda x: x!=\"\", data_raw.split(\"\\0\")))\n",
    "random.shuffle(data_split)\n",
    "\n",
    "train_data = data_split[100:]\n",
    "print(len(train_data))\n",
    "val_data = data_split[:100]\n",
    "\n",
    "train_dataloader = TextDataset(train_data, n_pad)\n",
    "test_dataloader = TextDataset(train_data, n_pad)\n",
    "\n",
    "dataloader = torch.utils.data.DataLoader(train_dataloader, batch_size=batch_size)\n",
    "testloader = torch.utils.data.DataLoader(test_dataloader , batch_size=1)\n",
    "testloader_iter = iter(testloader)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "id": "GvibftHanb0A"
   },
   "outputs": [],
   "source": [
    "model = Transformer(embedding_dim, head_size, n_features, n_layers, 256)\n",
    "model=model.to(device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "PXqmUSK0M3hD",
    "outputId": "69ad8160-d138-4e98-99b6-bd8cb83753c1"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([1, 512])\n",
      "torch.Size([1, 512, 256])\n",
      "tensor([[159,  66,  49, 121,  46,   8, 121,  45,  97,   0, 206,  45,   0,  86,\n",
      "         151,  47,   0,  75, 169, 121,  47, 212, 152, 206,   0, 169,  91,   0,\n",
      "         218, 169,   0,  30,  24, 152, 225,  91,   0, 175, 169, 251,   0, 237,\n",
      "         139,   0,  97,  48,  86, 212,   3,  18, 248,  47, 206, 225,  47, 134,\n",
      "         212,  19, 177,   8,  86,  97,  86,  43,  86, 212, 127,  73,  43,  48,\n",
      "         225,  18, 169,  13,  97,  18, 161, 169, 218, 250, 212, 134, 207, 169,\n",
      "         225,  18, 166, 225, 225, 205, 248,  47, 206, 139, 237, 169, 196,   0,\n",
      "         134,   0, 245,  45, 248, 152,   8, 144, 174, 134, 169,  28,  18,  30,\n",
      "         195,  86, 250, 127, 146, 152,  97, 225, 117,  47, 156, 121, 229, 212,\n",
      "         254, 150, 254, 150, 150, 150, 150,  86, 254, 150,  86, 150, 150, 150,\n",
      "         150, 254, 150,  19,  19, 254, 254,  19, 150,  19, 150, 192, 150, 150,\n",
      "         254,  86,  86,  86, 150,  86,  86,  86,  86,  86,  86,  86,  86,  19,\n",
      "         254, 254,  19, 254, 192, 150,  19, 254,  19, 254, 254, 254, 254, 254,\n",
      "         254,  19,  86,  19,  86, 254,  86,  86, 254,  86,  86,  19,  86,  86,\n",
      "         192, 192, 192, 192,  19,  86,  19,  19, 254, 254,  86,  86,  19,  19,\n",
      "          19,  19,  19,  19,  19,  19,  86,  19,  86,  19,  19,  19,  86,  19,\n",
      "          19, 254, 150,  86, 254,  19, 254, 254, 254,  86, 192,  19,  19,  19,\n",
      "          19, 254,  19,  19, 150,  19,  19,  86,  86,  86,  86, 150, 150,  86,\n",
      "         150,  86,  86,  86,  86,  19,  86,  86,  86, 192,  86,  86,  86,  86,\n",
      "          86,  86, 192,  86,  86,  86, 150,  86, 150, 150, 150,  86, 150, 150,\n",
      "         150,  19, 150, 150, 150, 150, 150, 150,  86,  86,  86,  19,  86,  86,\n",
      "          86,  86,  86,  86, 150,  86, 254, 150, 150, 150,  86,  86, 150, 150,\n",
      "          86,  86, 150, 150, 150, 150,  86,  86, 150, 150, 150, 150,  86,  86,\n",
      "          86,  86, 150, 150, 150,  86,  86, 150, 150,  86, 150,  86,  86,  86,\n",
      "          86,  86,  86, 150,  86, 150,  86, 150, 150, 150, 150, 150, 150, 150,\n",
      "         150,  86,  86, 150, 150, 150,  86, 150,  86,  86,  86,  86,  86,  86,\n",
      "          86,  86, 150, 150, 150, 150,  86, 150,  86, 150, 150, 150, 150, 150,\n",
      "         150, 150, 150, 150, 150, 150,  86,  86,  86,  86, 150, 150, 150, 150,\n",
      "         150, 150, 150, 192, 150, 150, 150, 150, 150, 150, 150,  86, 150,  86,\n",
      "         150, 150, 150, 150, 150, 150, 150, 150, 150, 150, 150, 150, 150, 150,\n",
      "          19, 150, 192, 150, 150,  86, 150,  19, 150, 150, 150, 150,  19, 150,\n",
      "          19,  86, 150, 150,  19, 150, 150, 150, 150, 150, 150, 150,  86, 150,\n",
      "         150,  86, 150,  86, 150, 150, 150, 150, 150, 150, 150, 150, 150, 150,\n",
      "         150,  86, 150, 150, 150, 150, 150, 150, 150, 150,  86, 186, 150, 150,\n",
      "         192,  86,  86,  86,  86, 150,  86, 254, 254, 150,  86, 254,  86,  86,\n",
      "          86,  86, 254,  19, 254,  86,  86,  86, 254,  19,  19, 150,  86,  86,\n",
      "          86,  86,  86,  86, 150, 150,  86, 150]], device='cuda:0')\n"
     ]
    }
   ],
   "source": [
    "input=next(testloader_iter)\n",
    "input=input.to(device)\n",
    "res = model(input)\n",
    "print(input.shape)\n",
    "print(res.shape)\n",
    "print(res.argmax(-1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "id": "tZji9Va7-unn"
   },
   "outputs": [],
   "source": [
    "lr = 1e-5\n",
    "optimizer = torch.optim.Adam(model.parameters(), lr=lr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "id": "hUN0DJQo-vmf"
   },
   "outputs": [],
   "source": [
    "n_epochs = 40"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "GczR7J4u6L5Q",
    "outputId": "15259e58-4468-44ff-dcf3-6c8dfb86d6bc"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sun Apr 30 09:42:27 2023       \n",
      "+-----------------------------------------------------------------------------+\n",
      "| NVIDIA-SMI 515.65.01    Driver Version: 515.65.01    CUDA Version: 11.7     |\n",
      "|-------------------------------+----------------------+----------------------+\n",
      "| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |\n",
      "| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |\n",
      "|                               |                      |               MIG M. |\n",
      "|===============================+======================+======================|\n",
      "|   0  NVIDIA A10          On   | 00000000:06:00.0 Off |                    0 |\n",
      "|  0%   58C    P0    64W / 150W |   1397MiB / 23028MiB |      6%      Default |\n",
      "|                               |                      |                  N/A |\n",
      "+-------------------------------+----------------------+----------------------+\n",
      "                                                                               \n",
      "+-----------------------------------------------------------------------------+\n",
      "| Processes:                                                                  |\n",
      "|  GPU   GI   CI        PID   Type   Process name                  GPU Memory |\n",
      "|        ID   ID                                                   Usage      |\n",
      "|=============================================================================|\n",
      "|    0   N/A  N/A     66681      C   /usr/bin/python3                 1395MiB |\n",
      "+-----------------------------------------------------------------------------+\n"
     ]
    }
   ],
   "source": [
    "!nvidia-smi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cuda\n"
     ]
    }
   ],
   "source": [
    "print(device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "id": "yYr65bmbITE1"
   },
   "outputs": [],
   "source": [
    "prompts = [\n",
    "    \"Why does Earth orbit the Sun?\",\n",
    "    \"Hello world, \",\n",
    "    \"How to use ChatGPT?\",\n",
    "    \"My code does not work. What should I do?\",\n",
    "    \"Why is this code not working: `1+\\\"A\\\"`?\",\n",
    "    \"Why is Java better than Python?\",\n",
    "    \"Why is Python better than Java?\",\n",
    "    \"What is the purpose of the main() function in C?\",\n",
    "    \"What is coding?\",\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "id": "o85FrKyF-wg-"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 1929/1929 [27:32<00:00,  1.17it/s]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
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     "output_type": "display_data"
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Prompt 0: Why does Earth orbit the Sun?\n",
      "Model output: Why does Earth orbit the Sun? the t the the the the the the the the the the the the the the the an t t the the the the the the t the the the the the the the the the the the t the the the the the the the the the the are the the the t the the the t t t the the the t athe t the the the the the the the an the the the the the the the the the t the the t the the the the the t t t the t the the the t t the the the the athe the the the the the t the athe the the the t the are t the the t t the the t the the the the the t the the the the the the the the t the the the the the athe the the the the the the the the the the the are the the the the the the the the the athe the the the the the the the ane the the are the the the the athe the athe the the the the the the the the athe are athe the the the an the athe the an are the athe are the are are are are are athe the athe athe athe athe the the the the the t the the athe the t the the the the the the the the the the the the the the athe the the the the t the the the the the t the t t t t t t t t t t<CUTOFF>\n",
      "\n",
      "Prompt 1: Hello world, \n",
      "Model output: Hello world, the the an the the the the the the t are the the are the the the the the athe the the the athe the the the the the the the t t the are the the the an the athe t the the the the the the the the the are the the the the the an the t the the the the at the the the the the are the the the t the the the the the the the are the are the the the the t the are an t t the the t the the the the the the t the the the the the the the the the the t the the the the the t the the the the t the t t the the the the the t the the the the t t the the athe athe the the are are the the the the the the t the the athe are the are are the the the the the ane the the the are the athe the the ane the oure athe the the the athe the the the the the the the the the athe athe athe are athe athe athe athe athe the athe the an the athe an are are the t are are athe athe athe athe the athe athe athe the the the the the the the the athe t the the the the the the the athe the the the the the the are the the the the t t the the the the t the t t <CUTOFF>\n",
      "\n",
      "Prompt 2: How to use ChatGPT?\n",
      "Model output: How to use ChatGPT? t the the the t the the the t the the the the the t the the the the the the the the the are the the t the the the the the are the the the the the the t t the the the the the the the the t the the the are the the the the the the the the the the the the the the the the t the the the the the the the the the the the the t the the the the the the the the t the the the are the the t the the the the the the the t t the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the are the the are are the the the the the the the the the the the the the the the the the the the the the the the the the the athe the the the the the the athe the athe athe the the the athe the the athe the the the the the are the the are are are are athe athe are the the athe the the the the the the the the the the the t the the the the the the the the the the the the the the are the the the the the the the the the the the t t t th<CUTOFF>\n",
      "\n",
      "Prompt 3: My code does not work. What should I do?\n",
      "Model output: My code does not work. What should I do? t the t t the the the the t the the the the t the the the the the the the t t t athe the the the t the the the the the the the the the the the the the the the t the t the the the the the the t the the the the the the the the the the the the the t the the t the the the the the the the t the the the the t the the the the the the t the t the the are the the the the the the the the t the the t the t the the t the the the the the the the the the the the the the the the the the the the the t the the the the the the the the the the the the the the the the the the the the the the are are the the the the the the the the the the the the the the the the the the the the athe the the the the the the the t the the the the athe the the are athe the the the the the the the athe athe the the are are the are are are the are athe athe the the the the athe the the the the the the the athe the the the the the the the the the the the the the the the the the the the the the the the the the the the t t t t t t t the t t t t t t t t<CUTOFF>\n",
      "\n",
      "Prompt 4: Why is this code not working: `1+\"A\"`?\n",
      "Model output: Why is this code not working: `1+\"A\"`?\n",
      "I t the the t the the the t the the the the the the the the the the the t the t the the the t the the the the the the the the the the the the t the the the the the the the the the the the the the the the the t the the the the the the the the the the the the t the the the the the the the the the the the the the the the the the the the t t the the the the t t the the the the the the the the the t the t the the the the the the t the the t the the the the the the the the the the the the the t the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the athe the the the the the the the athe the the athe the the the the the the the the the the the the the the the the the are the the the the the the athe the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the t the the t t t the t the t t t t<CUTOFF>\n",
      "\n",
      "Prompt 5: Why is Java better than Python?\n",
      "Model output: Why is Java better than Python? t t the the t the t the the an the the the the the t the the the t the the an t the the athe the the the an t are athe the the t the the the t athe the the the the the the the the the t the the the the t the the the the the the athe t the the t the the t t athe the t an the the the the the the the the the t t the t t t the the the the t t t t the t t an the t the t the t the the the t athe the the are t t t the athe the the t the the the t the the the t the the the the the the the the the the t the the ane t ane the the are the are the the the the the the the the the athe the ane are ane athe an the the athe athe the the the the the athe athe the the the the athe the the the the the athe athe the t the athe athe the athe athe athe athe athe athe the athe athe the athe the an the the are are are athe are are athe the athe the athe athe athe athe the t the the the the the the the the the the the the the the the athe the the an the the are the the the the t the the t the the t the the t t t the t t t t t t t t <CUTOFF>\n",
      "\n",
      "Prompt 6: Why is Python better than Java?\n",
      "Model output: Why is Python better than Java? t t the the t the the the athe the t t the an the the the the athe the the athe the t t the the the the the t t an are the the the the the the the the t the the the the the t the the an the the the t t t the athe the t the the the t the the t the the the the t the an ane the the the the t the the the t the the the an the the the the t t the the the are the athe t t the the t the the the the the the the t athe the at the are the the the the t the the t the are the the the t the the the the the the t the ane t the the athe athe the the the the the the the the the t the t are the are the ane are the the are the the athe the are the the the athe the the the the o athe the the the athe athe the the ane the athe the the athe athe athe athe athe athe the athe athe t the the the the the the the the are ane are are are are are the athe athe athe athe the t t the the the the the the the the t t the the there the the the the an the the the the the the the the the t the the the the the the the the t t t t t t t t t t t <CUTOFF>\n",
      "\n",
      "Prompt 7: What is the purpose of the main() function in C?\n",
      "Model output: What is the purpose of the main() function in C? t the the at an athe t an an an t the the the an an the the the the the an t the the the the t an athe the the an the athe t the an t t the the the the the the athe the the athe the an the t the the the the t the athe the the the the the the the an an the the the the the t t the the the t the athe the an t the the the t athe the t the the athe the the the t the t t t athe the the t are the the t athe the the t the t the t the the t t the the the the the at the t the t the the t the the the the t the at ane the the the the the the the the the the the the the the are are the the the the the the the the the an the the the an the the athe the the athe the athe the athe ane the the athe athe ano athe the the the the an athe athe athe the athe athe the athe are athe an the ane are athe are athe are are are athe the athe athe athe the t t the the the the the the the the the the the athe the the the the the the the the the ane the the the the the the the the the the the the t t t t t t t t t t t t t t t t t t t t t <CUTOFF>\n",
      "\n",
      "Prompt 8: What is coding?\n",
      "Model output: What is coding? an ang an ang the athe an athe an the an an the an the the athe the the the the the the an at athe the t the t the ane t the an an the the the the an an the the the the the the the t the an athe the an athe are the the t athe t athe t athe the ane the the t t the the the the the an an the the the the an t the the the the t the the t the t the the an t the the the the the athe t t the the t t the t athe the t the the the t at are the the an the the athe t t t the the t the ane t the the the the the the the the the the athe t t the an t the athe ane athe athe athe the the the the the t the the the are are the athe the the athe ane athe the the athe the athe ane the athe athe the the the athe athe the the the t athe athe athe the the athe athe athe ane athe are athe the athe athe an athe athe at the the an an athe ane ane are ane athe athe athe athe athe the athe the the the the the the the athe the the the the the the the the the an the the the the the the the the the the the the the the the the the t t t t t <CUTOFF>\n",
      "\n",
      "Epoch 2\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 1929/1929 [27:40<00:00,  1.16it/s]\n"
     ]
    },
    {
     "data": {
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i1JFbB0rLcdu7s0yvsWP6svP7a+TnalxvfI3T/tFNJ3hqSZk6buMEuVnRL7/uwgsumAETpieLP8WovZT4Zsv3v96Dmj8FpiiYuTUzFzFzEYD/AriDmf8XhCyWUgyrntCMNTvR8YkxidxPppcaNOp1O/aj69CxCWek/t4XzhvlV3M34su5G/HkFwsxbtEWDBttngE2lYqUed8YjZ3qDpdX2OrwWedz0jkaB7QUhem9AhqKGd3VrUGxupxwjbMrKSuvwN/HL9ddUc4OTU9adAtgXwmvJzhBT6C8DI/9AMBUAB2IaD0R3UxEtxHRbV7d0w7qiu829FtHZazfecDSeUadxa+KYogvpPtZJ47cTkPZuCtZrvhobczCzbj1nZkAYrMCO1hZsHbHf2bjn5NW2So3jp1+PD83vTIOK/uI65bvRKgU7LyrVs61Yz45cLgcb09Zk3E6mKD4bPYG/HX8Mrw6cYXmcbsLIt0wPS3dvAebdlt7h8NCUAOePK8KZuarbJx7g1dyWLi3JbOE1infLdmCI+vVQPP6NU3uoX/MWodi4SQVJw1z32FptX/6fPYG5Gl05FokObN1unKt77VGnXsOGs8K7b5fX8/biD+NWowzOzVJ+n72rzvx3tS1ePGK7vYKTJGlooIt+SiYGX8dvxwLN+zGhCVbUVhQPe0cdacZNtt9nPjsO0/nRyeiYy3K70aHGQ9/XzNsYMZleU3Qe6HLymyLMHNaI37l+5U4/+Ufza8F4+o3puE6JXRV7xyvcGMQ4vVIxk7x2p2JWYiVLXHw6GfzsWn3QexNUUA3jJyOz37ZgJIDDvbkUP7tO+w7HPv0WEtmlq17DuHlCcsxQdkRbr/GwshwqoZkKpdJ6EibMD35Io4u8egiu6SK7fb7Em8q2ejMjhR6z0crZYfWxVNWbsekZcVpZiFYbACZvD9LNqfv/2C3PDsWD8sOSStlaUjqpDNxqojdfC/jZe05VIaSg2WW6im1XZhdEtIJhSlx01Oq+HqLRr2aOV39L/9TgFgh6MealYpixda92HcoNjLb6NECpK/mbkx8XqzqqFOjpdQdYcnBUjzwyVzN8jJ5MeKO8kzKcyMDrhF6xVs1PTkt3ylWFY/xHifmv+PiVyebnuM04SEAzFu/C698r+038JP4T0itE7PIuSA9NklBBD5p6KBS0XjmowgzA16aiEa1q9m6puRAKXbuP6x5bP763SACujSvl/ju9x/8kvg8aZn2IsEPp/+KsYtiTmyG/ugpEz6bvd6VcrwwPamLtNPZOXkn7Xamemdb7RCsnGalpE0WBjJGv23ismIc27I+6tXM1zx+wfCYIvrd6W0tSOMdWhk8wr6Yzuj5Mbs7uxPTU0Bs36fd6euxc38pfquzdmDQ8J9wvsHmO7kqW4m6o3n4s/n4TrE9g/UbXiaOrF9SVktXymHt+pKDpXhj0irdVOlTVm5L2M/toh4d2Wn/VvJkpd/LGut37tdMRQK4P2p0MjN6aZzxmgV1iTv2Hcb1I6dbWvMyZWXMNr/7QCmKHh6FUfM22ZYtE+JKQV3HG1LNtBoEbZKxw6GycsdbDgQdpJC1isJPRqhCRo0et9GL4Vcz2b7vMLoO+TaxOO/Rz+bjT98sxn0fa5vErn4j2aa7dMselJU7WDmd4VDJPF2ItXL6Pvc9ev5xfEayWMWJr0XdRsYsiK1l0ftt8RT5q7aZL668+o2fsWn3gcRCzBE/2gtzzjTMVGtlthu78r76wwp8MD2WUaDo4VF49pvFePqrRVix1bhOVm/bh4k6lgAtrDzKDo+Pwc1vz7BcphZZt+AuW9Fb3MdgPPhf/eRuZgMKux2tUXF7DpXhxGcnADBeK7Jgg7apTMt5roWlFB5upQtx6sxWCVlewdh9wL1Vv5mOEm97T2OmkEGZcb+dHv+evFrXPHre342j/8yl8iYp4F/GLMUjn81PjORHTFqFkZNX46a3jDvs01/4IZFg0wl6re2Hpc5y1SWyQojpKTvYZ2UVuA+4Ea9uZG6zS6YvgOn1FspPUuIuzVCMClI/gstfn4KuQ5wt/LRC6va8Tnjqq0W6zmVL0X8GuLkyW4tLUoICMgkA0MTzZE+xf4JyZmedovDSQbbVQkqPXL0FR4a5Hszv7dXP8m0hsG7Uk/vFj56vbX+/6d/OzAJamz5Z8SupO8UZa3Zq7h1ul7LyCpzyl+8wVpVihRlp2/PaYee+w5bT1Thh/KItOKzkErMyc75qxLSkXSitMDdlJhRyP3kasuDOZ7xsIKN0OiA1l71ufYMgNW4PtKwUd+9Hc9wfeeng5Uhp/KIt+FrlnJ20XHv6r5Wi3YpUg9+d6UgutxaXqR/R9r2HsW7HATzxxQLX2sxxz4xDL8UU6TZTVm7DLe/MxF/GxPbKNpP5UFkFpq7anvh71/7DjmbpbgwYjWblVso/cNi6c1uinnzGaj0b5Q3S46mvFtm+Jo6ZXGYjCr3r5+jkr69sePp3/vyXDZ7OKJJ8FBne56OZ63SP3fLOTDzzdeWz2bmvVDdk2QlOt9/0MpBlS8khV2YBbueWSn1OO/fFTFZWIpwAJGYecS59bSrOePEH29mQrf6qMov73dsZ8cdntMc8OQZ3f/iLydnx8oMl6xSFXyNku3hlEpuv43CuvK/x9V6a6pyGx2bKmIWbcd3I6RltLbnfYlLFIJtbfI2EEaamHpdkiVO8Rzv02CqlZekd95aSQzjDpmnN6nNp+9ho10OFb//P7MTnr22WHdTakqxSFOUVbDtrahiwMppw2oDMrrIawZQpegrcyxfDyrRf7/YnmyReTO2At5QcTIt4u/292XADtcIdbnOVdbKY/ndCqfVk9rhLHYRea2FnwKiVUh9Irru032FTnu17D+G4p8fqRhIGnZolqxTFXR/8EkiueiuYNizXG0qswKBGKKuK9yZSq8fkAP4xYbnuixJ1btcIZT2kMTr2GzNdGbbV0aUWTUFmZPqr3I5e/HH5NuzcX4o3dNavUOJ9dfW2lskqRWHF2RwUZjmBzPSE3fYTX30cVDdwxosTcc9HcxJ/MxgvjluWFnLr5YpUAmGQSYivlfpRO6Uf/998Tf+Wpc2xBFPcUq6ZdLj/nbUenYd8i+Umi/acoCdXwqfo+h2tkVWKIsqYdZhOfC9LN+8JT5igXngsMzbsOoAHP5mbWGnsGqTvw4mLY2VErX42evuKBx3eqIUVZ7fT5mE5msdmub/usBYWu3hTieHxTGZKExbHZsLLt1aaZdPTjMf+vfbNn3Htm+YZaYNWBGaIoggJZg1k+RZjX4GTdr9976HAFvCkYiTFY5/Pxyez1qP946Nx6vPf+yYToJ/QUY2Vzs7uxOjH5cWWF8mZBiTofG8l5NVpfxrPSNv6kVG45l/Wt901u9+Bw9YGC09+scD4PgbH9LZrjbN9byxPnBXl/+PybY6j4tQEvTI7K7PHhhKTFvDHUYtdv+X+w+WhmVHopxlPZu12vT3FvaHEZOc8wF1HY0UFIyeHcO2bztNHpGIn0khd3+t27E/kSbJLfETPDExesR29WzdyVI5TMonm6/50sh8z9fHG19uoB1lutQFduZQbyMrsLCd15agaK23QienJaoinH+i9AF/M2eidMnOpXK2RZXlF8sjXqq/lz6PdHxBYJbWeB787C6/+sDIYYXRwq6P0uru1K6de+2Bm3PrOTExVsvuKM1vQxUrbcDrSDvuMwtN7uqcp0njii4WOivpizkbzk1S8O3WNo1+xXS+VuurzIY8HEiMmrdSN8tEjbYdIHUxTf7nw6L32O01btR0rtu7FuEVbMGPNTk/vZYaYniKMOjfQuSbZO7UghGcBopEcnk0oDAq24+y00l2YOVcTZdnse574YiGOKKhu65o9B0txvEYq9TP/Ogl3KhsYbd97yDBzsF1+0cgQ8Ow3S2yXY/UaszTidp7vvkNl2LT7AJrWq5n0fdIOdyAsVa05cvpaqS+7ckS6b0eingRdvBy3hENNABUGPkqvYvndKtWoc/dDD5fZTLNh5Hf5WEmx4YaSUK+J+c7h5lZO2X2gVDd9DWDvuUxYshV9/my8wBIAzv7bJN1jqbKktmnL77iszBb8hig8C6rUMwq38wvp4cdv37LnoK1dzZyYM+xeYVS/blU9M3Dpa1NsXzdm4WbbmWH1WGaQVcBJpt7ez07Awo2Vys9oZXYqF72SnE7FSLnPX78bw0Zrz5yq3IyCiEYS0VYi0oxTI6ILiWgeEc0hoplE1NcrWaLOZo9SPBMoNDOKf/1UaauevDLzcEIrGHWK+zRSh+th1Ln/sLQYL41bar0sB9NHN59hGAYOz39r3ySlxR8+1d8IzAmbSw7iNZVzP5NIpyFf6viwGLjo1cl4faJ2EEFVDI99C8BwAO/oHJ8A4EtmZiLqBuBjAB09lCeyZJpIzYgQ9AsAKheqAUjbSnVVsTsjzFTccmYfKC3HS+OW4WdV+ms1bm+pmSlGz7zcpQZBFHx+ojAzftGWpL+t1lVQ4bGeKQpmnkRERQbH1d6m2giPuTx0VM/z0EIYwlpPfWkOehV94+Jvf3nCct1jCzZYc2QDzlKW7Nh32Nb5RoEDdsxk03QUIwCMXrDZ23YbMuwGhfi2IZhLBPokiehiIloCYBSAmwzOG6yYp2YWF7u3j0BUcCtjZipEwY1QjEjtLL0amab+8suOb+HNjWxgdV+GTDB64nb6u7+PT1aOqQn7nOZl+mb+ZtPsvEGhVz2pHb95PepkSwYbziqzch0FM3/OzB0BXATgGYPzRjBzT2buWVhY6Ohecw0iIMKO1c1TnBAW05MavywWqSPx2tVyfbqzMSUZ7JNhBSM/hNWRcUUFJ+00BwAPfDI3I7nU+KEw48zU2NnQLnZnFHq5wJhNoujsCuYSoZgbMvMkAG2IqLFX91i309/UD27iVmrlVAihtDxh7CLt/P9u81CKs/PtqWt9ua8Zric/TMHI7GHF9LRi615NX4bdTXjs8t2SLeYnOcDO9sTqQZs6iMFupF7q2eqtTsOYQDIwRUFEbUmxMRBRDwDVAegbPTMkLycUOtERh700PYVwSqF2bAPezXq2eRgkkAlGtn93MFjcaKGu567bhZ370/0iXndvk1d4XS/mqOvn35NXJz6nmZ5MhmCpMxCrdVflop6I6AMApwFoTETrAQwBkA8AzPw6gEsBXEdEpQAOAPgNe9hrVcsLn5a2ilczCiCcMwq/COseES+NW+Zp+UZvmRUTyl/HL8P9GmYmuwv/7BK23SnVIdR2zVeGM5AQdlVeRj1dZXL8OQDPeXX/VKI8o7ATiWKXsKTwCAI7ayX8JDU82G0MTU8W2oObqT3s8P7PzjLZuone+7Jxd/Jap/d//hW3nNJGtxz9bMkmzmzJHustebkhVNOBQ6F0Zqey3Wb4p1W8VMCZ4GXwAmDc2UShPQTJ+MXWUpG8PtE42aG6mr+auxG3/6dy/3TDKL9sjHryk9wIr/7xyiK352Cp7gpQITi88knFmboyeFu/FXYf8Db6y0vM3ln18We/qUwtb7YmJig9njXZY6M8UPIqGmfolwtDa37JZrz0SQHA8O9WeFq+W6Tunx4lzPob9WRWPYSdsWYnaubrh2kHFXySNTMKmVKnI0oinHhtesrNie7sOiqYzwz0O6SgsxFrkUWKQjSFEA1KPfadaIW2Cu6zy6Cek2YUKZphfwgHcNmjKIIWQBAs4rXpyauUMEIyhmNTFzY28pPsURTybggRweu2Wi2LkvUFiXEqDmcPWUxPHpPN6wUEQU313Kx57QPFeKtdh2XKOgpvETUhCDFkTVHwRG3gmjWKImoPRhC8YmC3pkGLkBVYdVHYWeIlpiePkagnQYiRL6YnXzDqc6LWHWVNi6nwNpBEECLDvyevCVqErMCqLohC0ojsURRRU+GCIEQaq13Ouh3WkyzKymyPCWn+N0EQqiheRCjJOgqPER+FIAh+srXE/Y2xQu3MJqLaRJSjfG5PRBcQUb63ormLzCgEQfCTKCc1TMXqjGISgBpE1BzAWADXAnjLK6G84OS2jYIWQRAEISPCvuCOmHk/gEsAvMrMlwPo7J1Y7lO/VrWgRRAEQciIUJueABAR9QFwDYBRynf6SdMFQRAE1wm7M/seAI8A+JyZFxJRGwDfeyaVECgdjywIWgRBEEKEpR3umHkigIkAoDi1tzHzXV4Klk2cdHQjTInI9pSCIARHqE1PRPQ+EdUlotoAFgBYREQPeita9lC3RqQCyARBCIxwO7M7MXMJgIsAjAbQGrHIJ0EQBKGKY1VR5CvrJi4C8CUzl0Iyd2dEYUH1oEUQBCFihNr0BOCfANYAqA1gEhG1AlBidAERjSSirUS0QOf4NUQ0j4jmE9EUIupuR/Aoc8NJRYbHx9/Xzx9BBEGIFKFWFMz8MjM3Z+bzOMZaAKebXPYWgHMMjq8GcCozdwXwDIARVmSpCpzaodDwuKSBFgRBiz5HB7Nw2Kozux4RvUREM5X/X0RsdqELM08CsMPg+BRm3qn8OQ1AC6tCV3VaNTKsWs+RtFiCEE76tTceZHqF1aHrSAB7AFyh/F8C4N8uynEzYk5yTYhocFxJFRcXu3jbgJCOWBAEBwS1dYVVRXE0Mw9h5lXK/08BaOOGAER0OmKK4iG9c5h5BDP3ZOaehYXBaFS3yXTUXiM/HOap00zMaIIgRB+rvc0BIuob/4OITgZgfbcNHYioG4B/AbiQmbNmxZkbib1aNqjlgiSZUyBrQHylXk2p72wmqN3wrCqK2wC8QkRriGgNgOEAfpvJjYnoKACfAbiWmZdlUlbUcaI4wmK9yonANo5VifvPah+0CEKABOU/tJrCYy6A7kRUV/m7hIjuATBP7xoi+gDAaQAaE9F6AEMA5CvXvw7gSQCNALxKMTVZxsw9Hf8SIRCqgp44sm4NbC45GLQYlqAMhpREEqgQdYLa0tmSooijrM6Ocx+Avxmce5VJWbcAuMXO/asKzMC2ve7vfhUEmXRcYeGIutWjoyiCFkAIlKA2YMvEIypt1iFag4If/2C2LCWcVIVGwAyMvvuUoMWwRCZ6OacKKPVsJ6gtnTNRFDKJtYGZE7Jlw3A4p+0QVEy32zAYxzStG7QYlsiksxc1EX1COaMgoj1EVKLx/x4AzXySsUpw8XHNgxbBVTo0KcA7N50YtBhZh3T22U0oo56YuYCZ62r8X8DMtvwb2Y56ypg6KIgf+m2/NmjVKPiZhZUorGb1a3guR8Pa/mxfGyUHbyYzCjE9RZsLj22GJnW9f++0CMeqrSzAitP3kfOOwcQHrfkqgrJVxvnbb46LffCw7/HrN0ZJUWRU31muJ87pfGTQImTELX1dWePsCFEUKcx6fIDn90jtAKPUT8WpV8v7hV9e1cu5XZI7jCj5WmRW4Jyb+rY2PH6zyfGgCfLRi6JIoVEd2SeCQjL09GoEeMUJLZP+fvDsDp7cxwsyeTLZvjiyqLGxWXfAMU18kiR6iKLwiaBNRVHkyhOPcnTdHy/qYvncghp5yI1ID5qfSxmNKsMyAAiKIwqM7ftBLWaLAqIofIJ1Pgvu07B2NXxwa2+8XcWisggkpicPKQ8q9lTBbEYjpqcAqFUt19fy1IMVNwculxzXHF2aB7cGwMtRaiYl9zm6EU418D08e3HXjO/hOxRsZ+EVYZnRlQc8o+h4ZEGg9zciaxXFlSfYN2tc00v/mngba16/pvZxj+YRzerXxPCrenhSdljo1qKerfOtdDvndY35P6KWgiTTXE+CPhUBzyjMnk+QpsOsVRROBjFGDzKuCI5v1UD7uKoNZrKHQ0GN5OUrbiigejXz8do13iubMzoe4XqZt57iLFLFbPBoNCiIs2bYQLRvUsfR/Z0i0bHeEbTpKczPJ2sVhZPRlRWNrleuug3WyHdu9ooXX6taXtK/mZCbQzi3a9PE317Nfpy+CEad+h2ntU37Ls/GnuNWnpfh9T6/3uKj8I6wO7PFRxERDGcUShvTf5H1G6Gd9hk3Pfxf76Pw4NkdcMsprTPu1jOJyLLTeL2wAWvdv3Z1Y0XcvkmBhTqzJqtXSlWPoDqL87s1NT/JIWGJCCyvCFiAEA8CslZROLH1Gl3BKedcdWJyrP6ZnfQjGpy8KHk5Ofjd6W1RPS9zp7zR3Xu3aZj43KZx7YzuozW1r5aXWRPUGtEXVNdfDLhm2EA0r18zUee6aj0cfVcShAxNTxl0RC1CsqOil/Qs0jYbu8W9AzLbdEpmFAFgVOeznzhTM4mf4YuWoik6NEmOYOjXTt8vccfpR+se88MGbtQpfji4T+LzmHv6ZXQfran9jEcHYPpj/TXPt/RiaJzTuZl5FFjdmvmokZ+Dxwd20jxuVVH4bXoKqrPIC0lkkpd4nUfpyHrGi3nDXMNZqyiMaFi7Gqpp2LlPadfY9Fq9jiPVJNW4Tizh3b+u64njWzXUugQAMObufnjx8u6V5SvFJK3L0OjV7LzXWtd/fsdJaXtkZDr6LytPv0+9WvlJC6HsZtnV6jhzLPz4/NwcLHnmXFx6fAvN42G1V5cps7KBXb0zBWkRlhDWqoxEPYUQs4av1VH0P6aJ7ug3bqvWKvbr3/dN67y6t6hvSc6cHMKlx7cwjBjS6tJaNcrMTHTcUQ1c3yPDSuf7/GXdXL2n2zxybkfL51bPULGmwgBKFUN6Xq67plMzwjSjeOXqqh0OHkZEUeigF/WiF2Vk5Mzu0tzeOgAraM0C1D4EO6+1X2NnK+GHWhFLRg5j9sgBqXfHG04uSvo7nhzxomPTt2fxwuZdWhaTLN9GZJdVGhmkdc/VUEwf3NrbdRnMaF6/Jga64FjPZC+V6/u0cnSd2YzA9Lj4KPzHyYzCiPjZOTnJf7tNXBHpifeCykxlGZ80hYblKWPMnlNthyvw9cpNfZnjJspLeqSbsNy2XtWtkYfDyowiUzOgFg+doz9bSp1RtGxYE32ObuTKfe1UU1eXBl1dmtfTNC8L2mRtTeWaqGfbiiJxvrdqX6t0tajHHVXfVnkdjyzA806Ui4E80x/tj6mPnJH2vdsrXzs0KUhbgJhKzyJ9/48hOqKmNhujZmTUhuxYctodEQto+PuVxyVMT/mOVowaHzaaudWv6c8mUma4NapuWLsa6tb0ee81Mx+E2fMJ0G2WtYrCzOGp16eZtdMbTy7CEQXVcZ5FZ6PTZ6/5Ujt4icbc0w/npOzPkGmDPKJuDTStl57KJLVOzIIDtKbii54+O/H523v72VpcZwe9KrCz4M2oHp2EqhYWVE8EBHhhejKSN6g9xV+4vDv+c0uvxN9uLjhsUMua8vMrcCA8XqB0slZRmLU3p6anowvrYPpjA0xD7ey29zA3IqvcdmobLHyqsqN3Eo5odyX6dQ7tyXprW2z5fpQitBSi0+d5OOHMtv/qGt1z+NXHGQ5avLSPGypUACe3bYwbTiqq/MIGqZtUOeGVa3pg3L2VoeFO16NE+R3OWkVhht1FcPHTvWoMCcNWPDxWR7yQRnUCiL1gtav7O93vf0wTrBk20PZ1nZtp28L1+gitao8PNu7q385yOWZlx01P1RxEPelxyXHNcX63dGe8mlSfXrydGS0iO7F1Q4y8oWfG8gHOM6sa1bOdZ9CuSQGGDNJec+MWpqanADco8ExRENFIItpKRAt0jnckoqlEdIiIHvBKDl35TLr0k442XzOh5r2be+H8bk2THvaZnZpg2CVdnYhngOLMTvrOHTOUE/yIxAhC+V3bpxXuGaDVwVv/wfGUJVpWTicx8eorariQJj++PidevUb1rBf8cfeAdrhFZwvRoxrWwhkdM9s1Lh4cEq92u7Vm9XnZXb+TbXg5o3gLwDkGx3cAuAvACx7K4Jhreh2VNN2Mo2cj7duuMYZf3SOpYb5xXU/Hu7SlYtTc4yYcsz1/3Y7rByo7l4dtrC+IE+apeA4R7hnQPqMQ0LjzXrPNOPzxt57SBjecVIQbT2qNz+44KblIU2ep8QlGI1Yjl95jA4+xfY1V4gp12Za9AIDV2/bZut7Ip6FW1naDQLzA9PlURWc2M09CTBnoHd/KzDMAlHolgxVu7tsaJ2mE+RER2jUpwDd3nZL0fc1quXjyfG+noFZQN5qCGvlYM2wgrunlzB7vBg1q6edXigr/uOq4xOf4O9ugtvHvim9YpbUgLR4QoTUad7pyvnb1PAy9oDNqVstFj6OS12kYrYPQws5sMLXDVbc/IsKnt5+EOtXzkkxEbq4kLlLWCBXvOWR4XtN6yX4vqxL40QlHbe8TNZHwURDRYCKaSUQzi4uLXSoz9m9eDuHktvpmpk4aeYNuMhm528GuLyRsbc0tebo2r5emsP3+rYO6V9rpjW799k0n4qUrYmabP1/SDfcMaIc+bdIHG+UGMwqrnegdp1XmATOvD3sVVunvYuVf/XPN1h0d36oBFjx1dlI+sBwXepf4fbsrm1c1NFCGMx4bkLarYfw3aoVRq+vTynsYtP+vSs4o3ISZRzBzT2buWVjofNMfPYLpfDO7qb6ZIP179UjZCK+ddUZ89fu+eN/Dlb7f3X8qJj14uvmJCqmjvw5NChJO8VPbFyYW2DWsXQ33DGivGW4dd2YbdbJGCwKHDuqEPxgsgkuX2fKpKKiRl1BWCR+FwfnOwlIzf7HiodtGC03vHdAeX/++LwoLqqevc1H+NQsnzqQPfu5Sa35Is9oI2yBQTSQURdhJne6GAXWbi4/CzBqiXQd+prj1Ymilz0ilTWEdHNXIeu6qnMRo26lUlbsdasXrx3/75T1bph2Lc8PJsZnrkEGdcVTDWqapvr3siKwkWky7xoXnG+/g48pWa4BUs1qOKk2O9k293PPCzjpSIz/hUSa51apk1FOYeULlY2Bkbkv1wxGW+pLrtftCJRPrFTod0EeDe6ftleEGXk6Lzcr2wvbrRplDBnXG2Hv74cgMBxL92hdi0h9ON90Z0dZWs6o6TdSvQUWbZTLQws3HYjSjcGMRXibtV+tavcWkS/94rm45A7s2xUeD/c+fZQUvw2M/ADAVQAciWk9ENxPRbUR0m3L8SCJaD+A+AI8r53i+/LNaXg5u7ts6keclP5cCnfJZbZ+VSQeNr6tXMx8rnz0Pg/u10dxoqFebRvjzJe5laHWiZK1O1aNOtbwctG+iHf8frzW3RrpTHj4Dz1zUxfL5DI3Bh8H5TvwNZh34F7872XJZWun1te6TesucxEzEmEyegtZIv1uL9HU4VqLSemn4uhL3CdBH4dnqJ2a+yuT4ZgDamwF4xIKnzk44GK/pfRSK9x7C705vi3emrs2oXEcx8R4qp/g0vU6KA89MzqLGtXBM07qeR3XFzQl2dud79ZoemLB4K4CY03nvwcpgOT/0vFfPy613v1n99JQpqah/glpBWVlHYWfUPmRQJzz11aLE/X5+tD+Ygd5/npB0npFjOv3+cRnThTQycd11Rjt8NnuD5m9Tzxrj5TatVwMtG9TC/We1x29GTEs6X696tExPQTu+3cbnrFjBUke1Krh6Xm4iW2YHh6s+3cDyTmqpL4ONlmj1Fa+el4vRd59ifqJFTu9QiO+XpkepDereDCuL9+K2U/V39kvlvK5NE7mirDrn/ebKE1qiqHFtDBu9xPTcoEMltVqP0ezGjqKoraRZqVszFlqsl6rF3mZI+qYno3JSB0t6xJ3mUx+J7Tez71CZviQa7+LArk0xav6mxFdayiPMzmozstJHkcrpHY7AkEGd8O7NznPU2yXCbUYTM5OAmvzcHDx4dkcU1HB/7cVv+7XJ6Pqhgzo5XhMy7NJutpSfl/TVCPlOHkGnX9NJJ20JkN5ejZTKpce3wOMDj8HvTm9rKKMdRWFkelL/rtQSjUx88WNf/75vWqCA3RQrr1yTvJkSM+Onh6xH2YWdrJpRGHHjyRmsjfCx108NabR4kWeoX6gZjw1wJdLFvhCxf164vDsu09na1Co3nNw6EW0EeGdCaFynGvYeKtN9NHUyzIn1npJxtejhUabnxjvRE1s3xKDuzfDV3I1p59iZUeTmEG45xVxhuzXC1iqnX/tCNKiVn1Ai2grGoEyTl2buk2fhT98swscz12umz7+mVyvTKLU4d57eFm0KzXekrJI+CsEbaleP2fW9SMehx5d3noxDZdpbyakbb2GB8ebxdkhaDGXxGi9DIN3m/Vt748flxSiokY+3U3xk0x/rb8t/4wQGa5q/mqieYa/WDfHz6lhyhVRnthumszwbHvLKqCctH0W6M/vMY47AtX2KsHPfYd0yzRJsGlGvVj5qKlFoqZef1qHQVih2l+Z1cU4Xf/dAt4soiohxV/92WLdjPy7V2FHNDKfvdjeL+3u7xb9vPAFHN66DkoPWsrs4mmUFTLP6NfGbE7TzgB1R4P26HHXnqK43dRsZdmk3nP7CD8r3qSk8Mq9tW6Yn5V+raxZSsy0bNQ6tqCUr70pitmLVz6i7xsPi9QHaq8VHYYE3r++JoSmrlsfd2y+x0MvJ82tUJxbxUVNnZe74+07FWzeekPZ9zfxc3HlGO8827LGLF4339A5H2BqRRdlJGCRm1da6ce3EPhBebBuqpSg+vb2P5na+lT4K416VUpzeRiYku9GKqcoxXyfVu/rb11J8F1qc0Dp5F8Z4nb+YUg9iego5/Y9JT5XcrkkBzjimCf43J92ea4XHB3ZCp2b10E9nYU7bI+qgrbIFphpHU34fGphWIz6xdUNMWLIV53Y5EltNkrlpkW0KYMZjAzw1nyWFx1q85snzO+Ghczq6skf34wOPwR9HLU78rbWIr0vzeji+VUM88MncpO/j5iUzX0naYZN1R4B227XS9n7fvx0OlVXg6l76GaLPOKZyEaRemY3rJJtsh17QGUMv6Axmxv0p9RAUoigyIJOXunb1PFzb23621yjZ4Qf3a4PzujZFS5PUBEIMN308prDO5xRyckhz1uukFd5ySpskRaHlotAb5bdoUBO/7dcGV5xgLatA/D1JTXyYdK8MnNkAULdGPp6+MH2RY1PVmpZMsj6kmfsCNK6KonABf2Li7d8jnmMo30fHtxoi8ldJREeHesYHt/bWXciWHCDAmh2lX+s73r+1l6bDXu/2RIRHztPe9yLpPOVfTvnbCLcjou4/s3LXv6oyKxZFUYX562+Oxah5GxN7BHjRZv3Z4c7MLq2c54GmCHIU54Q+GnurVKLjTA3gN+oloHTSnNTXpCo6w/BYi2XaRW2mc/P1EB9FxAnroKFh7Wq4tk8R9liMHooqfiiroFdSu4MqbQc7N4uc0/lI3HmG8WI6p8Tr+cs7T8bm3Qd1zzu+VQPMWrtT93jqHvZGnay2Wco9k1FVIByhM4LgAhFy3wSOXlUZjrKVg89f3k2V1ts+qUEa8SgfoDJvU7cW9XFW5yN1y3j/1l44t4v+8TiGfbbhNqnukLRqPEMFEmTzlhlFBgTRMTm5ZWVMedUb6WQ7kx8+w3BRWTKZxfEnSsmgHU1/tD9qq1adxzeDemvKGltlV8/L1dy1Lk7qTzIyr9n2UZiISDqfo4woCheQ/jd6tnyrhH2W0rx+TTS3kDk2FWZtZ7YRhMxHtUfoJAjMBPXvSI1ySjWvfXtPP+w/XKYcMyozE9OTzmfHJcYIMuJRFIUQefxYmV3VxgJJ0bGqP+pr7MaXSljqQnP9g4l0Wpmiwz4YiBNkmLn4KDLAz1G0G7OWsLzgVonnAjLb2e3MTrEFkce2rO+6DPGFj0/a2E/8vZt74akLOls+/9IeLXBTJkkpLZIUHsva7eGWU2Jy/PPa4zWuj1oLqsTpojonqJWVW3W2ZtjAtIV5fiIzChcI++tTu1oe6tbIw5ODKjuv1/+vB9oekfk+HDGn5jq0bmSe/dIu7ZvUwb0D2uOKE4zzWg3o1AQr/nSuJ2lNauTnYvx9p9q6pm+7xuirs+JeixevSE9Z4TXPXtwVXZVd2C7p0TzxfX5uTsJvUBVwUxlEISeTV4iiyAJycwjzhp6d9J1b2SqvPvEonFDUUHfLz0wgItw9oJ2lc8OS+yoKdG9ZP5F2wolSCFtHaCXM17iP98cyELZ6s4MoigyIim3TS4jIEyVRVXn35hOxw3KUkrsU1qmO4j2H0CslCZ1VwtbPGUUrpb2bmv4MdyGy1ifUrpaLfYfLXb67t4iiyICOR9YFAJzaodC3e4pyijantPOvrQCxNQpfz9uIbXsPo0WDmnjlmh5o2cB+lJSaTPIXec21vVth1LxNGNQ9ltm5Wm4OmtWrgfvP6qB7je3wYLPjOifEU6uMuacflm7eg1vemWnvxgEiiiIDOjWri3lDz0JdD7b0TCUsr+a39/TDYZ1NjITwMfSCzujdphFue28WgFjqcKdEwXRS1Lg2pj3aP/F3Tg5hyiP9Nc9tWr8m8Osu02AJu+gp0njakpYNa0UuUaYoigzxQ0kA4cl3pxVeKGQH5MpKCg9wqMCGXdIVZ3VqktEq81QxQlg7riAeQEGo4pxQ1AA5BNzaz3wfayuEZWaRqRm2oEY+Ljy2ufmJ8fuZbZqkVExY6sdNZEYREapg2xN8olGd6lj156oT8hp2quK76tmMgohGEtFWIlqgc5yI6GUiWkFE84jIfM9AQRAEnxgyqBNG3dXX9nVum59u7uv9YkwzvJxRvAVgOIB3dI6fC6Cd8n8vAK8p/woa3NW/HeZv2I0+bYz2GxDCytmdm2B/xEIi0wjpUNkrsW60uVp+cL82eO2HlZpbvDolLIsfPVMUzDyJiIoMTrkQwDscy3Q1jYjqE1FTZt7klUxRpkvzepiqE70hhJ9/XtszaBEyJmx6ImyJKB86pyMeOqej5jErKdHDTJA+iuYA1qn+Xq98l6YoiGgwgMEAcNRR+huZC4LgPdm6liexGZJNjbngqbNRQ2M74sfOOwZLNu9xQTLviYQzm5lHABgBAD179szSZioIwVIVo3n8oE517W7WrSg0PwgyPHYDgJaqv1so3wmCIFgmyllto0KQM4ovAdxJRB8i5sTeXdX9E7/t18aTjVsEwU/C5hvwm2xUTJ4pCiL6AMBpABoT0XoAQwDkAwAzvw7gGwDnAVgBYD+AG72SJSw8ct4xQYsgCI7500Vd8ew3i1E9z92UF47Jbn3lK15GPV1lcpwB/M6r+wuC4C6XHt8Clx5vvDeIU+47sz2+nrfRk7Ld4uS2sVxN/TseEbAk/hMJZ7YgCFWbu/q3w139re09EhRdmtcLzboGv5FcT4IgRJrs8xj4jygKQRAiibgo/EMUhSAIgmCIKApBEATBEFEUgiBEmixc1uA7oigEQYgknK1JpwJAFIUgCIJgiCgKQRAEwRBRFIIgRBrxUXiPKApBECKJeCj8QxSFIAiCYIgoCkEQBMEQURSCIEQakmxPniOKQhCESCLLKPxDFIUgCIJgiCgKQRAEwRBRFIIgRBpZR+E9oigEQYgk4qLwD1EUgiAIgiGiKARBiCRicfIPURSCIEQSMT35hygKQRAiSfW8WPeVmyNzC6/xVFEQ0TlEtJSIVhDRwxrHWxHRBCKaR0Q/EFELL+URBKHq8MTATrjt1KNxTucjgxalyuOZoiCiXACvADgXQCcAVxFRp5TTXgDwDjN3A/A0gD97JY8gCFWLerXy8fC5HZGXK4YRr/Gyhk8EsIKZVzHzYQAfArgw5ZxOAL5TPn+vcVwQBEEIGC8VRXMA61R/r1e+UzMXwCXK54sBFBBRIw9lEgRBEGwS9JztAQCnEtEvAE4FsAFAeepJRDSYiGYS0czi4mK/ZRQEQchqvFQUGwC0VP3dQvkuATNvZOZLmPk4AI8p3+1KLYiZRzBzT2buWVhY6KHIgiAIQipeKooZANoRUWsiqgbgSgBfqk8gosZEFJfhEQAjPZRHEARBcIBnioKZywDcCeBbAIsBfMzMC4noaSK6QDntNABLiWgZgCYA/uSVPIIgCIIziCO2+0fPnj155syZQYshCIIQKYhoFjP3dHJt0M5sQRAEIeREbkZBRMUA1jq8vDGAbS6K4zZhli/MsgHhli/MsgHhli/MsgHhli9VtlbM7CgaKHKKIhOIaKbTqZcfhFm+MMsGhFu+MMsGhFu+MMsGhFs+N2UT05MgCIJgiCgKQRAEwZBsUxQjghbAhDDLF2bZgHDLF2bZgHDLF2bZgHDL55psWeWjEARBEOyTbTMKQRAEwSaiKARBEARDskZRmO2258P9WxLR90S0iIgWEtHdyvdDiWgDEc1R/j9Pdc0jirxLiehsH2RcQ0TzFTlmKt81JKJxRLRc+beB8j0R0cuKfPOIqIeHcnVQ1c8cIiohonuCrDsiGklEW4logeo723VFRNcr5y8nous9lO15Ilqi3P9zIqqvfF9ERAdUdfi66prjlfawQpHflT1HdeSz/Sy9eKd1ZPtIJdcaIpqjfO9r3Rn0Id63O2au8v8DyAWwEkAbANUQ2wejk88yNAXQQ/lcAGAZYhs3DQXwgMb5nRQ5qwNorcif67GMawA0TvnuLwAeVj4/DOA55fN5AEYDIAC9Afzs47PcDKBVkHUHoB+AHgAWOK0rAA0BrFL+baB8buCRbGcByFM+P6eSrUh9Xko50xV5SZH/XA/rztaz9Oqd1pIt5fiLAJ4Mou4M+hDP2122zCis7LbnKcy8iZlnK5/3IJYoMXUjJzUXAviQmQ8x82oAKxD7HX5zIYC3lc9vA7hI9f07HGMagPpE1NQHefoDWMnMRqvzPa87Zp4EYIfGfe3U1dkAxjHzDmbeCWAcgHO8kI2Zx3IsUScATEMs7b8uinx1mXkax3qXd1S/x3X5DNB7lp6800ayKbOCKwB8YFSGV3Vn0Id43u6yRVFY2W3PN4ioCMBxAH5WvrpTmRqOjE8bEYzMDGAsEc0iosHKd02YeZPyeTNiWX6Dkg+IpatXv6hhqTvAfl0FJedNiI0047Qmol+IaCIRnaJ811yRx0/Z7DzLIOruFABbmHm56rtA6i6lD/G83WWLoggNRFQHwKcA7mHmEgCvATgawLEANiE2tQ2KvszcA8C5AH5HRP3UB5XRUWDx1BTb1+QCAJ8oX4Wp7pIIuq70IKLHAJQB+I/y1SYAR3Fs87D7ALxPRHUDEC20z1LFVUgepARSdxp9SAKv2l22KArT3fb8gIjyEXvA/2HmzwCAmbcwczkzVwB4A5UmEt9lZuYNyr9bAXyuyLIlblJS/t0alHyIKbDZzLxFkTM0dadgt658lZOIbgBwPoBrlA4Fiklnu/J5FmJ2//aKHGrzlKeyOXiWftddHoBLAHykktn3utPqQ+BDu8sWRWG6257XKPbNNwEsZuaXVN+r7foXA4hHW3wJ4Eoiqk5ErQG0Q8xB5pV8tYmoIP4ZMefnAkWOeFTE9QC+UMl3nRJZ0RvAbtX01yuSRnRhqTsVduvqWwBnEVEDxdRylvKd6xDROQD+AOACZt6v+r6QiHKVz20Qq6tVinwlRNRbabvXqX6PF/LZfZZ+v9MDACxh5oRJye+60+tD4Ee7y9QTH5X/EYsAWIaY1n8sgPv3RWxKOA/AHOX/8wC8C2C+8v2XAJqqrnlMkXcpXIo4MZCvDWKRI3MBLIzXEYBGACYAWA5gPICGyvcE4BVFvvkAenosX20A2wHUU30XWN0hprA2AShFzMZ7s5O6QsxfsEL5/0YPZVuBmF063vZeV869VHnecwDMBjBIVU5PxDrslQCGQ8nk4JF8tp+lF++0lmzK928BuC3lXF/rDvp9iOftTlJ4CIIgCIZki+lJEARBcIgoCkEQBMEQURSCIAiCIaIoBEEQBENEUQiCIAiGiKIQBA2IqJxiGUHnEtFsIjrJ5Pz6RHSHhXJ/ICJXNrwXBL8QRSEI2hxg5mOZuTuARwD82eT8+gBMFYUgRBFRFIJgTl0AO4FYnh0imqDMMuYTUTxj6TAARyuzkOeVcx9SzplLRMNU5V1ORNOJaJkqkZwghJa8oAUQhJBSk2Ib1NRAbB+AM5TvDwK4mJlLiKgxgGlE9CVi+wB0YeZjAYCIzkUszXMvZt5PRA1VZecx84kU25xnCGLpIQQhtIiiEARtDqg6/T4A3iGiLoilRXhWyaxbgVh65iYa1w8A8G9W8ioxs3qPg3gyt1mIbX4jCKFGFIUgmMDMU5XZQyFiuXUKARzPzKVEtAaxWYcdDin/lkPeQSECiI9CEEwgoo6Ibb25HUA9AFsVJXE6YluyAsAexLanjDMOwI1EVEspQ216EoRIIaMZQdAm7qMAYuam65m5nIj+A+ArIpoPYCaAJQDAzNuJaDIRLQAwmpkfJKJjAcwkosMAvgHwqO+/QhBcQLLHCoIgCIaI6UkQBEEwRBSFIAiCYIgoCkEQBMEQURSCIAiCIaIoBEEQBENEUQiCIAiGiKIQBEEQDPl/oD2zAsD05qcAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Prompt 0: Why does Earth orbit the Sun?\n",
      "Model output: Why does Earth orbit the Sun?\n",
      "I st a the se t the t se the t the the the s are the an the an the the the se an an the the an the the the the the ane the the the the the an the an the the the an the the the the an the se the the the an the the the the the the the the the the the the s the the the s the the the the the the the an the the the the the the the the the the the the the s s s the the the the the the the the an s s the the the are the the the an the the the s the the the the the the the the the the the the the s s the the the the the the the the ane are the the the the the the the the the the the the the are are s the the the the the s the s s s s the the are s are s the s the the an the the s the are the the the the the the the the the the athe the an the an athe whe the an are the the the the are the the the s are s s are are the are are are the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the t the t the t<CUTOFF>\n",
      "\n",
      "Prompt 1: Hello world, \n",
      "Model output: Hello world, the the se an the an se the se the se the were the the the the an an an an the the the the an an s and the an ane the the the the an the the the the the se the the se the the an se the the the the the an the the are t the the the the the the the the an the the the the s the an the the the s s ane the the the the the the the the the the the s the the the the s the the the the the the the the the the the the the s the the are an are the the the whe s the the the the the the the the the the the the the the the the the the the s the the an the ane the the are the the the the the the the the s the ine the an are are the the the s s the the the s s the are are the s the s the the the the the the the are the are athe the the the the the the an the the an the the the an the the an the an an an an an are the are an an are s an the are are are are the the the are the athe the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the t<CUTOFF>\n",
      "\n",
      "Prompt 2: How to use ChatGPT?\n",
      "Model output: How to use ChatGPT?\n",
      "I s an an the the t the the the s the an the the an the the the the an the the the an se an the the an the the the se the the are the the the the the the the the se an the the the the an the the the the an s the the the the the an the the are the the the the the the the the the the s s ane the the the the the the the the the the the the the the the the the an the s the the the the the the the the the s s athe the the s the the the the whe the the the the the the the the the the the the the the the s s the the the the the the are the ane the ane the the the the the the the the the the the the the are s s the the the s s the the s the s s are the the are the the the s the the the the the the the the athe the the the the we the the the the whe are athe an are the an the an the are the are the the are are s are are are are the the are athe the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the t the the t<CUTOFF>\n",
      "\n",
      "Prompt 3: My code does not work. What should I do?\n",
      "Model output: My code does not work. What should I do? I I I I I I wo the t t wo the the t the in in the the an the an an an the an the an the at the an ane the the the an the the the the an the the at the the the the an the athe the an the the the an the the the the the the the the the the the the the the the the are s the the the the the the the the the the the the the the the the the the the the the the the the the the the the s the s ane athe the the the the the the the the s the the the the the the s the the the the the the the the the the the the the the the are ane the ane the the the the the the s the the the the the in ine are the the the the s the the s the s are in the athe the the s the s the the the the the are the the are the athe the the the the the the the athe athe athe athe the the athe the an the are an an are an are are ar the are are are are are athe the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the t t t t s the t the the the th<CUTOFF>\n",
      "\n",
      "Prompt 4: Why is this code not working: `1+\"A\"`?\n",
      "Model output: Why is this code not working: `1+\"A\"`?\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      " ithe the   in                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                 <CUTOFF>\n",
      "\n",
      "Prompt 5: Why is Java better than Python?\n",
      "Model output: Why is Java better than Python? I an an se the the an an the the an the whe the the the the se the an an the the an the the the se an the the the se the the the se the the the the the the whe the the the the the the the the the s the the the the the the the the s the the the the the s the the the s the are s the are are the the the the s the s an the an the the the the the the the the s the the the the the the the the the s s the the the s the an the an whe the the the the the the the the the the the the the the the the s s the the the the the s are the the the are the the the the the the the the the the the in in are the s the the the the the the s s s s are the the are are are the the an the the an the the are the an the the an the an the the the the the an an are whe the the the the the the the the an an the the are are the are the are are are are the the the the the athe the the the the the the the the the the the the the the the the the the the the the the the whe the the the the the the the the the the the the t the t the t the s the<CUTOFF>\n",
      "\n",
      "Prompt 6: Why is Python better than Java?\n",
      "Model output: Why is Python better than Java?\n",
      "I s the the the se t sous the an the the the the the at se the the the sere the the the an the the the the the se the the the the the the the the the the the se the the the an the the an the the the the the the the the the the the the the the s the the the the the s the se the the the are the the the the the the the the the the the the the s the the s se the the the the the the the the the s the the the the s the the the ane the the the the the the the the the the the the the the the the the s the the the se s the the the the the the the the the the the the the the the s the the are s the s the the the the s s s s the s se the are the the the the s the the the the the the the the the the the the athe the the the the the the whe the the are the the are are the the the the the the the the are we s are the are are are we the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the t the the the the<CUTOFF>\n",
      "\n",
      "Prompt 7: What is the purpose of the main() function in C?\n",
      "Model output: What is the purpose of the main() function in C?\n",
      "I a sthe st the an se the at the se an the the the the the ane at the the at the the an the the the the the ane the an an the the the the the an the the the the an an an the the the the an the the the the the the the the the the the s the the are the the s the the the are the the the the the the the the the the the the an the the the s are the athe the the the the the the the the the the are the athe the whe the the the the the the the the the the the the the the the the s the the s the are the the the the the are the athe the the the the the the the s the are the are s s s the the are the the are s the s s the the the the the s we the we the are the are the are the are the the the the the the the whe the the an an the the the the are are an the the the are the are are are the are the the are are the the the athe the the athe the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the t the the t t the the the the the the<CUTOFF>\n",
      "\n",
      "Prompt 8: What is coding?\n",
      "Model output: What is coding? the the se se the se the the se the the ar an an at the at sere the an the the an the the se an an the an the the at the the an an the the the an the an an the an the the the the the the an the the the the an the the the the the the the the the the the the the the the s the an the the the s are an the athe an an an the the the the the s the the an the the the the the the the the the the the the the the the an the the s an an the an the s are the the the the the the the the the the the the the the the the the the s the the the an an an the ane the ane the the the the the the the the the the the ane the are are the the are s the are the s the are the are the the the the s the s the are the the the are the are the the the an the the the the the an an are the athe the an an are the athe the the an the are the an are an are are are are are the athe the athe the the the the the the the the the the an the the the the the the the the the the the the the the the the the the the the the the the the the the the the the<CUTOFF>\n",
      "\n",
      "Epoch 3\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 1929/1929 [27:36<00:00,  1.16it/s]\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Prompt 0: Why does Earth orbit the Sun?\n",
      "Model output: Why does Earth orbit the Sun?\n",
      "I w the wat a see tee te teer tee teallll the the the st and tear tear the the st the the st tear the st and tear the the the the st an teare the the the steare the the the the st and tear tear the the the ste the the the the the st and the the the st steare the the st the ste the the the st the st the t steat t temer the the the the st the the the st the ste the t the t sthe t the te st ste te t the the t t t the mp the the st mpe t te the t the t ste t the t te te the t t the t t t the t st s t the s te we t in the t te te the the t the t t t the t we t the t t t t t s s sthe we t in w t the w t w the t s t the t t t the s t s the we t t t s t the t t w t t t the the t w w t t t t the t t t t t the t t w t t the w t the the we we t the t the w w t t t t t s t t t w t t t w  w  athe t  t r we w s t t   t the the we t the the the the t the the t t t t t t t t t t the t the t the t the t the t the t the the the t t the t t t  the s t the t the t t the t t t t t t t t t t t t t t t t t t t t t t t  t  t  t  t <CUTOFF>\n",
      "\n",
      "Prompt 1: Hello world, \n",
      "Model output: Hello world, have sel te st te fer the st a st fer tes the st a st fer tear the st and tee the st the st and tere the st steare the the st steare the the st and teare t teand the the st tear the te steant t the the st the steat t the the st the st teand teat the the the the st steat teat t tear te the te the the the st te ste tear t the the t the t at st te the t the t the t t ste t the st t te t t the t t me te t sthe t t mpp the t t the t t t the t the st the s s t t t the t the t t the t we t te t t t te t t te t the t t t t t s t the te t t we t t t te s sthe t the t we t t t t t t t t t t t t s t s t t we we t we t t the we t t t s t t t t t the s  s s t t t t t s t t s t we w  s w t  s  s   t  the  w w w t t t t   t t w t t t t t  t  t t       wathe wathe                              fffff  the                                               the  the                fff          the                                                                                                                                           <CUTOFF>\n",
      "\n",
      "Prompt 2: How to use ChatGPT?\n",
      "Model output: How to use ChatGPT?\n",
      "I wak to the the the ave the steat and tee the teas tean tear the the the the the the st and teat t ter te st tear tear the the st and tear te the the the the st and the the the the the st the st and and tear the the ste tear the the the ste the teand the the ste the the the st teand the the the the ste the st the mperent the the the the the the the s the the st the st at t me sthe t the t the t sthe s me the t the the s t sthe t mppe the the te t t the st the t t te te the the t t t te t the t the t s s t the the s s te at t t we we the the t t the the t t the we t the t t t s s t s the we we t t t we the t t t s s the we the the s s me t t t t t t t s s the s the w t t t the t t w t t w w t the t t t t the t t t the t t the s t w the the t the w s t w t t t t t t w we t t t t t t w w t w w mp t w w t t t t t the t t w the the the the t t the t the the t t t t t t t t the t t t the the the the t t t the the the the the t t t t t t t t w t w the t the the t w t t t t  t t  t t t t t t t t t the t t  t  t t t<CUTOFF>\n",
      "\n",
      "Prompt 3: My code does not work. What should I do?\n",
      "Model output: My code does not work. What should I do? I wh whe the see the see tee t the st to sear the the st and the the st and the te the t stear the tear the the st teare the the the st the the t steare t tear the the the the st tear the the the the the st teat t the st tear te the the the the t the the st te the st the t ste te the t the t the st te the t the st te the t the t the t the st the t the the t st the t we the t the t the the t the t t t the we t the the me t we t the t the the t t the t we t the t t te the t t t the the t we the t t t t the we t we we the the t the t t the the t the t t t t t t t s the t we we we the the w t we t t t the t the the t t t t t t t t t t the we t the s s t w t t t w t t w the the t t the t t t t t t t t w the t w the the w w w the w t t the w t t w t w w t the the w t t w w t the t t t r w t t we w t the  the the the the t t the t t the the t t t t t t the t the the the the t t the t the the the the the the t t t the t t  w w t the t t the t t t t t t t t t t t t t t t t t t t t t t t w w t w  t t t t t t t t t t t<CUTOFF>\n",
      "\n",
      "Prompt 4: Why is this code not working: `1+\"A\"`?\n",
      "Model output: Why is this code not working: `1+\"A\"`?\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "I would th the a the at st a ste tear the st is the the st and the the st the the stpperent the the st and the the the the st the the stere teat the ste the tpppperting the the the an the the the the the st the the steat the the the st and the the the the ste the the st the the the ste the the ste the t the st the st the te st the the t st mppeat the t the t the the st the the st se the the the s the st the st the te the t the t the the the t the w t the we the the t the t the t s s the t the t te the be the t the the t the the the t t the t t the t t t s s the the the we we w we the the t t t we the t the the s s the t t t t s t the we the the t s t s t t the t t the w the t t the the t the t the t the t the the the the w w t t w w t t w t t w the t we t the the t t the the t r w t t s w the the t the the the t t the t t the the the t the the t t the the the the t the t the t the the the the the the t t the t the s t the the the t t the t t t t t t t w the the t the t t t t t t w t t t t t t t t t the t<CUTOFF>\n",
      "\n",
      "Prompt 5: Why is Java better than Python?\n",
      "Model output: Why is Java better than Python?\n",
      "I wan the a stame tear and the the the an the the st and tear tear the the an tere the the st and tear the the st and the an the the the the st and tearer the the the st and the the the the st and the the the the st teare the the steat the st and the te the the st te st ffer the the the st mpeat the the st and te the t the st the the the t ste the the s the st the t ste the me the t sthe s the st mppeat t t the t the the t we the the t t t the t sthe the s at s t the t t the the t t st t s the t the t s we te t t the t we the the s the the t t the t the the t t t t t t s t the we we we s t s s the the t t s the s t s the t s me t t t t t t t s t we the we t t t t t the t w w t w the t t w t t t t t t t t the t t the the the the the w t the w w t t t t t t w t t t t t t w w t t t the t t s in w s   t t we w t the the the the t t t t t the t t t t t t t t t t t s the t the the t t the the t the the the the the the t t t t t  t t w t s  the t t t t t the t s t t t t t t the t t t t  t t t t      t       t t t t<CUTOFF>\n",
      "\n",
      "Prompt 6: Why is Python better than Java?\n",
      "Model output: Why is Python better than Java? I wand ter the wee the st and tee ter tear stear tear the st and tear the the st and tear the st the st and tere the the the stean t the st teare teand the the t steare the the st teand ter the the the the st and teat t the the te sterere the the st te the t the st steat t the the t ste st tean t the t the t te the t the st t the st the st t the t the mp the t the t st the t te the t the st t the t the s t t sthe t t t ste we t the t t t the t t t the t t t the t t t t t the t t t t st t the t t t t s t s we t int in t the t t the t the t we t t we w t t t t t t t t s t t t t t we in w w s s t t m t t t s t t t s t t s s s s t t t t t t t s t t w w  we s     w the t   w t t w w w  the  t   t t t t t t   t  t t t  the the the                 w                     wat                                                                                                                     ffffffffffffffffffffffffffff                                                                                                      <CUTOFF>\n",
      "\n",
      "Prompt 7: What is the purpose of the main() function in C?\n",
      "Model output: What is the purpose of the main() function in C?\n",
      "I and a te the stat a stand tear the the st the the st and a the the s the the st and the the the st and tear the the and the the the st the an t the the st the steat the st the the st the the the the the st teat st the the t the st meat the the st the the ste fffferer the the the the the st and te the t the the st the the the st the mperent the the the the the the t the st the sthe t the t ste the the mpp the the in t the t the the t t the we t the the t the the t the t t t t the st s the the t ine we w athe t the t the t t the t t we the t t the t the w the w w the w the w the t w the s the t t the the w the t t t t t t the the t t t w t t the the t t w the w w the the t t t t we the t t t t the t w the the w the the t w the w t w t w the t the t the t t t t t t w the in w r s t t athe t w t we athe the t t the the t the t t t t t t the t t the the the the t t the t t the the the the the the the t the t t t t the the the t t t the t t t t the t t the the t the t t t t t t t t t t t t t t t t t t t t t t t <CUTOFF>\n",
      "\n",
      "Prompt 8: What is coding?\n",
      "Model output: What is coding?\n",
      "I t stan tan t the st to the st stare and tear tear tear the and teat the steat the and the the the st and tear the the st and a the the the the st and and the the the the st the the st theave the st and tear the the the the st the the the st and the the the st the the st mpeat the the st ffear the the st tear the the st and the the the st the t the the st the t st meat t the t st te the t the t t ste the s t mpperer the t t the the the t the t at t t t the t the t t the t t me the the t te t t the the t s t s s the me t te t the t t we athe t the t the t t we the t the t t t t t the s s s the we t we t w m the the t t the t t t s s the s w t s t the t the s we t t t the t t s t t s t w w t t t the the t t t t the t t t the t t t w the t w w the w the w the w w t t t t t w the t w t t t w t  w t the at s t  t s t t t t  t we we the the the t t the t the the t t t t t t t t t the t t t t the t the the t the the the the the the t w t t t  t t w w t the the t t t t t t  t t t t t t t t the t t t t t t t t      <CUTOFF>\n",
      "\n",
      "Epoch 4\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 1929/1929 [27:30<00:00,  1.17it/s]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Prompt 0: Why does Earth orbit the Sun?\n",
      "Model output: Why does Earth orbit the Sun?\n",
      "It was tel teally tear teallly the teat spperend tear the teat tealll tear tear alll tear teall tear tear tealll the tear ar and tear the and tear and tear the and teat and teall the teat tear and teall the tear tear the teat the and and tear tear teak the the the sppeare tear teall the tear the tear tear all the to tear t pperstion the the t persing all t the t a skeall t the t the t the skeat t t the ap ar and t t ak t ak wallll t ak t ak w w the b t w t the w t ak w t t t t ak le w rk w at w a w w w w le le lle a t ak le t llle w t w a t w w w w w w w w w w t t w w w le t t w le le  w  le    w  w  w  w  w  t                                                                                                                                                                                                                                                                                                                                                                                                                     <CUTOFF>\n",
      "\n",
      "Prompt 1: Hello world, \n",
      "Model output: Hello world, dvent stal w tes fel teal stent tes teall tes tear seall tes tear teally ter spent ters tear teally tears tear tear spenting t ter the ppersing and tent ter teat temens the ter seall tens tear teat ter teatent ter teat ter teall tear teat tear teat tear teater teall the teat ter tear teat tear tear the te ppperstiont tenter the t tear t t teat t skeall ak t theat t t akeand t the t t at the t ak wat t ak t the t ak at w t ak t wak w wat t w w t wak w t at t w w t wak w t w w t wat w le w t t t t t w le w r as a w t w le t w le le wa le t le a t le le le w w w w w t w w w t t w  w t w w w le  t w w  w  w  w     w w w   w w     w  w                    w       w      w                                                                                                                                                                                                                                                                                                                                                              <CUTOFF>\n",
      "\n",
      "Prompt 2: How to use ChatGPT?\n",
      "Model output: How to use ChatGPT?\n",
      "In teally teally tear teallly teally the tear tealll tear teallly and tear teallly the and tear and teallly teat the the sppeare and and t a s a t tears a t skeallly the the tear s alll and t tear tear teall the the teat tear alll tear tear the seare tear tear the tear tear seare the and tear the and tear tear the the seare t and tear the the tear and tear the the sear t tear t ffear t and tear t the skeall ak and t a the t a t t ak wak wablll at ak t ab ab t abllle ab at at ak a t ak t t ablllll ab as a a a a a t a a a a a a a a a a and a a a a t a a w w t w w w t t w w t t w w w llle t w w w le a w w w w w w t t le t t w w lle he w rk a w a a t w w w llle w t le w w w le w w t w w llle w w w w t t w w w w w t w w w w w w t t w w a t w w h h w     h h h h    a  w w w  w w w w w w                                                       w                                                                                                                                                                                <CUTOFF>\n",
      "\n",
      "Prompt 3: My code does not work. What should I do?\n",
      "Model output: My code does not work. What should I do? I would th wh we ke w the dver? I w wek the w week the te tee ter teake teak the teak te teak the tear teake the teat ter teak the teat the ter tear teak ter teak the teat the tear tear teallly the tear tear teate tear the te teak teat the teak the the te teme the te tear the te teake teat the the teake t the te teake t teake the t the t the te t teak t the w the t the tee t t thee t t the w the t t w the the ake w w the t t te t w w theat w w the t t w t thee t t te t t the d w s w wat w w the w w we t akee w t w w thee t t the t w w w w w w w the the the w w w t the w w w w w the w w t w w the t w w w the w w w the  w t w w the w w w w the w w w w w w w the w w t w w w w the w t t w w w w t the t w w w the w w w w w  w w w he   w he he w   w the w  w w w the                            w      w  way  wa w w woule    the        he                                                                                                                                                                                     <CUTOFF>\n",
      "\n",
      "Prompt 4: Why is this code not working: `1+\"A\"`?\n",
      "Model output: Why is this code not working: `1+\"A\"`?\n",
      "\n",
      "It would th the pperse an teallly tear teally tear the sears and a t teall t teall tear the teat teat ske and the tear and tealll tear the teat tear teall tear the tear teat the tear and tear the tear tealll the teat tear teall tear tear the teat temer the teat temer the the tear tear teall the the temer the tear tear tear tear the teat ter the and the te the te temer the the the sear t tear t teall ake t the t t wake t ak t the t t wake w the t t at at ak w wak t t t w wak t le t wat t w as w lle ak ak ak w wat llllllllll ak at at w w w w w w w w w w the the w w t the w w w t w the w w w the w t t w w w the the w le t he ak t t t t w w w w w w w t w w the w t w t w w t w w w t w w the w t w t  w t w the  the w w the w w  t w he w w he      w he w   w  w w w  w     w                                                                                                                                                                                                                                                     <CUTOFF>\n",
      "\n",
      "Prompt 5: Why is Java better than Python?\n",
      "Model output: Why is Java better than Python?\n",
      "In't and the teallly tear teally temp the the and and tear the and tear alll tear the teat and tealll tear tealll the the tear teat tear and tear the teat the teat and teall tear the teat tear and tear teall the tear the teat and tear tear tealll tear the tear tear teall the and tear tear tear the tear teat tear the the teat the ppearser the t tear tear t ppear tear t the the at and tear t the t appp ack and t at a t a t a wa le t a t a a s wa lllle ak at a a a s a w t ak a a t t t a as a a a a a w lllllllllll a a a a a a a a a a a a a a s w w w w w w t a w w t t w w w lllllllllll a a w a a a w a a a w s t a t w a a t a a s a a a a a a a a a s w w w w w w w a t l w w w w w l w ll w w l l t w t w w w t w w w w w w t a w w t w w w w t w w  w h w w t w w t w w a t w w w  w                       w             w      wa      thou                                                                                                                                                                                           <CUTOFF>\n",
      "\n",
      "Prompt 6: Why is Python better than Java?\n",
      "Model output: Why is Python better than Java?\n",
      "I ava and a the and a tealll tear tealll the tear and tear and teall tear tealll and the teat ter and tear alll tear the teat ther and and and t the te spears tear the teat ther tear and teare the the tear and tear teall the teat the teat the tear sppeare tear tear tear the and tear tear teall the the teme the tear the ppersint tear the the tear t pperers tar and t a the sk tealle at t the t the ak at and t t wake t t ak w wak t ak w the t w the w t w the t at w at le le t w t t t ak le r as a w w llle a lle ak ak a at a a s a lllllle as a w w w t w w t w w w w t w w w w t le w w le a le lle w w a a t t le t r w  le t s   w    a  w  t  w w w t w w w  w w w   w     w    w  w     w    w         w w w     w     w  w                                                                                                                                                                                                                                                                                                             <CUTOFF>\n",
      "\n",
      "Prompt 7: What is the purpose of the main() function in C?\n",
      "Model output: What is the purpose of the main() function in C?\n",
      "I an a cke th a ppper teally and the and the the and a sker and the tear and tealll the teat tear the and and tealll the the the teat tear the teat teat tear the and tealll tear the the tear tear and tear the and tear teall te the teat temen the the teat tear and tear teall the teme the tement the the te the te ppearent tear the te the tear the te tear the ppear tear the te t the ak t the and t teak t ake w w the t ak t the w the t t at ak at w as w t the ak ak lllllllll at at at ak ak at ak ak a ak alllll at a a a a a a t w w w t wa w w t t w the w w w w the w w a a w t w w the w a w t t t the a w le w w le a t le w t t a w w w w t w w w t w w w w w w w w w w t w t w t w w w w the t w t the t w w   w w the w w w w he t w w he   he    w t w w  w w w w w w               lle                   wa wa    wathoulou            thou                                                                                                                                                                                           <CUTOFF>\n",
      "\n",
      "Prompt 8: What is coding?\n",
      "Model output: What is coding?\n",
      "I th sould the the some and a alllear the and the tear and tealll tear the and tear and tealll the the teat tear and tealll tear the the the teat tear all tear the teat the tear teat the tear and the and tear the the and tear and teall the teat the temer the teat the teat tear teall tear the tear teall teme the the tement the the tement the tear tear the tear the temer the the te the te temer the the the te teat t the t skeat t ak t the w the t the ak t t wat t the be t ak at t t wak w t t the t wak t at ar as t ak wak w w wat ak allllllllll at at ak at at w w w w w w t t w w t theak t w a w w w a wak w w w w w the the t w t w the he t w w the a t w w t w w w w w w w w w w w w w the w w w w w w t w w the w t t the w the w w w t t the w the w w he the t t w w he he w w w w t t t w t w       w   he w    w r          w      w      wa wa   w w  thoule     wathe      the the                                            w wathous                                                                                        <CUTOFF>\n",
      "\n",
      "Epoch 5\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 1929/1929 [27:31<00:00,  1.17it/s]\n"
     ]
    },
    {
     "data": {
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r9JnPzMD1//w+WKEsOFAaWzC4x8HKzuQK+drMIgx69At8kjRfo6e8ohLf/+h+3sAruUaawgavesJrc+TkOk5GN7sPxJ7ZE5P9h0cf9fQMDH/iK9/l6Hlv7o+BPPtqrzTfRfkingfFL0GrsarEZeL1lD4s2LAHP3txFmav3eXI/TGI3o+f9ydu1iHEzFBXvvIdZqwyTqq0c/9h3KcLjvbN6thCn3W7zH/n05+vwoXPf2sZs8lQLjb+bIeR6cnu/kShA/rJgs046bEvq3rPYfQSDyS50a7Yug9v+kile8d/FuHC5/172sWfj1Ozoqp1BUMfd65Iw3h+YYW9F68nhxg9nnhwvKLHRrs+1/A4Zrw03b87ZfJcRFVvmoB9peWYsWon5v+4B4seOMPw/DdmrUdBfi7uPOuYKiVn1dDGwyZsL3Zn1kh0OTaZozDYbDSiqGDGDf+cZ3ott+so1u7YH7g30V0fLsK+0nIcOFyOhgX5jpT/6HHuVhq7VYhnPB1b53LVCR3dnRgwceXpdLSoyvTkec2TYqUhpqc0wmsvxqpObysuQcexE/Hh9xsxt2i34foAIncV5dzxiesX4pdnBorj5ieb8v6uKSw3r811bxZamqis8Dui2H2wzLJhd+see9nLs/HE5BW+A7H5bc92HVC/gDIKVJgoclVBMFVjpMj2HDzs36FETE+pJZUa2mq4uEbLCf3vwo2uo7KasTJp1XK8cv173kZXQ2tAH57bWQ0d/6Uz18SYXLrJbJNjjJ5TjsI5irdmFWHKkq1VK529vphO6pebOljJwOIoBIT0yOHyStz78WLTyXQ2maMY8devDY8PO6RFMk7mm/o8ONU2RL9p+SH/XjE9BcCL09agXu08XDm4Q9W2hIixlq4v2vG2LZL3irLTwJXVaWlOTE81yvYgqpkpwWjz9JU151fs5KseUVgLd8/HMXfFBgXqXo31Ow+iXu1cV666O/aV4uxnv8G7YwYrkWn+hj3o066xkrIBYPKSrXhz1vrqEW0SlVofyXWIFb+CmfD1iu1YuHEvbjq1q+kxXt5Ir4ssqyazPZ3tn6wdUQTJY58uxz3/TUy7MeqZ6hhHjJjt08j+GX8xbNWEjw7FU1NXGpTnrkAV+RychkUJ5FoeL5A8Gvzb1JXoOHYiSsu9mxDOGf8NTnlymqeWZuPuml45ViPWop0HHMl6/nMzsa3YZWh3FxzSTC5mo8Eq05Nuv5Hcew+W4akpK1zNJfziH9/h/z5z5zV2zWtzDd8bpwRen+MhPMT0FC2cRj01I9n80++hqRj8ly9qHFelKJjxHxNfeXLQ91QdhMyo+Ldmu8sIZla2WflBUr3gzh+vzVwHACg57N9M6EUW/ZzJiVp9MqsdxSVlGP7Xrx0vKttfWm64fe+hMs8LLOPE1yvVzjMOoxwfgefqOjBGzhF/+XQZxn25Gj9qK7jt7uF/f9iEb1bvxAtfx6JAV1Qyrn9rHhYEFD4lTsQsYYEjpicTkqOert91ENNWbjc52ppv1+w0nBT9eP4mfLU8VmYlAx/+sMlT+V5wW7GN2vF7/rs4wdzmC+WKQvuQ8hfa/IfpR3WVlbFxgZ3Xz0P/q86uuFnLIWL2LA9qGRJnai7OXhnw8FSUVbCtd58VpVWKwrhvGn8+OTZd19KkBbJ21ebm9+YnfN+0+xA+W7IVS7b4n+/RP79UVauw3GOzckSxevs+096TFc995S3BTHK2tTh/eHc+/js/5iVkOSIge1OR2elFJl4jjucotIrp1PS0c3+p49GBvtKbh/AI5sVgByOK5CCQybhRrm5DhZw1bga63DXJ1Tl2uLWHmz23sgr/z+CwraJwNofkJ2T/8q3FKNbStFYG4zdiihN3cjeEHXomK0cUI5+ajmb1aoVy7aUmkWoDSFlhyCvfrPN1vluT0K4Dhz25dPo3PVkXUJ24yPyFS55nsr5asIsol2/1Z+o04nqLdSWppkJrmc1GTE7XufiZKxv19AzUiUAGsTOfmWE4Ovt29U4M7NQUebk1lSnJHEU4qPBPj5uRrDDzy/c7x+D2bKeT2VUvpoIKqv/JKibLq6/D1avTLX624bNR+GIGZde2u3XMSFg1z8x48JOlWLkteOVkhlV9c5Nsy+/jiE+qO33fPp6/yZfjglO+Xb0Tl//jO4w3iXwb9hRIVimKkrIKQ1fRoPjl63Ntj/lkobF5Q51zrL/y7PTEZ4u34AsHCtIIff5flR0l5mpTw56DZa4CQerl8vIM2KKAVE6Anjt+ZtXnLXtL8OrMdbj61Tk11i3sKylD17snVc+dqRrqaqzctg99HpyK9+ZuUHqdZJz+qj+8O980mKb+1iU/y3jHZNf+UkeOANu19SVrbfLJZFw+iihy3RuFGOAj6mkQvP2dcfx7qx61k8RFqkMafDzfeKLdT3DDa16bU/XZr/hW5zMS7+/4L2umSnWKr4VPSTJa2Z3X7NiPnxStytbP6yf/ntXb96OsgvG0lk5WdbMUT2n77ZpdsevZDo+Cua6b+rbVQWgas2fZ/+HPHUVatqpWL3y9xlaBqCar5ii+8en9oRK7ybWw3O/2lcQm/VV0LBPcY81agACuy8wJ13KUg8RgJXclm5sOk/G7MvvUJ6ehUZ18R9dyi74xziFAhWFl2sodOLlbzfz2XpxI9CR3qE59chrO6d0az17W11c5errf86kn2dxgusBU9zluktOvAZE5iizH7/M3O3+uSTj06smxcGreup0HquzFMUHUXYuR5GHl4lpuxXq/cAOue8PeBOkEv3GmzKgOEkm2HQB9/eg4dqLjNRnvzTUeOb85y9/aG6MG/pMFm9Fx7ERX5Vj97JIy/y5RQbxWfR6cioGPJK69kpXZWY5Vg01kv+TO7HRzb5r4Qj8n0gVPcgwfv2K8a2HjZk4cEblSFC5v0O0fLMTny0zmbFI4KnSaoMitu6nb1KELN+7BeosQ9W4JamTrp94v3VyMoY9/maDIXa9Lcnj9wwHFf/OLKIqI4DfUk1d3zbB6KMmoVFgMTuiJBr1oaZNFspuE35U8RxGQPTH592zZewjHP+LPLl5dtjOSAxbGf/e542fi5Ce+dliK/fVaNSpwXJbldXxUuPFfrcKGnw5h5ppEU/YwlwE3vSCmpyzHrvH6l024DLcVyKnpaUWKXCjd5Mx2XTYn/s4gTE/6Mi58fmaN/c4y3AVPWUUlTvjLl5bHxGWvGTeq5q91eq/OfvYb+4MMcKsr+7Vv4ui4fSXWZjs/9arUxDQVDyviBPN6FZWuWyKiKCKC3ZDa1Jyh4VpRxM9zd5oymIFvVu00NJls31eCh/+31POq3NXb92PZlmqF56QUN1fa5jRhU1Kj+Pt3fnBxFWe8OrOoxrbtSffU6YiqaOcBPDppWRBiGbL7wGHsdrGGAnAu+1ibuRQ3DXJZeWXVim4AntzB3y9MNI0mX18/utyxrxRfrTC+hmS4y3JWb99vus9Jp8ttBdq+rxTzN+xBj1YNXZ2nCgbwi1e+Q/umdTH99hEJ+/780WJMWboNJ3VtjhFHH+G67OTe7sHD5Vi8aS96tmlkOwlq1p5MWrzFtRyq0MvoxKvIaRv56zcLscqiXvql70NTXZ/jVPZte60j4brpc3y2ZCs+W7K1xmpq/byh3cjo9g8WJnwvtxDg8pdnm953MT0JvvBSgb5dEx134XgP68efDtYYVcQTOgU1LJ+0aCvOfvYbZ42qyfa7P7IO95HcDizYsKfK1Thd8BNXyTMpumQgeewtlINd8fr6U1JWgZWa0wnDOqufeD0Jvihcv9v1OQ0L8kMbyiajf7EWbdqTuE/7X1pWGWg4hTIHK7SdNChGDUY8ttF7hRuwc38p/vRv48CQYRDGEw9Kyd8/YYmz69ntD/gmuC1PH4n69g8WJoTuiGLIclEUGcLVr86xPyiJOvm5lhV81NPTzXcqJDFPRfWXG/71PY4PcGV9UF4/RsXEM+UBwE3v/BCplz8VE6bJl5i/wVlY72KbSehih6Mys+CbcbzcgVFPTze9d25jlekHawlrndgmUqyE8BDMUNXI2JWrIqKpGWb1Pzl4o9OGIiicJLixc3MtLilTFo7jhn/Oc20iCqOpcToSnLFqJ94PIO7TobIKTDNImRvHi7JcvnWfacj15O12I3W9YqlReyLUqYgjiiLLiYo3nlmPbIaJJ1SqMEo9mozde71uxwHs3K9GUXy6eKtrs6OTZ75sczHWWtjK3WLmUmrE1x4ThCVjNco+cNibCfPlGWurPuuf+0cuk46ZBVtkcI1AjYn7w0EURRqzz2fcHCA8d7tkKnStV3JDFoaicBOTyG5k5rVRUofxMx/51PSqEVTQK4LnmISSSTeWbPafGQ9IrOPJI1KrECLi9aSYVSmMvR80qrJbEUVnRGEdwiSFgngg7OxjQWKWDyEKpDJ/hhn64J2vGaxZiWP3XpmNoKNal7JGUSxLob09XSBQRMYT1n7tUX154gSpyN6yWYEfBFaNWHJOaiO2F1uvUfCL2fMOYu7CL/oGPiGopetyjLfbjfAlZ7ZicqPeLbXg4OH08r/3QpnO1JGqVyGo0VSQ6w3u+e9ifLd2V2DlGWElrZOkTskxjoLGbBJa1Su8aKNzc1JQT1plRkcVZI+iSONfusVmlalXZq3ZhZ73TVZStltGj6tePZ2qeDdBXSW+yjaoHu+t76tdc2F1e8sczE04eTxFuw7g5elr7Q80wGx+SFW1OGe88zhVTmWwu49m5ThJaxsGyppPInqViLYTkeESVooxjohWE9FCIuqnShbAPml7lDmoaDL0vcLwh/JGpGpBcJAK6bPFW3D7fxbaH+iAMHubTu79XR/Z56RYvnUfHvERJ+pnL3xbY1sYC8W9ol9HY4TXZ5yJXk+vAxhlsf9MAF21vzEAXlAoC3KtfM4iTjaYnhJJrxEF4C8lbDKqa6pfO7eZV87zXwc3EW7k8hsFD73Pl21zdNy3Ntk0E9ZR6B541o0omHk6ACufuPMAvMkxZgNoTEStVMmTk8aK4lDk3CvVkvwyqBoMJl+nQUE0YmSqbguCamyS5xIe/2xFMAWbkE5m/V0HDlvO96TT6AgId46iDQC97WOjtq0GRDSGiAqJqHDHDvPVllak82S2VaTJTCT516pqIL5blzhpHJWgfaqfdxD3s6KSPYWNySa8WALE68kHzPwSMw9g5gEtWtRM2O6ojIBlSiVRSYeYaYz/MpprBlRHbQ2isZkW0OppN0Q1qQ8A5OfW7Ig6devW92EnL3Fm2ko1YSqKTQDa6b631bYpId3c0fS8/Z27PMXpTqqelZM1A2HgJKqtH4K4vb96vdB/ITYkz3lE+Q02igGlpPefaXMUDpgA4CrN+2kwgL3MrCwbTJR7I0IiN779Q8L3rYoWeJX6WDClEhlBxnhnTnJWuJAE8YhTed0sKA3rFiibvSOidwAMB9CciDYCuA9APgAw84sAJgE4C8BqAAcB/FKVLAAg756QTFQbZCdrGfwwZclWpeWrYsKCzWGLkLUoUxTMfJnNfgbwO1XXTyadTU+CGlRFdPWLat+Ft+ekpylz7yHrXBVRw+ljdONnE5ZlJC0ms4NATE+CEKN2Xm7YImQFmdTmZI2iyDIPU0EwpXZe1rz2oeK0ySl2MVLKuAV3giBEk7q1ZUSRCpw26rsPulAUHmXxS9YoirOOU7boWxDSikuObx+2CIJHZEQhCEJKqGWwOEwInijEpgoKURSCkGVk0BxrtFGy3k68ngRBSAGb9hwKW4SsIJP0sSgKQcgyno1ojKtMY6uChGORnqMgonpElKN97kZE5xJRvlrRBEEQ0hdVCcfCwOmIYjqAAiJqA2AKgCsRS0wkCIIgGPD1itRH2FWFU0VBzHwQwIUAnmfmnwM4Vp1YgiAI6c3fPeYMtyLqITyIiE4AcAWAido2WbWToXRpUS9sEQRBiBBOFcXNAO4E8BEzLyGizgC+UiaVECoSC0gQokmkw4wz8zQA0wBAm9Teycw3qRRMEARBSCTqXk9vE1FDIqoHYDGApUR0m1rRsoehXZuHLYIgCGlA1Bfc9WDmYgDnA/gUQCfEPJ+EAGhURzyNBUGILk4VRb62buJ8ABOYuQyZtfBQEARBMMGpovg7gCIA9QBMJ6IOAIpVCSWEi/QABCGahJVXx+lk9jgA43Sb1hPRCDUiCYIgCEZEeh0FETUioqeIqFD7exKx0YUQAFHrwUsQ6sykQW1H/UIhwlSENKRwanp6FcA+ABdrf8UAXlMlVKbTp11jDOzUNGwxhGxDegBpz9CuLUK5rlNF0YWZ72PmtdrfAwA6qxQsk7lxxFGolesvcO9RR9QPSBoha4ja0FVwzaCQOphOW6tDRDQk/oWITgIgQe09kpMDlFdWmu5vWq+WbRkqbZXSnghCNAlrMtuporgewHNEVERERQDGA/iNMqkyHAJZhiB+/zcn2JYhjbngFqkz6U9YC+6cej0tANCbiBpq34uJ6GYACxXKlrEQAftLyi2OsK8MYm4W3BKWx4wQHE6sDSpwZShn5mJthTYA3KpAnqwghwi7Dhyu3pD0/kpQPkEFOSTdi3Tmf78fElrb4GdGVWqdR3KIsPdQmen+dk3rplAaIWuQN1bwiB9FIeNYj+QE8MJG5eb3a984bBEEh8iIwppxl/UNWwRLwnx8loqCiPYRUbHB3z4ArVMkY8ZBEX9hndiyu7Wsj6LHRqNDs8xYd/nFH08OWwTlRLzahU6bxnXCFiGyWCoKZm7AzA0N/howsyzz9EgQI4qwoQyyY/Ru2whdWqTHupRRxx4ZtggZS9QVaZjvnL9VX4IncpI1hZfnHxHbU8TfLUdE5FY6YkR37ytzM+FZ+aFtk/QeMUTW9CSooYZlh4EF950eiixCeuHHbBl1k6dqxl/ez3J/WHGU0gGlioKIRhHRCiJaTURjDfZ3IKIviGghEX1NRG1VyhMmF/ZrU/W50mAOQJIX1eT2UUen5DrptLwgu5t6f7RoUNtyf1mFebSEVHBmT2uzYkaOKIgoF8BzAM4E0APAZUTUI+mwvwJ4k5l7AXgQwF9UyeOUBfeq6dnrFUGlSc/lVyd1QsuG1pU5mxhyVGpSxIa12tULfjyXsl3J1LeJnltWEW49iPI8mcoRxUAAq7UggocBvAvgvKRjegD4Uvv8lcH+lNOorpqevX4iymyEe+85PfDdXSMty8nT5jfCbtqOa9soZAnMueqEDmGLoAw/vcostzyhbi3rxWrlIY8o7J5Ppk5mtwGwQfd9o7ZNzwIAF2qfLwDQgIiaKZQpNPTz10amJ6fUsansqeLh83vGPiisu15fjP4dmrg6Pq1MT1ne2PvBbjQWtukpyo827MnsPwE4mYh+AHAygE0AakTLI6Ix8aRJO3bsSLWMgaAfRVQktUzpZPqIU5AfDYXlhdd+eTzq6RRuuiiKWnk5PhfNRbkpUo+dW3r7ptFeE5SRcxSINfrtdN/batuqYObNzHwhM/cFcLe2bU9yQcz8EjMPYOYBLVoEk7ijT7vGgZQTp5lNsK5DZdVBAP0EZ4vXlTACvE2/zV/2214uzVUqX4wPf3tS1edBnSWJVDZg5/XVo3XDFEliQoSHiyoVxVwAXYmoExHVAnApgAn6A4ioORHFZbgTsUx6KcGtecIOu2b7cHn1ERapKGwJ08WxfTN/Maia1A0n8qURR+g8YO4+65gQJXGHhOFIXx6Km2tNiPKTVaYomLkcwI0AJgNYBuB9Zl5CRA8S0bnaYcMBrCCilQBaAnhElTzJBP1Q4j385vWNvZZuPOWoqs/JpqdsISq/unZeTpUsjevmI89ntsFUQZDJ7KC488zuKb9mrVzrB2D3fMJsNpS+Icw8iZm7MXMXZn5E23YvM0/QPn/AzF21Y65j5lKV8qgkPgdhFiSvqa433ddHID2nL3un5qmxt7qZcI5CPoQHzzsWJ3Su9pdIt7YzPqLw4jocgdtviJ3ZVgUDOzVFQX5qOwh270qUw+KkR1dKAUH3ruKeTLkmM2a5ut7EEQ0KfF8v/s5fN6QTbhnZrcb+oH6ek2x7Tknlyte3rh2IV64eUGP7VSd0BBHZKq3hR4eTxN6OqjmqFI/PGhSoC+0WxkgnVR0pN9iOKEIck2exonBfO68cbOGfrz1DMxtyrsX1vPT04tepUysX5/ZRF8h3YIDJ3L26BR/Tyv0k49CuLXDqMS1N99fXGr7fjTjKcL+T+ZS3rxuErkekdpFUvN56uZV+GuRLBrSzP8gjbn5Lw4AUVuMIzZc5JcwRYdZGgPXyzli9aGxzTI6FSq4RJNBKBu3/NSd2xIpt+zBmWGfs3H/Y8pyoEKVQOrXzclH02GjT/U7MZCce1TylvWG9RLXy3Pfx/DQ0uTb29VRxQpdmmLxkW9hieMN2QV10ydoRhZenYnVKvLdsNqLIs9AUtW1e+n9dN6jGtjq1cvHoBcehQUF+ILb/iwdUh9lyU5ybhjIKcxROcSppSu3KDBSXxDIjNk1xjzhPYWx8NyPNIO931Kpj1k5mZxpW5iquMj3F/p/dq1XVvrq1ci0X+9jlwT3pqOY4tfsRSRe0PMUVjerk4/Gf9fZ0rpvK62REMeWWYY7Lyzfp5YYds0clJWWx9ah1a7tf8GjXEF1tEfok12pI7BOrenH5oPYJ36PgueU1Hpud6FGO7pu1isJqzsCMm07tipHHHGG4Lz7RFB9RDNCt01j64CjLSmA3otBTZaO20xQGl4ut7K25PVU9faPJ7BuGd8HpParnErq1bOC4vEcvOM5w+/4S83zkTgnilniZW7EjrgStRqheOba1+YLIfIOK88avBgZyXbMgmUbsioCZNazkUTKZHQJeFi41rVcLz1xqnFc33rB46RW0b+p8IZtR6W6qT9BVza/p6Y5R3fHSVTW9kxyVZ7I934P9Phk/8bjiBO1+yWBUaKs1zUZTfss3I3mtSdsmdXByt2A8w9zc6zlFP7kqu3Uj/x6GQjYrioBtrsmmJ6ec36c1rjmxo+n+1355vOX13GJ0Xqr6KUEvNDQbCQXhoWMm6cL7E8PQWzo4BPRzm2gRjUce07JqRKHSFGSE2jkK831GV33YZoWznt4WoXpqhbDQ0mwUDMgcRSSJm55GGPjLFz02GqN1cwxOSDY9OWVUz1aWSmvE0camLiP0vuGupODkr4w/jz7GVEl5xe36EaNb+e3YU6o+m704gay0Nik7jMYlft9+f0rXKvOdiobbqiFqlzTqDbLRshpRGO35hZWbehJWr2OrxqkdbRBRjTkXPQ0Kopu8LHsVhfbLux3ZwHDBmllv1WxBXfxwp26E3VoG739PAJ6+pA8AIN9ng3bd0M6ulJQTnrq4N/76c2+T5nFaN67Oe3xO79a2WcG8csVg4xfaTT8g6LnJnJzqnAlm9dAPVj371g4b1TtGJYbGcOLGa6UoerVphBO7VK+k79zC3UI5K1OwUw+q7+46FUseOAP3nZOcdy1YLju+He49W+01vJK1isJuLqFZPWPPhoL8XDx+Ua8a2+NV/dbTuuGyge1wyfHtUb92Hq4d0smwnI7NvK0MNRJb/56d16c1bhjeBS9d6dzuf1F/7xlo3TRXjevWws98XCuZerXz8MIv+lse48aWr4/T1attY8y+89Qax7gZMQbR606OuFtbC+/eoCAPc+6qKZ8Xzu7VCt/fc5rlMXa/e9ptwzH37pG4YXiXqm1DuzbHzDtOsTgrhtWK/dr5OXj714NtyzDD6dPqYqGAWjYsQD2b7HhByJKXm4NfmbQXYZO1C+70Fd/oHbjrrGPQvmldPDJpWY19Fx/fDrf/Z2HCtnivqEndWvjLhTFFsviBMwKUOBHDV4tiCjC5V6fbXYOlD56BAhv3XLc8fH5PHDxcjkcnLXd8zvu/OaFGox5EQ3til+aYttJZDpMv/ngyej8wBUBsrslIybhRjEFYZx7/WS/84Z35Vd+vHdIJzIyrTujoetGd2f2snZeLpvVqWU5m241gOhh0fNo0rmObpxqwm6NIuq7Lm2ql4PS7Lh/kxpwVXTdWVWTtiEKP0WOvUysXvx7WGUscNvYX9I0l71M155dcN41eei+eXHVr5QUysd/9yGq31l8M7oAxw7pYHF2TgZ2aom/7YEO/u0Wf15xAaFa/do21Ba4aiYAM+foGvCA/Fzee0tXTymw/k6Ve6lYQ7anfMoJs08NeoCeT2RHG6ZDz8Yt6YcF9pyvrbVRXkprld25eD9cO6YSXPbqZBsGvTgp+yBzErbztjKM9zQfFr53c00z1iELfow56FfjjP0s0oVrJ623Ky7+8cQeN+IJTt/c0ruDinmNm+InZ9dD5PTHn7mDMgFFFFAX7b5Dq185DXm5OQo9UFXFZ9b3MnBzCPWf3sI2I6fR3eum5qF4M1LONt8VrPds0wpRbTg5MjqAGFAq9TR0Tb0SdPDsvI4ogfmOvto0BAHePNk8upZ/sTqY62q45z13eD8P8rAlhruHR16ttI/znhhMTZfF5P2TBXQgE2fEf1s19bgC3BCWv1ZoNL/iR68J+bRwf+96YExJcY90y5+5TMe/PIx0fb/a7kkeMDbXOwSCDKLtW3jxOR55EzhT3c5f3s52QrlF2/EO8fIsLmSmKt68bhNvOONq4fAXK0Mgb8Yxjj8TzV/RzXVb8GXRsXu36+58bTsSnfxjqTiaDbUOOal4ji2Y6T21kraLQkw6TU1Urv5O+O0HvpXXfOT2w5tGzghPMgzxx3PRS69XOS3CNdcsRDQrQzCT7oBFOzTzx8Cu/NQhXHvSIwup2nXXckWjqIgFQfQOTqrXpyfjiJx7V3DRUe5BpW6tD19QkN4fQsqG1+67Vs9Dv69+hCY5p1dBVp8SobBWRkmWOIkQYAUyYRTpAcOLaAyIyfelV5hwwIqi7dmFf5yMTpwTRxsVHFPrIvFXlO/z1BGd2ebedHX3P3MGAwpPDg90ZbnJLWJWVm0Po36EJPv7dSbj0eOM6bDQSsSrTLKVxnAt0dc6o7HSKlOyErFcU6UINrycP59o1TtcONZ6Qnn3nqfjmjhEurhgsYbx0Tu33N444CnXyc9E7ab2Dnl8aTPR7UURBd0dqetJZuMd6ELggP9HtOjkF8PvXu8+eaOztF/vfu13jGgrtzOPcRViIY7heSff5b5f0qTLjGt21oV2jmSHRK1m7jkJP1EcEeqoqsIPGs2XD2thW7D8N+ZEWgdXS6d65IflXdWxWF++MiS38+uTGIdh7KBahdlDnZlj20CjDMuIjCqNGx2m7G5uj8KYom9TNx+6DziPpXtS/Le7/ZKnhvuQBhZVMKx8+E898sRK/HZ5okko2RdXNdzGi8FjNzHLY25Xp5nLJZqahXZtjSNea85Zm78r4y/s6yoEe5hgla0cURo/sN8M6B1dYBLCz2ybTpUV9XDygLZ7zMDEIxMIvO5mgNjMPmOHGrNK8fjAJfZIvWTsvF60axUx4x7VtZNgQJNNb89hpaBDDx4n9/obhXdClRbXbptvGcuqtid5e+tP1jU680W9QkG/q5uzG9FQrLwe3ndG9hmt53VqJIwwvcQ3tRnjJUjqpO0Y6z8l5VR6ISQW4jcNVKzcn8qlZs0pRXDukE0Yc3QJTbhlWFeSsQ7O6OpfT6BPvlaiQNTeH8PjPervKCaHnxSv746mL+1R9r5FsSSNugghsrlMr5+aRXfHFrcODKdKDcJ2b18M5vavzlz90fk98cuMQw0l4J6XfMao7iAijtHhWTU3Cyhjx3V2nWtrZ2cQt3DSVr4+HNevOUzDrzlPwZFKcLzdlVtV7lxXfyj3W7YiCk0aIY4Z1xsCOTXFRv8Q5KH3d+fpPw22v53TiO8x5j6wyPd2jC7jV9Yj6eG/MYAzs1BRz1sVi3A/sWNPFMWr0aN0QW4tLXMWKitqAJx5Hy48XkxFtGtdBI5uFVW5x825+qTUKnyzYDCBmoz/OZO6iTq08HDhc4ajcP552NK4b0hlNXHg1GY0mE0YRum9OfmIN05NjSVA1EqtZpnHNbNO4DjbtOZSwrZ6W0S8+SjMjuUiy0hRVuwwmuh28NK0a1bGdZ0mOumsmgRG3jzoaj3+2ouq7m7w1QZNVIwo9RIRBnZtV/V9w7+kYqcu05qosD+fEJ/qcRwGNHXfJ8e0w6aahnhYIqfQCdtNwnHrMEfj7lf1xo4lbZRQJ+t6995vBuKhfW0cr2nNyyJWSCAq9KVaFC7mR6en/LjrOsPFtVr82Pv7dSYbRh41Wrz943rEoemy05Rya5b6Afq+fUvRzPEWPjXbl3h00WTWisCLonqgdD5x7LNo2qYNTTMwzZhBio4qo4Ml7hwhnBJhOMh0n1Lu0qI8nL+6NJyY7D5zol4Q5Cja+b/ot+sYyuT/j9463a1qnhlcUAJzft41pDnmrJETJVI0EHZiV3ZuzrH+9vvPn5P1IHllPu214lbNEVMjaEUXYNKlXC7eP6q4kr0AN0q8dTSDZNvvZzUPxyY1Dah4XwLUeuaAn2gRsEos6Zg2lfrV5kIvnAGDG7acYTvr6VfpmpifDdRSK3ot7RrvLKdEryZzWoVm9GtvCRkYUDpg59hTsKzHX8OmwsjuT6H5k4ogqyNt/xaAOuMJFyGlVOAnP7Re7+zai+xG4bkgn/OObdVUr0OMEoZSNlE9Qz7Jq4tnJscFcsoqmOs+7TGkbZEThgDaN69RonG46JX3s62G5cw3qHH3ngCiy4N7TMe224YGXyw4+J/Pns3ug6LHRwaSXdYCnZpVqfqyyPFmE/lDVhJuVG5eltcW6pKgiisIjt55+NJ65tA8ANRXu3TGD8cC5x/oq42+X9MFF/dri2NYxzxuVfRsj88Wvh3bGjNtTuKI7HfybHdCobj7q1lI82NfdK71ZxmkHeHgAaXKNRxTOBJhx+whDE2Hy+cnrU289rZuSkPhu+PjGITi7l7cV42EhpqeIMrhzMwzubB4+2QnxCdP9peUBSeUOInLoHpjI81f0Q7eWDVBa7sx9NNUKUBV18nNxqMzZb7bCLNx94oI7dm0WiUeynXLLMNuQ9maMPq4VTtJWIRtNzzmVqF3TujihSzN8MG+j4f6qIJpJBd50aleDY4N9yHa3tUWD2hh3aV/8b+GWQK+rElEUPghj/UuGdJotOUuLz7N0c7Gr88KM1+8V/crrH+49zXedmvfnkZ6y37mhVaMC5Hs0RelX/RspqvimiTcNwXab8DNu7pVh3fA4f2B3mtmEvH5rEFklU4mYngIg6vNVKld0OvntyXH5neK04Y/6/bdCH4W0ID8XdWr5y1/erH5tNDAIGZKMvkro73IqJ18NRxTa9Y9t3QgjHLqOW0nsxIsq/boXqUdGFEIgWDXq744ZjAoVAfozgDC9YrxeObDFaAGVY1SzkrcZxnMK5OoG5ZoUnM4dGqUjCiIaRUQriGg1EY012N+eiL4ioh+IaCERBZ9RRyGpNHVEt5LZC5afm2O4uCpoVAyc0tGc5QQvvyqyVVCH6ToKi3Os6o0+qmuGpZhwhbIRBRHlAngOwGkANgKYS0QTmFkfx/jPAN5n5heIqAeASQA6qpJJFenwAgGqeq/hvz2pWJmtsuc//vK+NSKrqsD0Jzh8hOQ0L2uKsQrgZ3mezSOdc/ephpF/0+V9DxKVI4qBAFYz81pmPgzgXQDnJR3DAOILFBoB2KxQHiHNSMfQHF44u1drnNLdW5yxwTZrVfQL5RLmJZg9j1Kj/FS81RljpXJEgwJPI+Hojv69o3KOog2ADbrvGwEMSjrmfgBTiOj3AOoBGGlUEBGNATAGANq3bx+4oF6JYOfKEJViHtMqpufDjGwZJyqPo0FBHvaVpMYl+e3rBlv+7gX3nY7PFm/Fze/NT9huFkk2A9u4GCmYo4gPuMyUld7DLd0IezL7MgCvM/OTRHQCgLeIqCczV+oPYuaXALwEAAMGDIhKe4AOzWKNY9TispihohG4cnAH9G3XxDScdio4+egWeK9wA3q2Dk8GPdNvG5GytSt2bpYF+bkoyDc2HLjtfUdNiViFB4934mrl5uCoI+obrp+oKsdpPggHSZOMjnj0guPQsVndqk6VGy4f1B6jAgyg6RWVimITAH0qs7baNj3XAhgFAMw8i4gKADQHsF2hXIHRv0NTTL1lGI46Ito9hXjwtc4tvC2S0nN6j5bYo0uvSUShKgkgtu5iyQNn1MioFhZN6tUKJSy4HfogfHaN44V9zTMVBm1aaV6/FnbuP+z5fP38Ub/2TfAK1lVFWM7JIXyelOkvTv8OTfD9j3sCD9+dfH8uH+TdCvLoBcf5lCYYVL5ZcwF0JaJOiCmISwFcnnTMjwBOBfA6ER0DoADADoUyBU5Xj9ngUkndWnl47ZfH2yZ9ccJLVw3wL5ACoqIk7Jh40xAUHwpnpfyxbRrhwn5tMf6r1fh5/+qsbAlKQ2vkjOq1Ctv7O78ejM4t6mHQo18EUt7oXq3Qt/0pjpJi3TGqOy7q39bzKnMzojbyCgJlbxczlxPRjQAmA8gF8CozLyGiBwEUMvMEAH8E8DIR3YLYqO0aDjPfXxrg9e6MSIrNc2KXZlUpNgVzgq6Nx4ZoHiMAfzrjaFw/vAvq5Odi8pKtABJ/45ihnbF2xwFcPtC8Fxykk8EJXfyFqTHCaebEvNycGsE+nWC+TiKaXmFBoLQbxsyTEHN51W+7V/d5KYCTVMqQKQTdS3n714MDLjF4urWsj7OOOxI3jjC3L6eKTOol1tdGX0a/qVn92ng5oqPGGkSsTc4hoAKREysQ0mO8LmRk5bMjLzcHz1/RP1QZWmkhoc/r0zpUOfxhrOZO6toc3Y9sgFtO6+awFLPpWgEAPvrtSZi4aIthQqZ0RxSFIFjQrH5tLH9oVI3EPemFcePesCAfn908zHVpmbhOwAl2VqWebRqhZxvnpsWGBXkoTpEbtV9EUaQJWfpuRoJUhB9JCyJaCSMqli0z7jgFpQGElU8FoigEIeNJ16Y0s2lUJx8wyR0SNURRpAl3nnUM9peWY1i35vYHC5Fj3GV9UVZeaX+gEFmaa+stWjZMv1SmfhFFkSZ0al4vLTyVBGPO7R3eZHi3lrEFoaf18BZPKk62j0su7NcGBfm5WelWLopCEDKczi3qY9mDo3wnRYoaqfa/IiKMTrNc10EhiiKFTLllGOrIxKgQAkEqiQxdUyZYIIoihXRLg3AfgmBGtrrFCpIzWxAEl0Qt658oMPWIohAEwRENDLK9hYmEhUsdYnoSBMER7//mBHyxbBvq1pJmI9uQEYUgCI7o1LwerhvaOWwxhBCQroEgCKHz6R+GYtX2/Z7OlTkK9YiiEAQhdI5p1dBTqlAhNYjpSRAEQbBEFIUgCGmJ+DylDlEUgiCkNUGmZhWMEUUhCEJaE7UFgJmIKApBEATBElEUgiCkNWJ6Uo8oCkEQBMESURSCIAiCJaIoBEFISyQmYOoQRSEIQlojITzUI4pCEARBsEQUhSAIgmCJKApBENISmaJIHaIoBEEQBEtEUQiCIAiWiKIQBEEQLBFFIQhCWlI7L9Z85eaIf6xqlGa4I6JRAJ4BkAvgH8z8WNL+vwEYoX2tC+AIZm6sUiZBEDKDe0b3QPP6tTHq2CPDFiXjUaYoiCgXwHMATgOwEcBcIprAzEvjxzDzLbrjfw+gryp5BEHILBrVzcfYM7uHLUZWoNL0NBDAamZey8yHAbwL4DyL4y8D8I5CeQRBEAQPqFQUbQBs0H3fqG2rARF1ANAJwJcm+8cQUSERFe7YsSNwQQVBEARzojKZfSmAD5i5wmgnM7/EzAOYeUCLFi1SLJogCEJ2o1JRbALQTve9rbbNiEshZidBEIRIolJRzAXQlYg6EVEtxJTBhOSDiKg7gCYAZimURRAEQfCIMkXBzOUAbgQwGcAyAO8z8xIiepCIztUdeimAd5klurwgCEIUUbqOgpknAZiUtO3epO/3q5RBEARB8EdUJrMFQRCEiELpZvEhoh0A1ns8vTmAnQGKEzRRli/KsgHRli/KsgHRli/KsgHRli9Ztg7M7MltNO0UhR+IqJCZB4QthxlRli/KsgHRli/KsgHRli/KsgHRli9I2cT0JAiCIFgiikIQBEGwJNsUxUthC2BDlOWLsmxAtOWLsmxAtOWLsmxAtOULTLasmqMQBEEQ3JNtIwpBEATBJaIoBEEQBEuyRlEQ0SgiWkFEq4lobAjXb0dEXxHRUiJaQkR/0LbfT0SbiGi+9neW7pw7NXlXENEZKZCxiIgWaXIUatuaEtFUIlql/W+ibSciGqfJt5CI+imU62jd/ZlPRMVEdHOY946IXiWi7US0WLfN9b0ioqu141cR0dUKZXuCiJZr1/+IiBpr2zsS0SHdPXxRd05/rT6s1uQPJOeoiXyun6WKd9pEtvd0chUR0Xxte0rvnUUbor7eMXPG/yGWinUNgM4AagFYAKBHimVoBaCf9rkBgJUAegC4H8CfDI7voclZG7FcHWsA5CqWsQhA86RtjwMYq30eC+D/tM9nAfgUAAEYDOC7FD7LrQA6hHnvAAwD0A/AYq/3CkBTAGu1/020z00UyXY6gDzt8//pZOuoPy6pnDmavKTJf6bCe+fqWap6p41kS9r/JIB7w7h3Fm2I8nqXLSMKt9n2AoeZtzDz99rnfYgFSjRM5KRxHmLBEkuZeR2A1Yj9jlRzHoA3tM9vADhft/1NjjEbQGMiapUCeU4FsIaZrVbnK793zDwdwE8G13Vzr84AMJWZf2Lm3QCmAhilQjZmnsKxQJ0AMBuxsP+maPI1ZObZHGtd3tT9nsDls8DsWSp5p61k00YFF8MmJYKqe2fRhiivd9miKBxn20sFRNQRsfzg32mbbtSGhq/Gh40IR2YGMIWI5hHRGG1bS2beon3eCqBliPIBNXOXROXeAe7vVVhy/gqxnmacTkT0AxFNI6Kh2rY2mjyplM3Nswzj3g0FsI2ZV+m2hXLvktoQ5fUuWxRFZCCi+gD+A+BmZi4G8AKALgD6ANiC2NA2LIYwcz8AZwL4HREN0+/Uekeh+VNTLK/JuQD+rW2K0r1LIOx7ZQYR3Q2gHMC/tE1bALRn5r4AbgXwNhE1DEG0yD5LHZchsZMSyr0zaEOqUFXvskVRuMm2pwwiykfsAf+LmT8EAGbexswVzFwJ4GVUm0hSLjMzb9L+bwfwkSbLtrhJSfu/PSz5EFNg3zPzNk3OyNw7Dbf3KqVyEtE1AM4GcIXWoEAz6ezSPs9DzO7fTZNDb55SKpuHZ5nqe5cH4EIA7+lkTvm9M2pDkIJ6ly2KwlG2PZVo9s1XACxj5qd02/V2/QsAxL0tJgC4lIhqE1EnAF0RmyBTJV89ImoQ/4zY5OdiTY64V8TVAD7WyXeV5lkxGMBe3fBXFQk9uqjcOx1u79VkAKcTURPN1HK6ti1wiGgUgNsBnMvMB3XbWxBRrva5M2L3aq0mXzERDdbq7lW636NCPrfPMtXv9EgAy5m5yqSU6ntn1oYgFfXO70x8uvwh5gGwEjGtf3cI1x+C2JBwIYD52t9ZAN4CsEjbPgFAK905d2vyrkBAHicW8nVGzHNkAYAl8XsEoBmALwCsAvA5gKbadgLwnCbfIgADFMtXD8AuAI1020K7d4gprC0AyhCz8V7r5V4hNl+wWvv7pULZViNml47XvRe1Yy/Snvd8AN8DOEdXzgDEGuw1AMZDi+SgSD7Xz1LFO20km7b9dQDXJx2b0nsH8zZEeb2TEB6CIAiCJdliehIEQRA8IopCEARBsEQUhSAIgmCJKApBEATBElEUgiAIgiWiKATBACKqoFhE0AVE9D0RnWhzfGMi+q2Dcr8mokAS3gtCqhBFIQjGHGLmPszcG8CdAP5ic3xjALaKQhDSEVEUgmBPQwC7gVicHSL6QhtlLCKieMTSxwB00UYhT2jH3qEds4CIHtOV93MimkNEK3WB5AQhsuSFLYAgRJQ6FEtQU4BYHoBTtO0lAC5g5mIiag5gNhFNQCwPQE9m7gMARHQmYmGeBzHzQSJqqis7j5kHUiw5z32IhYcQhMgiikIQjDmka/RPAPAmEfVELCzCo1pk3UrEwjO3NDh/JIDXWIurxMz6HAfxYG7zEEt+IwiRRhSFINjAzLO00UMLxGLrtADQn5nLiKgIsVGHG0q1/xWQd1BIA2SOQhBsIKLuiKXe3AWgEYDtmpIYgVhKVgDYh1h6yjhTAfySiOpqZehNT4KQVkhvRhCMic9RADFz09XMXEFE/wLwCREtAlAIYDkAMPMuIppJRIsBfMrMtxFRHwCFRHQYwCQAd6X8VwhCAEj0WEEQBMESMT0JgiAIloiiEARBECwRRSEIgiBYIopCEARBsEQUhSAIgmCJKApBEATBElEUgiAIgiX/D1rswnXEu8OfAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Prompt 0: Why does Earth orbit the Sun?\n",
      "Model output: Why does Earth orbit the Sun?\n",
      "If you cke a ppper the pppersing and the ampler and templer the specing a s a w th compent temple the speed a t skeed t the sppered and teather templer the sperens. I w heaver a w ther as a spening t the sppere temp the temer the spening t temp the temple temend the temper the semend temp the temend the temend the temens the temend they temener the te semend the tement then temer the tementher the the temener temens the temend the te the t teme the t te s wee t t teemp te t t t seempp terse se se t t t was w bee amp ang and t t w t w the w w w w the w w w w w w w w w w the the w w w w w w w w w w w w w w w w w w w he he w w w he w rk w w w w w w w w w w w w      w   w   w w w  w w w    w      w w w w       w  w w                                                                                                                                                                                                                                                                                                             <CUTOFF>\n",
      "\n",
      "Prompt 1: Hello world, \n",
      "Model output: Hello world, I dveled wek te speed tem dvend tes tels teat ter and tem the ppers and t spend t w the spend ters tep teat ter spent ters tears text spencent ters tep ter spent terss and tens.\n",
      "\n",
      "If w th w th ch depend tersend teat the teat ter teater temeny thene temens they temenent they temend temens thener temens they temens teather then temens temens they they temens temens temens tenses thener temenent thenerer thener temens theneres temens teneres temens thener teser temes teses temens tensens temeng tenerers theng thes tens thens tereng theneng tereneng thes tenes t therens theng theng t teses tenseng teneng teres t theng terereses teres t t theres theres theres t teses thes t t theseses t t thes oumes t t t t the the te t the the t t the thes the the the t the t the the che t che che te t t be the t t t che che t che s s s se d be be w theeate d the d t t the t these s se te d t t t t t the d the che d d d s w d d the d the the the che d d d d che ce ce w d te w che che che t d d s d che d te te t t d t t t t t w te <CUTOFF>\n",
      "\n",
      "Prompt 2: How to use ChatGPT?\n",
      "Model output: How to use ChatGPT?\n",
      "If could teally temp the bead temp temple temp temp temp temp temp temp temp templenting themate templines templens the temps the temp templement the spens temp to temple the spens temp templent the tempsent the tempsent templenent the tempens themer templened themen temens them templener templenent the tement themener tementiner the cemplent temens temens themen temens them temenent the cemplenenenent temenent the temens temener temens themenener the the te the te teme te sempseneners teners teng tenenes ther teseneng oreng tenenenenenes theneng teneneners theng theng thes theses teng thenenenereng theng tesereng thes theres thes thes oures ous thereseseng theng ous ous ous thes ous thes thes ous the the the the then then's the the the the the the the he the the the the the the the the the the the che thes the che be the the the the the the the thes the thes the the the the the the the the the che the the the the the che the the che d che thes the che che the t che the p d che the the the the the the d te d<CUTOFF>\n",
      "\n",
      "Prompt 3: My code does not work. What should I do?\n",
      "Model output: My code does not work. What should I do?\n",
      "If when't you ck and the temer and temp the temp temp the temp temp the temp temp temp tems to the t sppecent ts temp texp temp temp temp the temp tempend templement to the sppend t temp thend temp templer to templement themend temend themer tempentinting the tempener tempsent the temps templener the tempsened they temer them tement themen themen tementer themener temener temens themer themener te temementher thener tememener the thes the tey the te temener temener ter thener tes temer ather tes w the the te athe ate se the w the t t weer the t the w the w the w s che w w beee che t be w be the s s w the t che se w s s w t d w w w w w w w w w weee w w w w t w w w w w w the w the w w w w w w w w w w w d w w w w w w w w w w w he he w he he w w w w w d s w w w w w w w w w  w  w  w w w w   w w     w w  w    w he  he w w   w       w                                                                                                                                                                                        <CUTOFF>\n",
      "\n",
      "Prompt 4: Why is this code not working: `1+\"A\"`?\n",
      "Model output: Why is this code not working: `1+\"A\"`?\n",
      "\n",
      "It's a see tealll tem the speed temp to the seam and tess and the temer as and a t skend to the teat skend and teally the tems and teat temp the tems the spens to temp ther teather the specent tems the temend they them temend the temend them templenes they they temend they temend they tempsener the temps temend they the temend temens they they temener themenes themener the temener the temener the temener the temener themer temener thes tey the they tey tey they the te temer ther the te te se seempp temereng terereng tenererers teners teng theng teng t tenes tereneres therereres thes at thereres teres ther thes ther oumes thes thes at theses t thes thes ous t the the t the the the the the the the the t t the thes the the the the the he the the the the te t the the the the d the the che s the te che the the the the the the the the the the the t the the the the the the the d the d the the the the the the the d d che d d w the che d the che the d the d te the the the the the the the d te d te d the d d the the <CUTOFF>\n",
      "\n",
      "Prompt 5: Why is Java better than Python?\n",
      "Model output: Why is Java better than Python?\n",
      "In't the and teally temp temp the speend temp teat templing the and templer the spend temp tes temp temps and temp templer the spend templent temps temp then temp tempent ther tempend templenent the tempsenent templent the temps tempentineng the temens templener the semens templenes the temen temens temens templenes the temen temens the temener tement the sementent temens the ffeencent t temen tementher the temen they the w the tey te sempsenereng tenerenes tener tesener terend thereng theneng theng tens tereng thenerereng theneng tenenereng theng theng theng theng teneng theneng thererereng theres thes teres thes thes ous thes thes theres teses then thes t thes ous thes the thes the the the the the the the the then the the the the the the the the the thes the the the the the the the the ches s the che the te the the the the the the thes thes the thes the the the the the the d the the the the d the the thes the the che che the che s the che che che che s the the te the the the the the the te d s d s s the ch<CUTOFF>\n",
      "\n",
      "Prompt 6: Why is Python better than Java?\n",
      "Model output: Why is Python better than Java?\n",
      "I was temp the sppere as and a tesss and teally temp temp the sears a a skend and t teally temss and teat tells temp the temp temp then temp temp temp templent the temp the ppersing t templene t the spens temp the temple templener the templene the sppenent t tementer the sementhing temens tementher the semend temps temens temps temens the temenent themer temens the temener temener the temens temens the temens temener temens the the temen tess temenener tes temens teseseng tereng tenens tens teres thes terend or the tener thenenses at at theng w theng theng t the t these the te w w the t the w w the w the t the the the t se s che t t t t the s s w t w w w teee w w s t s w w w w w w w the the t w w w w the w t t w d s w w the w w w w w he he he s s s w s w w che d w t w w che s s s w d w s w w w s w w w d w w w w w w w w w the w the d  s the s s s d t t t s s w s w w he he w w w d p  w w w w w w w w w w w w he w w w              w w      w  w     w w   w w                                                       <CUTOFF>\n",
      "\n",
      "Prompt 7: What is the purpose of the main() function in C?\n",
      "Model output: What is the purpose of the main() function in C?\n",
      "I wh could a ske a ppper the and templer the spears and and t the sk and t ambore t skep t teat tell t sp temp the temp temp to temple the temp temp them temp temend the temp temp the temp tempend then templement the temp temend then temp temens then temp temend them temp thement tems themend tems then temend themener the temener the tement themer the tement themer the sement temener the temens temener tement the the the te semeneng tener ter temenes tener tener ther ther thes t the the t te the w the the te the te w w the t w w the w the w the w t t t w the w w w w w w w w the w w w the s w s w w he w w w w he s w w w w w w w w w w w w w w w w w w w w w w w w w w w w w w w w w w w w w w w w w w     w w  he w w            w w          w w                                                                                                                                                                                                                                                                                   <CUTOFF>\n",
      "\n",
      "Prompt 8: What is coding?\n",
      "Model output: What is coding?\n",
      "I somple the somears and the asked a the as a a skeally and the and teally the spens tesss a and teask t the sk allly teathers and and the teat temens the tem spens temp the tem the spears templens the temp the spears tear t temens the they spensing temple the tems the speare templent the spersing tems the temens the specenced and tes temend the temens the sement temens the tement them tement the tement them tement themener tementher the themener tener the t teme temens tes temenseng tenener tereng ter thes theng thenes t tereng teat t thesesenes t at thens t ther t w the t the t t w the the t w w the t the w w w the the t the t t the w w s the w w t the s w w w w w w w be s the t t w w w w the w s w w w w w the w w w be w w w the s w w he w w w w w w s w he w s s w w w w s w w w w w w w w w w le w he be s re s s w he re w w w the w he the  s w  s s  d d re s s w  w s   s he w w w  w     w   w   w w       he w  w  w w w w  w          w                                   w                                      <CUTOFF>\n",
      "\n",
      "Epoch 6\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 1929/1929 [27:49<00:00,  1.16it/s]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Prompt 0: Why does Earth orbit the Sun?\n",
      "Model output: Why does Earth orbit the Sun?\n",
      "It's week the werk the tes tes tess tess tess tes tess text tes text tems temp tes temp tes temp templere the spensinced temps tess temps temp the tems templer templener.\n",
      "\n",
      "Prompt 1: Hello world, \n",
      "Model output: Hello world, hend wel w dvepent wek wek wek wek wek te?\n",
      "I'm would we the ckep the weark tes tes tess tess tes tess tess tess texp tess tes tess texp text tess temp tess temp tess temp therer temss and templerent them spensing temples temples temss templenes them templened templements templenes them semend temens tems templementing them semens temples tems templenes temss templenenes themes temens temens temens them censent t weer the temend temens they temener ther te semend temeneng tenenes tesend tend teneneners theng thes theng tensenereng tenerereses thens.\n",
      "\n",
      "Prompt 2: How to use ChatGPT?\n",
      "Model output: How to use ChatGPT?\n",
      "It's see tess tes tes tess tess tess tesst tes tesst temp tess tess tess tes text tesst temp tes tesst tess temp tes tes temp tes temp templent the spensing temps tesss tess.\n",
      "\n",
      "Prompt 3: My code does not work. What should I do?\n",
      "Model output: My code does not work. What should I do?\n",
      "If when you could and tess tess tess tess tess tess tess tess tess text tess tess tess temp tess temp tess temp tess temp tess temp temp ther temss and templeny them templements temss tems templing them templements temss templens templements themend tems tems templenents templenes templenents temss templentines temps templenes templenes temens tement them tementer tementing temens temens the censement tementhing themes temens teseng tenes temeners theseng tesend tenseners tensereng tenses thenseng oumeng theng thenenenenententhent theng theng theng teng theng outheneng teseneses.\n",
      "\n",
      "Prompt 4: Why is this code not working: `1+\"A\"`?\n",
      "Model output: Why is this code not working: `1+\"A\"`?\n",
      "\n",
      "I would th compenting the is a speced a tess a tesss text tess amp the searce as a skep tess tess tevery tells temp the temp tems temples tems the tems temp then templed them templens them tems templenting to the cempled tems templener them templements them tems temps.\n",
      "\n",
      "Prompt 5: Why is Java better than Python?\n",
      "Model output: Why is Java better than Python?\n",
      "If ckeed the abject and templer the sempling and temstaning the temps and tes temps temp the seem t specing as a skerg tess and teat tells temst temp tems tems tems to templened them templenes the temps templementing them temer sssing templates. It's t seend temend the temen there temend temens temens they cemplened them temend temens they cemplenent t well the tement tement themerer temens them temener thener the temen tey cempseneners tensers temenes thes theseng tesenes teneng tensereres theng thes theng teng thesenenes.\n",
      "\n",
      "Prompt 6: Why is Python better than Java?\n",
      "Model output: Why is Python better than Java?\n",
      "I web as a sked a sker a and temp ther and tes temp tes temp ter as tess temp tess temp tess text temp templener them s temp temp templener. I w w well the w the semp and ters templened temp the tempend temend tems templener templenes templementinty them spend tems templenent tems templenent templener.\n",
      "\n",
      "Prompt 7: What is the purpose of the main() function in C?\n",
      "Model output: What is the purpose of the main() function in C?\n",
      "I w dep th spere is a bjeck and a sker a w the reath ar a skerlly of tess. It's a welll the be tell tem temp the temp temp tems them to templer the temp them templenting them temss temp the templer the tempserer the templere. It's t welll t temend them temp them temend to templement themem them temend temend them temend they tement them tement them them tement themer the seend temend the temens them they the temer semeng teser the temeneng therers.\n",
      "\n",
      "Prompt 8: What is coding?\n",
      "Model output: What is coding?\n",
      "I would dvelop the speck and and and teally the ask and teather as and teams the ar and teally temp the specintion terss and tess. I heaver teat ask tess tems and tes tems the tem tems as temp them tems temp them tems them templenes the tems templenes. It well t we the temp temens templenes them temend templeneres them temend them temend them temens the cemplent temend them tement them cemplenent t tement them temend them tementher themer the temener the they thes tey tey teeseng tenerenerers teres theng theres thes teneng terereng theres.\n",
      "\n",
      "Epoch 7\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 1929/1929 [27:34<00:00,  1.17it/s]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Prompt 0: Why does Earth orbit the Sun?\n",
      "Model output: Why does Earth orbit the Sun?\n",
      "It's wek the specifical and tes the specire as a sk tess of the specifical and tes tess tess temp tems templere. It's all tem them specing tems temps temples tems templement them spended tems tems templeter.\n",
      "\n",
      "Prompt 1: Hello world, \n",
      "Model output: Hello world, welppsppent wevent tems and teps wek wek wek wek wek teps?\n",
      "I'm well k we wek welll we tes tes tess tess tess tess tess tess text tess as teg tess as ask tess tems temp tems temp templere.\n",
      "\n",
      "Prompt 2: How to use ChatGPT?\n",
      "Model output: How to use ChatGPT?\n",
      "It maked ther arbout and temps tesss and tess tess and tess tess tess tess tess tess tess tess tess. It's telly tems tess tess temp tes tesss tess tess temp tess temp tess. It'm well tems temend temend tems templenes temend tems templenes tems temply templementent temens temss and tels templeneres templenes templenes. It's t well t be tement temens tement tementer the spenced tens tement them tementers the temens the temens the temens temens the te these t wey he tey hey tey heng teneneng teng teneneng tenenes.\n",
      "\n",
      "Prompt 3: My code does not work. What should I do?\n",
      "Model output: My code does not work. What should I do?\n",
      "If you have any and temp the becomend and tes tess tess tess tess tess. It'm would ther we ther any ard temps tess tess. It's telll tem the speed tems tems temply them temens them tems temp themes templenes templenes temss templens temss.\n",
      "\n",
      "Prompt 4: Why is this code not working: `1+\"A\"`?\n",
      "Model output: Why is this code not working: `1+\"A\"`?\n",
      "` `````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````<CUTOFF>\n",
      "\n",
      "Prompt 5: Why is Java better than Python?\n",
      "Model output: Why is Java better than Python?\n",
      "\n",
      "I was a seed ther and tess and tess tess tess tess tess and ther of tesst tesss and tess. It's am the seed tems tess tess tess tess tess temp tems tess temply temens temply templents them temples templements.\n",
      "\n",
      "Prompt 6: Why is Python better than Java?\n",
      "Model output: Why is Python better than Java?\n",
      "It's any the seed tes tes tes tes tess tes tess tes tes tess temp temp tess temply templeres temply templementinty of tess templing themes and temples them specing tess. It'm tess templing the temens templenes tems templementing them specince tess.\n",
      "\n",
      "Prompt 7: What is the purpose of the main() function in C?\n",
      "Model output: What is the purpose of the main() function in C?\n",
      "I wek web as a devery and team temps the speend tems temp the speced of tess. It's and templerent the specting tess temps templing the tems temp tems templementing them temps tems templementing them templements templines templens the speced templenting the specing tess and tell temps. It's t g tet temend templenered then temens tems tementing the specent tement tementher the spensed temens the temener the weneres the teseng teseng teeneng theneneneres.\n",
      "\n",
      "Prompt 8: What is coding?\n",
      "Model output: What is coding?\n",
      "I desn't the being and teally the bead and tess tesss and tess tess and tess tems temp the specing tess tesss. It's tell temend temp the spensing temples tems tems templines templens templing the temps templing the temes templenes tems templines them temens templens tems. The seend tems templenent tems templenent the spensices and tels templenent templenes tess tems templenener. I'm seend tem tement the spensed temend then temens themer the temens tementers thenes tesensers.\n",
      "\n",
      "Epoch 8\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 1929/1929 [27:34<00:00,  1.17it/s]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Prompt 0: Why does Earth orbit the Sun?\n",
      "Model output: Why does Earth orbit the Sun?\n",
      "It's would be there arbout tess the specific and tes tess tess tess tess tess tess tess tess tess tem tems temples tems templere.\n",
      "\n",
      "Prompt 1: Hello world, \n",
      "Model output: Hello world, I wekp wek wek?\n",
      "I waked have telk the mp bession and tes tess tess tess. It's all tess tess tess tess tess tess tess tess tess temp the specifically tess temps tess. It's all tess tess temp them semens tems tems temply temens templens temply them temes temples tems templenes.\n",
      "\n",
      "Prompt 2: How to use ChatGPT?\n",
      "Model output: How to use ChatGPT?\n",
      "I was tes thererer and and tesk of tess. It's and there ally arbout team tess tess. It's any tess any tess and tess tess temp the specing tess. It welll we tess temp the specific tess. It'm the spensifically and tess temp the specific and tessem temps templing the specing and temples temples.\n",
      "\n",
      "Prompt 3: My code does not work. What should I do?\n",
      "Model output: My code does not work. What should I do?\n",
      "It's would be the best and tess tess tess tess tes tes tess tes tess tess tess tess tess temp tes temp tess temp tess templing temply temply temples. It's welll temend templeterents tems templing them temes templementates templens temens temss and templens templementing temens templenes they templementing temens templenes temens tems templenes tementherers sssing temens temens temens temens the cemplenent themeres thes temens temes theses temenses.\n",
      "\n",
      "Prompt 4: Why is this code not working: `1+\"A\"`?\n",
      "Model output: Why is this code not working: `1+\"A\"`?\n",
      "` `````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````<CUTOFF>\n",
      "\n",
      "Prompt 5: Why is Java better than Python?\n",
      "Model output: Why is Java better than Python?\n",
      "I would be a specificaly and tes and templening the specing and tes tems temp the specing of test temps. It's all tems tem tem temples templine the specing the specifically and templens. I w he aver and templent templement them spensing and temens templenes templementing the specing and templenes the specince and tell temps the specince and temps the spend tem the spendenced the welpsenenenent theres the temens the temens therm they theseres.\n",
      "\n",
      "Prompt 6: Why is Python better than Java?\n",
      "Model output: Why is Python better than Java?\n",
      "It's seed there arbout test tess tess tess tes tes tess templing tess tess temples temples templing the specifically and templens the specifically and specific temps templing the specifically and specifically temples. It's see t templerent and templens templenes the specifically and specific and tell temps templenes the specifically and specific the specific and temend the specenced of the speced and the sppecerent of the t seermend amens.\n",
      "\n",
      "Prompt 7: What is the purpose of the main() function in C?\n",
      "Model output: What is the purpose of the main() function in C?\n",
      "I werk wek web a sk tems tes temp the specing to we tes templere tess templines templing the specing tess temps templing the specing templing them specifically and tell temps. It's t well be templere templementing the celpsenced templenes templenes they well temsted templetention.\n",
      "\n",
      "Prompt 8: What is coding?\n",
      "Model output: What is coding?\n",
      "I develong the being and and tess and tess and tess. It's any test ask tess tess tess tess tess tess tess tess tess tess temp tess temp the spend tems tems templines. It's t's need tems temply of tess temply temens templing themes templements the spensing temples. It's temend they templetented they templetenting the tempsing the specince tess.\n",
      "\n",
      "Epoch 9\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 1929/1929 [27:33<00:00,  1.17it/s]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Prompt 0: Why does Earth orbit the Sun?\n",
      "Model output: Why does Earth orbit the Sun?\n",
      "If you want the between there and test the specific in test test temp the spensific and tess temps templing the specific temps the specifically and seems. It's well the specific and temples the specific and temples templication the cerceses and temples templication temples the cemplent tems. It's seend temples the becomend and templenes the specifically cerment the tementhererers and the spendencents of the temens them temens them the temen theres theseres.\n",
      "\n",
      "Prompt 1: Hello world, \n",
      "Model output: Hello world, wek wek head wek theres? \n",
      "I way want th specing the specific and tes test tes tes temp tes temp the spend tems templing temples the specifically and tes templication temps. It's all templeres the templication temps templication temps templication temples. It's tell templens the specifically and tell temples templicate temps temples. I well well them temens tempeneres them tementic the tem tement the cementing temens the temens the temen thes themeres.\n",
      "\n",
      "Prompt 2: How to use ChatGPT?\n",
      "Model output: How to use ChatGPT?\n",
      "I waked therer and and tess and tess and tes tess tess tess. It's am spening termens the spend temples tess templing the specific temps tess. It's tell tem templication temps templing the temples templines templines templing the templementing the specifically and specing the specific and tell templened the cercepting templenes.\n",
      "\n",
      "Prompt 3: My code does not work. What should I do?\n",
      "Model output: My code does not work. What should I do?\n",
      "It's week the been any week the speend the were tems tess tess tess tess tess tess tess temp temples tems temp temply temples temply templing the temples templing templementing them specifically templementic temps templing the temples templens templications.\n",
      "\n",
      "Prompt 4: Why is this code not working: `1+\"A\"`?\n",
      "Model output: Why is this code not working: `1+\"A\"`?\n",
      "` `````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````<CUTOFF>\n",
      "\n",
      "Prompt 5: Why is Java better than Python?\n",
      "Model output: Why is Java better than Python?\n",
      "\n",
      "I was a seem the best and tes the specific and the specific in test and tes temp the specince of tess. If years all templeres the specifically and tess templing the specific and techem temples. I we well as templement the specing the temples templicate the cercent all tems templeres.\n",
      "\n",
      "Prompt 6: Why is Python better than Java?\n",
      "Model output: Why is Python better than Java?\n",
      "If years and the being and the specific in test temp the specing of the specific and techer and templement them specing temples. I well we temp templetered them tems templing them templeting them specifically temples. I well we the cerent and and templent templements and temens templenes the specince and templenes the specificall and templenes.\n",
      "\n",
      "Prompt 7: What is the purpose of the main() function in C?\n",
      "Model output: What is the purpose of the main() function in C?\n",
      "I were and the specific and tes test temp the specifical and techem temps of tes templication of tess. The specific in term temps templication temples templication templications templications templents the templications templications.\n",
      "\n",
      "Prompt 8: What is coding?\n",
      "Model output: What is coding?\n",
      "If completinty the best and there arbout and the specince of tess and test temps the specing of the specific and techer and temps of tess. It's all temples the specific and techer and temples temples the specific and terming the specific and temps. It t's all temp the spensice tem temples temples templenes. I well asppening the tem templenes temens templenes the tementing the specifically the ppendicent the templenes.\n",
      "\n",
      "Epoch 10\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 1929/1929 [27:39<00:00,  1.16it/s]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Prompt 0: Why does Earth orbit the Sun?\n",
      "Model output: Why does Earth orbit the Sun?\n",
      "It's wek the specific and the specific and test test test temp the spend tems the specific in test and temples of test templing the specific and templines. It's all temples the specific and tell tems templication temps the specific and tell temp temples. It's all temest templines templines the specing and templens the specifically and templenes the specific and the spensing and the specting and the templerent the tempserent.\n",
      "\n",
      "Prompt 1: Hello world, \n",
      "Model output: Hello world, wek hepspopents?\n",
      "It's would bener and there arbout there arbout therem and tess the spensifically webset the specifically and specific and tell temp the specific and temps of tes. It's all the specific and temps templic templications and tell temp the specific and tell templement templents. I well we templened the cercent and templent the templent them tementing the celps and tementherer.\n",
      "\n",
      "Prompt 2: How to use ChatGPT?\n",
      "Model output: How to use ChatGPT?\n",
      "I keed as a few the best and test tes test and tes tess templent the speend templing the spensifically temps. I well we templere them spend templing the weren templementing of tess. It's all temens templines templing the temes templines templing the specifically and temens templenting the spensed of tell templenes. It well been there all temens templenes the tement tem tem tementing the tementing the tementhererers thenes temens them tementhererers.\n",
      "\n",
      "Prompt 3: My code does not work. What should I do?\n",
      "Model output: My code does not work. What should I do?\n",
      "If week week and tesk and the spend the were tems tess and tess tess. It's well tement the spend the were tementing templing the temps templing the templing templing the templication temps. I well well templement them and templent the were templent templenes templines templent the temens temens templing the templenes temens templications.\n",
      "\n",
      "Prompt 4: Why is this code not working: `1+\"A\"`?\n",
      "Model output: Why is this code not working: `1+\"A\"`?\n",
      "I week week and the are and the temples the speend the spensifican the specifically and specific the specific and temples. I well are sementing and and templens of templication the specific and the specific and templent the specting templent the specific and templens the cercent and templent templines the spensifically and templens the spective the spensent and the spensenced of the tem templenent them tempsenterent off theneres.\n",
      "\n",
      "Prompt 5: Why is Java better than Python?\n",
      "Model output: Why is Java better than Python?\n",
      "I was a seed a server and and and spend the spend of the spend of the spend of the spend of tes temples the spend of the spend of tes. It's all temply the spensific the specifically and specific in term temples. I well we bell about the develop and templent the spensed of templenes the specific and temples. It's all tementher tementing the specing and templenened the tem tem tementing the tem tementherer.\n",
      "\n",
      "Prompt 6: Why is Python better than Java?\n",
      "Model output: Why is Python better than Java?\n",
      "If you have a spericence and tes and tess the spension of the specing and the specific in test temps the specific in temples. If years the specific and templing the specific in temples. I we all temples templication and tell temps the specific and and templening tem templement them specince and templens the spensifically and spend tem templenes.\n",
      "\n",
      "Prompt 7: What is the purpose of the main() function in C?\n",
      "Model output: What is the purpose of the main() function in C?\n",
      "I was any are and the spend of temples and tes temp the spension of tess tess. It's all temples the spensing the spend of tess temp the spensing of tess. It's all temples the spensific and tell temps templing the spensifically and spensed tem temples templing templement them spensificall and spensifically the specing and templenes.\n",
      "\n",
      "Prompt 8: What is coding?\n",
      "Model output: What is coding?\n",
      "If you could be a send the specialing and test test test and test temp the specifing the specing and the specific in tester and temps the specific and templines the specific and temps the specific and tell temps templing the cercoms. It's seend templines templines templines tems templines templines templines the celps and templenes.\n",
      "\n",
      "Epoch 11\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  7%|▋         | 135/1929 [01:55<25:34,  1.17it/s]\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-23-3fffb970ab35>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     11\u001b[0m         \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdevice\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     12\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 13\u001b[0;31m         \u001b[0moutput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mloss\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     14\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     15\u001b[0m         \u001b[0mloss\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbackward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/lib/python3/dist-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_call_impl\u001b[0;34m(self, *input, **kwargs)\u001b[0m\n\u001b[1;32m   1128\u001b[0m         if not (self._backward_hooks or self._forward_hooks or self._forward_pre_hooks or _global_backward_hooks\n\u001b[1;32m   1129\u001b[0m                 or _global_forward_hooks or _global_forward_pre_hooks):\n\u001b[0;32m-> 1130\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mforward_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1131\u001b[0m         \u001b[0;31m# Do not call functions when jit is used\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1132\u001b[0m         \u001b[0mfull_backward_hooks\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnon_full_backward_hooks\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m<ipython-input-9-f94c1bba862f>\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, x, calculate_loss)\u001b[0m\n\u001b[1;32m     54\u001b[0m             \u001b[0;31m## Run the decoder\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     55\u001b[0m             \u001b[0;31m#do masked self attention\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 56\u001b[0;31m             \u001b[0mmask\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mMultiHeadAttention\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgen_causal_mask\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mres\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdevice\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     57\u001b[0m             \u001b[0mres\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mres\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0md_self_attention\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mres\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mres\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mres\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmask\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmask\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     58\u001b[0m             \u001b[0mself_res\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mres\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "model.train()\n",
    "\n",
    "epoch_losses = []\n",
    "for epoch in range(n_epochs):\n",
    "    print(f\"Epoch {epoch+1}\")\n",
    "    \n",
    "    batch_losses = []\n",
    "    for data in tqdm.tqdm(dataloader):\n",
    "        optimizer.zero_grad()\n",
    "\n",
    "        data = data.to(device)\n",
    "\n",
    "        output, loss = model(data, True)\n",
    "        \n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "\n",
    "        batch_losses.append(loss.item())\n",
    "\n",
    "        \n",
    "    epoch_losses.append(sum(batch_losses)/len(batch_losses))\n",
    "\n",
    "    plt.plot(range(len(batch_losses)),batch_losses)\n",
    "    plt.xlabel(\"Batch\")\n",
    "    plt.ylabel(\"Loss\")\n",
    "    plt.title(\"Batch loss\")\n",
    "    plt.show()\n",
    "\n",
    "    plt.plot(range(len(epoch_losses)), epoch_losses)\n",
    "    plt.xlabel(\"Epoch\")\n",
    "    plt.ylabel(\"Loss\")\n",
    "    plt.title(\"Epoch loss\")\n",
    "    plt.show()\n",
    "\n",
    "    torch.save(model, prefix_models+f\"model_E{epoch}\")\n",
    "\n",
    "    with open(prefix_models+\"losses.txt\", \"a\") as f:\n",
    "        f.write(f\"{epoch_losses[-1]}\\n\")\n",
    "    \n",
    "    \n",
    "    with torch.no_grad():\n",
    "        for i, prompt in enumerate(prompts):\n",
    "            print(f\"Prompt {i}: {prompt}\")\n",
    "            output=generate(prompt)\n",
    "            print(f\"Model output: {output}\")\n",
    "            print()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "DUracnfcZSLX",
    "tags": []
   },
   "outputs": [],
   "source": [
    "model = torch.load(prefix_models+\"model_E9\")\n",
    "\n",
    "with torch.no_grad():\n",
    "    for i, prompt in enumerate(prompts):\n",
    "        print(f\"Prompt {i}: {prompt}\")\n",
    "        output=generate(prompt)\n",
    "        print(f\"Model output: {output}\")\n",
    "        print()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "r31WSN9YiTuZ"
   },
   "outputs": [],
   "source": [
    "import builtins\n",
    "while True:\n",
    "    prompt = builtins.input(\">>> \")\n",
    "    output=generate(prompt)\n",
    "    print(f\"Model output: {output}\")\n",
    "    print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "accelerator": "GPU",
  "colab": {
   "machine_shape": "hm",
   "provenance": []
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  "gpuClass": "premium",
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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