Fine Tuning Cosmos by LoRA and QLoRA.ipynb

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     "text": [
      "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\r\n",
      "cudf 23.8.0 requires cubinlinker, which is not installed.\r\n",
      "cudf 23.8.0 requires cupy-cuda11x>=12.0.0, which is not installed.\r\n",
      "cudf 23.8.0 requires ptxcompiler, which is not installed.\r\n",
      "cuml 23.8.0 requires cupy-cuda11x>=12.0.0, which is not installed.\r\n",
      "dask-cudf 23.8.0 requires cupy-cuda11x>=12.0.0, which is not installed.\r\n",
      "apache-beam 2.46.0 requires dill<0.3.2,>=0.3.1.1, but you have dill 0.3.8 which is incompatible.\r\n",
      "apache-beam 2.46.0 requires numpy<1.25.0,>=1.14.3, but you have numpy 1.26.4 which is incompatible.\r\n",
      "apache-beam 2.46.0 requires pyarrow<10.0.0,>=3.0.0, but you have pyarrow 15.0.2 which is incompatible.\r\n",
      "beatrix-jupyterlab 2023.128.151533 requires jupyterlab~=3.6.0, but you have jupyterlab 4.1.2 which is incompatible.\r\n",
      "cudf 23.8.0 requires cuda-python<12.0a0,>=11.7.1, but you have cuda-python 12.3.0 which is incompatible.\r\n",
      "cudf 23.8.0 requires pandas<1.6.0dev0,>=1.3, but you have pandas 2.1.4 which is incompatible.\r\n",
      "cudf 23.8.0 requires protobuf<5,>=4.21, but you have protobuf 3.20.3 which is incompatible.\r\n",
      "cudf 23.8.0 requires pyarrow==11.*, but you have pyarrow 15.0.2 which is incompatible.\r\n",
      "cuml 23.8.0 requires dask==2023.7.1, but you have dask 2024.2.0 which is incompatible.\r\n",
      "dask-cudf 23.8.0 requires dask==2023.7.1, but you have dask 2024.2.0 which is incompatible.\r\n",
      "dask-cudf 23.8.0 requires pandas<1.6.0dev0,>=1.3, but you have pandas 2.1.4 which is incompatible.\u001b[0m\u001b[31m\r\n",
      "\u001b[0mNote: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "#pip install -q -U transformers datasets accelerate peft trl bitsandbytes wandb sentence_transformers faiss-gpu"
   ]
  },
  {
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   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-03-26 16:50:33.809409: E external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:9261] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n",
      "2024-03-26 16:50:33.809511: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:607] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n",
      "2024-03-26 16:50:33.941916: E external/local_xla/xla/stream_executor/cuda/cuda_blas.cc:1515] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n"
     ]
    }
   ],
   "source": [
    "import warnings\n",
    "warnings.simplefilter('ignore')\n",
    "import os\n",
    "import argparse\n",
    "import accelerate\n",
    "import torch\n",
    "from trl import SFTTrainer\n",
    "from datasets import load_dataset\n",
    "from transformers import TrainingArguments\n",
    "from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig\n",
    "from peft import LoraConfig, prepare_model_for_kbit_training, get_peft_model\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "a7a48258",
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   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[34m\u001b[1mwandb\u001b[0m: W&B API key is configured. Use \u001b[1m`wandb login --relogin`\u001b[0m to force relogin\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: \u001b[33mWARNING\u001b[0m If you're specifying your api key in code, ensure this code is not shared publicly.\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: \u001b[33mWARNING\u001b[0m Consider setting the WANDB_API_KEY environment variable, or running `wandb login` from the command line.\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: Appending key for api.wandb.ai to your netrc file: /root/.netrc\n"
     ]
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    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from kaggle_secrets import UserSecretsClient\n",
    "import wandb\n",
    "wandb_api = \"your_wandb_api_key\"\n",
    "wandb.login(key=wandb_api)"
   ]
  },
  {
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   "outputs": [],
   "source": [
    "#dataset = 'ardaorcun/turkish-instruction-dataset-prepared'\n",
    "dataset = 'merve/turkish_instructions'\n",
    "base_model = 'ytu-ce-cosmos/turkish-gpt2-large'\n",
    "model_name = 'cosmos-finetuned'\n",
    "auth_token = 'your_huggingface_token'\n",
    "#push_to_hub = False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "3a42f31f",
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   "source": [
    "def print_trainable_parameters(model):\n",
    "    \"\"\"\n",
    "    Prints the number of trainable parameters in the model.\n",
    "    \"\"\"\n",
    "    trainable_params = 0\n",
    "    all_param = 0\n",
    "    for _, param in model.named_parameters():\n",
    "        all_param += param.numel()\n",
    "        if param.requires_grad:\n",
    "            trainable_params += param.numel()\n",
    "    print(f\"trainable params: {trainable_params} || all params: {all_param} || trainable%: {100 * trainable_params / all_param}\")\n",
    "\n",
    "        \n",
    "\n",
    "def format_instruction(sample):\n",
    "    return f\"\"\"Sen cevap vermeyi seven yardımcı bir dil modelisin.\n",
    "        ### Input:\n",
    "        {sample[\"talimat\"]}\n",
    "        \n",
    "        ### Context:\n",
    "        {sample[\" giriş\"]}\n",
    "\n",
    "        ### Response:\n",
    "        {sample[\" çıktı\"]}\n",
    "    \"\"\""
   ]
  },
  {
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       "Downloading readme:   0%|          | 0.00/28.0 [00:00<?, ?B/s]"
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    {
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     "text": [
      "Downloading data: 100%|██████████| 21.2M/21.2M [00:01<00:00, 19.0MB/s]\n"
     ]
    },
    {
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      "text/plain": [
       "Generating train split: 0 examples [00:00, ? examples/s]"
      ]
     },
     "metadata": {},
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    }
   ],
   "source": [
    "dataset = load_dataset(dataset, token=auth_token)\n",
    "# base model to finetune\n",
    "model_id = base_model"
   ]
  },
  {
   "cell_type": "code",
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   "outputs": [],
   "source": [
    "dataset['train'] = dataset['train'].remove_columns(['Unnamed: 0'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "6a09689f",
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatasetDict({\n",
       "    train: Dataset({\n",
       "        features: ['talimat', ' giriş', ' çıktı'],\n",
       "        num_rows: 51563\n",
       "    })\n",
       "})"
      ]
     },
     "execution_count": 8,
     "metadata": {},
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    }
   ],
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    "dataset"
   ]
  },
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   "source": [
    "bnb_config = BitsAndBytesConfig(\n",
    "    load_in_4bit=True,\n",
    "    bnb_4bit_use_double_quant=True,\n",
    "    bnb_4bit_quant_type=\"nf4\",\n",
    "    bnb_4bit_compute_dtype=torch.float16\n",
    ")"
   ]
  },
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   ],
   "source": [
    "model = AutoModelForCausalLM.from_pretrained(model_id,\n",
    "                                             quantization_config=bnb_config,\n",
    "                                             use_cache=False,\n",
    "                                             device_map=\"auto\")"
   ]
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    {
     "data": {
      "text/plain": [
       "GPT2LMHeadModel(\n",
       "  (transformer): GPT2Model(\n",
       "    (wte): Embedding(50257, 1280)\n",
       "    (wpe): Embedding(1024, 1280)\n",
       "    (drop): Dropout(p=0.1, inplace=False)\n",
       "    (h): ModuleList(\n",
       "      (0-35): 36 x GPT2Block(\n",
       "        (ln_1): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
       "        (attn): GPT2Attention(\n",
       "          (c_attn): Linear4bit(in_features=1280, out_features=3840, bias=True)\n",
       "          (c_proj): Linear4bit(in_features=1280, out_features=1280, bias=True)\n",
       "          (attn_dropout): Dropout(p=0.1, inplace=False)\n",
       "          (resid_dropout): Dropout(p=0.1, inplace=False)\n",
       "        )\n",
       "        (ln_2): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
       "        (mlp): GPT2MLP(\n",
       "          (c_fc): Linear4bit(in_features=1280, out_features=5120, bias=True)\n",
       "          (c_proj): Linear4bit(in_features=5120, out_features=1280, bias=True)\n",
       "          (act): NewGELUActivation()\n",
       "          (dropout): Dropout(p=0.1, inplace=False)\n",
       "        )\n",
       "      )\n",
       "    )\n",
       "    (ln_f): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
       "  )\n",
       "  (lm_head): Linear(in_features=1280, out_features=50257, bias=False)\n",
       ")"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
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       "merges.txt:   0%|          | 0.00/585k [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "2ea9acd37f174b0aa07e3ffd34fb55da",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "tokenizer.json:   0%|          | 0.00/2.37M [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "00211f9b38534a79b7668ba59d7bcc5c",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "special_tokens_map.json:   0%|          | 0.00/438 [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tokenizer = AutoTokenizer.from_pretrained(base_model)\n",
    "tokenizer.pad_token = tokenizer.eos_token"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "aaf0df31",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-03-26T16:51:43.547171Z",
     "iopub.status.busy": "2024-03-26T16:51:43.546867Z",
     "iopub.status.idle": "2024-03-26T16:51:43.899675Z",
     "shell.execute_reply": "2024-03-26T16:51:43.898646Z"
    },
    "papermill": {
     "duration": 0.369601,
     "end_time": "2024-03-26T16:51:43.901971",
     "exception": false,
     "start_time": "2024-03-26T16:51:43.532370",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "trainable params: 12211200 || all params: 431932160 || trainable%: 2.827110627743023\n"
     ]
    }
   ],
   "source": [
    "# LoRA config based on QLoRA paper\n",
    "peft_config = LoraConfig(\n",
    "    r=16,\n",
    "    lora_alpha=32,\n",
    "    target_modules=['c_proj',\n",
    "                    'c_fc',\n",
    "                    'gate_proj',\n",
    "                    'c_proj',\n",
    "                    'c_attn'],\n",
    "    bias=\"lora_only\",\n",
    "    use_rslora=True,\n",
    "    fan_in_fan_out=True,\n",
    "    lora_dropout=0.05,\n",
    "    task_type=\"CAUSAL_LM\",\n",
    ")\n",
    "\n",
    "\n",
    "# prepare model for training\n",
    "model = prepare_model_for_kbit_training(model)\n",
    "model = get_peft_model(model, peft_config)\n",
    "\n",
    "\n",
    "# print the number of trainable model params\n",
    "print_trainable_parameters(model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "7a45d105",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-03-26T16:51:43.929693Z",
     "iopub.status.busy": "2024-03-26T16:51:43.928980Z",
     "iopub.status.idle": "2024-03-26T16:51:43.932944Z",
     "shell.execute_reply": "2024-03-26T16:51:43.932128Z"
    },
    "papermill": {
     "duration": 0.019835,
     "end_time": "2024-03-26T16:51:43.935133",
     "exception": false,
     "start_time": "2024-03-26T16:51:43.915298",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "new_model = 'final_moodel'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "2304534a",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-03-26T16:51:43.961768Z",
     "iopub.status.busy": "2024-03-26T16:51:43.961290Z",
     "iopub.status.idle": "2024-03-27T01:40:40.193821Z",
     "shell.execute_reply": "2024-03-27T01:40:40.192769Z"
    },
    "papermill": {
     "duration": 31736.248208,
     "end_time": "2024-03-27T01:40:40.196312",
     "exception": false,
     "start_time": "2024-03-26T16:51:43.948104",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "d8c166f585f141a193f850ae94924711",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Generating train split: 0 examples [00:00, ? examples/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[34m\u001b[1mwandb\u001b[0m: Currently logged in as: \u001b[33marda-orcun\u001b[0m (\u001b[33mdata-leak\u001b[0m). Use \u001b[1m`wandb login --relogin`\u001b[0m to force relogin\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: Tracking run with wandb version 0.16.5\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: Run data is saved locally in \u001b[35m\u001b[1m/kaggle/working/wandb/run-20240326_165157-mq87k92o\u001b[0m\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: Run \u001b[1m`wandb offline`\u001b[0m to turn off syncing.\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: Syncing run \u001b[33mclassic-wind-32\u001b[0m\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: ⭐️ View project at \u001b[34m\u001b[4mhttps://wandb.ai/data-leak/huggingface\u001b[0m\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: 🚀 View run at \u001b[34m\u001b[4mhttps://wandb.ai/data-leak/huggingface/runs/mq87k92o/workspace\u001b[0m\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "\n",
       "    <div>\n",
       "      \n",
       "      <progress value='5865' max='5865' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
       "      [5865/5865 8:48:15, Epoch 5/5]\n",
       "    </div>\n",
       "    <table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       " <tr style=\"text-align: left;\">\n",
       "      <th>Step</th>\n",
       "      <th>Training Loss</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>11</td>\n",
       "      <td>60.268300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>22</td>\n",
       "      <td>62.161200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>33</td>\n",
       "      <td>56.601300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>44</td>\n",
       "      <td>6.667000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>55</td>\n",
       "      <td>1.991300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>66</td>\n",
       "      <td>1.907200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>77</td>\n",
       "      <td>1.851600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>88</td>\n",
       "      <td>1.822400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>99</td>\n",
       "      <td>1.816800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>110</td>\n",
       "      <td>1.804300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>121</td>\n",
       "      <td>1.824300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>132</td>\n",
       "      <td>1.801800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>143</td>\n",
       "      <td>1.826400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>154</td>\n",
       "      <td>1.721000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>165</td>\n",
       "      <td>1.774600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>176</td>\n",
       "      <td>1.724800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>187</td>\n",
       "      <td>1.779500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>198</td>\n",
       "      <td>1.799000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>209</td>\n",
       "      <td>1.822800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>220</td>\n",
       "      <td>1.778400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>231</td>\n",
       "      <td>1.791300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>242</td>\n",
       "      <td>1.728900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>253</td>\n",
       "      <td>1.779600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>264</td>\n",
       "      <td>1.787900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>275</td>\n",
       "      <td>1.777300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>286</td>\n",
       "      <td>1.732400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>297</td>\n",
       "      <td>1.761200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>308</td>\n",
       "      <td>1.744100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>319</td>\n",
       "      <td>1.716700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>330</td>\n",
       "      <td>1.770800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>341</td>\n",
       "      <td>1.736600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>352</td>\n",
       "      <td>1.834900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>363</td>\n",
       "      <td>1.792100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>374</td>\n",
       "      <td>1.773700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>385</td>\n",
       "      <td>1.828700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>396</td>\n",
       "      <td>1.812600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>407</td>\n",
       "      <td>1.778900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>418</td>\n",
       "      <td>1.777100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>429</td>\n",
       "      <td>1.852100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>440</td>\n",
       "      <td>1.775200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>451</td>\n",
       "      <td>1.792400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>462</td>\n",
       "      <td>1.814900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>473</td>\n",
       "      <td>1.819900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>484</td>\n",
       "      <td>1.810800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>495</td>\n",
       "      <td>1.787200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>506</td>\n",
       "      <td>1.840300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>517</td>\n",
       "      <td>1.748500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>528</td>\n",
       "      <td>1.809500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>539</td>\n",
       "      <td>1.780400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>550</td>\n",
       "      <td>1.863500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>561</td>\n",
       "      <td>1.841900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>572</td>\n",
       "      <td>1.782900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>583</td>\n",
       "      <td>1.821400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>594</td>\n",
       "      <td>1.896900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>605</td>\n",
       "      <td>1.835100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>616</td>\n",
       "      <td>1.856400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>627</td>\n",
       "      <td>1.824600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>638</td>\n",
       "      <td>1.877600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>649</td>\n",
       "      <td>1.857800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>660</td>\n",
       "      <td>1.903700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>671</td>\n",
       "      <td>1.860100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>682</td>\n",
       "      <td>1.846200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>693</td>\n",
       "      <td>1.921400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>704</td>\n",
       "      <td>1.934800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>715</td>\n",
       "      <td>1.974500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>726</td>\n",
       "      <td>1.870800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>737</td>\n",
       "      <td>1.851900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>748</td>\n",
       "      <td>1.886000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>759</td>\n",
       "      <td>1.941000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>770</td>\n",
       "      <td>1.909300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>781</td>\n",
       "      <td>1.902600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>792</td>\n",
       "      <td>1.926900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>803</td>\n",
       "      <td>1.938800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>814</td>\n",
       "      <td>1.900700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>825</td>\n",
       "      <td>1.947300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>836</td>\n",
       "      <td>1.895100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>847</td>\n",
       "      <td>1.971900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>858</td>\n",
       "      <td>1.913700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>869</td>\n",
       "      <td>1.973800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>880</td>\n",
       "      <td>1.955100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>891</td>\n",
       "      <td>1.927400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>902</td>\n",
       "      <td>1.950500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>913</td>\n",
       "      <td>2.018800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>924</td>\n",
       "      <td>1.912900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>935</td>\n",
       "      <td>1.885900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>946</td>\n",
       "      <td>1.960200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>957</td>\n",
       "      <td>1.951900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>968</td>\n",
       "      <td>1.983800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>979</td>\n",
       "      <td>1.966600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>990</td>\n",
       "      <td>1.962900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1001</td>\n",
       "      <td>2.007700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1012</td>\n",
       "      <td>2.038600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1023</td>\n",
       "      <td>1.959400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1034</td>\n",
       "      <td>1.934700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1045</td>\n",
       "      <td>1.950700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1056</td>\n",
       "      <td>1.939200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1067</td>\n",
       "      <td>1.982300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1078</td>\n",
       "      <td>1.990300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1089</td>\n",
       "      <td>2.014600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1100</td>\n",
       "      <td>2.087000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1111</td>\n",
       "      <td>1.993900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1122</td>\n",
       "      <td>1.969000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1133</td>\n",
       "      <td>2.020900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1144</td>\n",
       "      <td>1.984100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1155</td>\n",
       "      <td>1.987600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1166</td>\n",
       "      <td>1.969500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1177</td>\n",
       "      <td>1.965600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1188</td>\n",
       "      <td>1.933100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1199</td>\n",
       "      <td>1.937300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1210</td>\n",
       "      <td>1.992000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1221</td>\n",
       "      <td>1.899800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1232</td>\n",
       "      <td>1.882200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1243</td>\n",
       "      <td>1.993500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1254</td>\n",
       "      <td>1.904600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1265</td>\n",
       "      <td>1.916300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1276</td>\n",
       "      <td>1.984300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1287</td>\n",
       "      <td>1.957000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1298</td>\n",
       "      <td>1.971700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1309</td>\n",
       "      <td>1.980200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1320</td>\n",
       "      <td>2.016500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1331</td>\n",
       "      <td>1.963900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1342</td>\n",
       "      <td>1.969700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1353</td>\n",
       "      <td>1.977600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1364</td>\n",
       "      <td>1.956600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1375</td>\n",
       "      <td>1.922300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1386</td>\n",
       "      <td>2.041900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1397</td>\n",
       "      <td>1.999800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1408</td>\n",
       "      <td>1.999000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1419</td>\n",
       "      <td>1.934500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1430</td>\n",
       "      <td>2.043900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1441</td>\n",
       "      <td>1.967000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1452</td>\n",
       "      <td>2.059900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1463</td>\n",
       "      <td>1.995500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1474</td>\n",
       "      <td>1.943100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1485</td>\n",
       "      <td>1.956100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1496</td>\n",
       "      <td>2.011400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1507</td>\n",
       "      <td>2.034500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1518</td>\n",
       "      <td>2.001400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1529</td>\n",
       "      <td>2.089600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1540</td>\n",
       "      <td>2.050300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1551</td>\n",
       "      <td>2.021900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1562</td>\n",
       "      <td>1.979700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1573</td>\n",
       "      <td>1.989800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1584</td>\n",
       "      <td>2.049800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1595</td>\n",
       "      <td>2.025700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1606</td>\n",
       "      <td>2.053700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1617</td>\n",
       "      <td>2.066000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1628</td>\n",
       "      <td>2.068800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1639</td>\n",
       "      <td>2.005200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1650</td>\n",
       "      <td>2.021100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1661</td>\n",
       "      <td>2.014000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1672</td>\n",
       "      <td>2.014700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1683</td>\n",
       "      <td>2.015300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1694</td>\n",
       "      <td>2.049600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1705</td>\n",
       "      <td>1.981600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1716</td>\n",
       "      <td>2.065800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1727</td>\n",
       "      <td>2.013100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1738</td>\n",
       "      <td>2.010300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1749</td>\n",
       "      <td>2.019300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1760</td>\n",
       "      <td>2.057800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1771</td>\n",
       "      <td>2.045300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1782</td>\n",
       "      <td>2.033400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1793</td>\n",
       "      <td>2.078200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1804</td>\n",
       "      <td>2.069200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1815</td>\n",
       "      <td>2.029500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1826</td>\n",
       "      <td>2.122400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1837</td>\n",
       "      <td>2.030600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1848</td>\n",
       "      <td>2.154900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1859</td>\n",
       "      <td>2.058300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1870</td>\n",
       "      <td>2.059800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1881</td>\n",
       "      <td>2.089100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1892</td>\n",
       "      <td>2.055900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1903</td>\n",
       "      <td>2.022400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1914</td>\n",
       "      <td>2.160600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1925</td>\n",
       "      <td>2.045200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1936</td>\n",
       "      <td>2.079800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1947</td>\n",
       "      <td>2.023400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1958</td>\n",
       "      <td>2.166000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1969</td>\n",
       "      <td>2.072400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1980</td>\n",
       "      <td>2.131100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1991</td>\n",
       "      <td>2.025700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2002</td>\n",
       "      <td>2.047400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2013</td>\n",
       "      <td>2.024800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2024</td>\n",
       "      <td>2.037100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2035</td>\n",
       "      <td>2.097100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2046</td>\n",
       "      <td>2.034600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2057</td>\n",
       "      <td>2.028100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2068</td>\n",
       "      <td>2.027000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2079</td>\n",
       "      <td>2.042800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2090</td>\n",
       "      <td>2.065600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2101</td>\n",
       "      <td>2.059900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2112</td>\n",
       "      <td>2.043600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2123</td>\n",
       "      <td>2.081300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2134</td>\n",
       "      <td>2.057100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2145</td>\n",
       "      <td>1.986500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2156</td>\n",
       "      <td>2.061500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2167</td>\n",
       "      <td>2.064300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2178</td>\n",
       "      <td>2.082400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2189</td>\n",
       "      <td>2.045200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2200</td>\n",
       "      <td>2.076600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2211</td>\n",
       "      <td>2.118200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2222</td>\n",
       "      <td>2.102700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2233</td>\n",
       "      <td>2.072700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2244</td>\n",
       "      <td>1.971600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2255</td>\n",
       "      <td>2.047300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2266</td>\n",
       "      <td>2.099500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2277</td>\n",
       "      <td>2.099600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2288</td>\n",
       "      <td>2.083500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2299</td>\n",
       "      <td>2.062700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2310</td>\n",
       "      <td>2.081100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2321</td>\n",
       "      <td>2.123000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2332</td>\n",
       "      <td>2.028500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2343</td>\n",
       "      <td>1.996700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2354</td>\n",
       "      <td>1.966800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2365</td>\n",
       "      <td>1.974700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2376</td>\n",
       "      <td>1.984100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2387</td>\n",
       "      <td>2.005100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2398</td>\n",
       "      <td>2.008700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2409</td>\n",
       "      <td>1.933400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2420</td>\n",
       "      <td>1.962200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2431</td>\n",
       "      <td>2.007700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2442</td>\n",
       "      <td>1.959300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2453</td>\n",
       "      <td>1.888300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2464</td>\n",
       "      <td>1.932700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2475</td>\n",
       "      <td>2.004100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2486</td>\n",
       "      <td>2.025900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2497</td>\n",
       "      <td>1.972900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2508</td>\n",
       "      <td>1.930600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2519</td>\n",
       "      <td>2.006400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2530</td>\n",
       "      <td>1.919100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2541</td>\n",
       "      <td>1.982700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2552</td>\n",
       "      <td>1.967600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2563</td>\n",
       "      <td>1.910700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2574</td>\n",
       "      <td>2.099400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2585</td>\n",
       "      <td>1.982800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2596</td>\n",
       "      <td>1.963500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2607</td>\n",
       "      <td>1.937800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2618</td>\n",
       "      <td>1.961200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2629</td>\n",
       "      <td>1.977700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2640</td>\n",
       "      <td>1.917900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2651</td>\n",
       "      <td>1.877900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2662</td>\n",
       "      <td>1.957200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2673</td>\n",
       "      <td>2.007800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2684</td>\n",
       "      <td>1.978200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2695</td>\n",
       "      <td>2.016400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2706</td>\n",
       "      <td>1.950000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2717</td>\n",
       "      <td>1.923300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2728</td>\n",
       "      <td>2.015700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2739</td>\n",
       "      <td>1.982500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2750</td>\n",
       "      <td>1.967500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2761</td>\n",
       "      <td>2.010400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2772</td>\n",
       "      <td>1.958100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2783</td>\n",
       "      <td>1.932300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2794</td>\n",
       "      <td>1.985400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2805</td>\n",
       "      <td>1.971700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2816</td>\n",
       "      <td>1.913600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2827</td>\n",
       "      <td>1.964000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2838</td>\n",
       "      <td>1.975300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2849</td>\n",
       "      <td>1.952100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2860</td>\n",
       "      <td>1.933400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2871</td>\n",
       "      <td>1.944800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2882</td>\n",
       "      <td>2.040600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2893</td>\n",
       "      <td>1.896500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2904</td>\n",
       "      <td>1.965100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2915</td>\n",
       "      <td>1.936400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2926</td>\n",
       "      <td>1.973200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2937</td>\n",
       "      <td>1.944900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2948</td>\n",
       "      <td>1.949100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2959</td>\n",
       "      <td>1.892900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2970</td>\n",
       "      <td>1.983400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2981</td>\n",
       "      <td>1.944100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2992</td>\n",
       "      <td>1.957600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3003</td>\n",
       "      <td>1.902100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3014</td>\n",
       "      <td>1.931800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3025</td>\n",
       "      <td>1.960900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3036</td>\n",
       "      <td>1.959700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3047</td>\n",
       "      <td>1.879800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3058</td>\n",
       "      <td>1.961700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3069</td>\n",
       "      <td>1.941100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3080</td>\n",
       "      <td>1.894400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3091</td>\n",
       "      <td>1.840800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3102</td>\n",
       "      <td>1.943400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3113</td>\n",
       "      <td>1.966600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3124</td>\n",
       "      <td>1.918500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3135</td>\n",
       "      <td>1.988700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3146</td>\n",
       "      <td>1.917100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3157</td>\n",
       "      <td>1.971100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3168</td>\n",
       "      <td>1.914900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3179</td>\n",
       "      <td>1.932600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3190</td>\n",
       "      <td>1.977200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3201</td>\n",
       "      <td>1.874500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3212</td>\n",
       "      <td>1.973100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3223</td>\n",
       "      <td>1.884800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3234</td>\n",
       "      <td>1.939700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3245</td>\n",
       "      <td>1.916700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3256</td>\n",
       "      <td>1.853100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3267</td>\n",
       "      <td>1.991600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3278</td>\n",
       "      <td>1.936200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3289</td>\n",
       "      <td>1.906900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3300</td>\n",
       "      <td>1.887500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3311</td>\n",
       "      <td>1.899300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3322</td>\n",
       "      <td>1.897100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3333</td>\n",
       "      <td>1.895600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3344</td>\n",
       "      <td>1.929700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3355</td>\n",
       "      <td>1.891800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3366</td>\n",
       "      <td>1.866400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3377</td>\n",
       "      <td>1.884000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3388</td>\n",
       "      <td>1.948100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3399</td>\n",
       "      <td>1.928300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3410</td>\n",
       "      <td>1.863300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3421</td>\n",
       "      <td>1.916200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3432</td>\n",
       "      <td>1.899200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3443</td>\n",
       "      <td>1.954400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3454</td>\n",
       "      <td>1.858500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3465</td>\n",
       "      <td>1.885200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3476</td>\n",
       "      <td>1.846900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3487</td>\n",
       "      <td>1.822800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3498</td>\n",
       "      <td>2.013700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3509</td>\n",
       "      <td>1.867900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3520</td>\n",
       "      <td>1.867200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3531</td>\n",
       "      <td>1.813000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3542</td>\n",
       "      <td>1.864200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3553</td>\n",
       "      <td>1.834200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3564</td>\n",
       "      <td>1.815900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3575</td>\n",
       "      <td>1.809300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3586</td>\n",
       "      <td>1.803700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3597</td>\n",
       "      <td>1.831100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3608</td>\n",
       "      <td>1.775800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3619</td>\n",
       "      <td>1.811000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3630</td>\n",
       "      <td>1.737500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3641</td>\n",
       "      <td>1.820600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3652</td>\n",
       "      <td>1.790500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3663</td>\n",
       "      <td>1.845800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3674</td>\n",
       "      <td>1.785100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3685</td>\n",
       "      <td>1.817800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3696</td>\n",
       "      <td>1.803400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3707</td>\n",
       "      <td>1.772100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3718</td>\n",
       "      <td>1.818800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3729</td>\n",
       "      <td>1.874300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3740</td>\n",
       "      <td>1.751000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3751</td>\n",
       "      <td>1.823500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3762</td>\n",
       "      <td>1.788100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3773</td>\n",
       "      <td>1.790500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3784</td>\n",
       "      <td>1.788800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3795</td>\n",
       "      <td>1.820700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3806</td>\n",
       "      <td>1.830700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3817</td>\n",
       "      <td>1.828100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3828</td>\n",
       "      <td>1.788900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3839</td>\n",
       "      <td>1.871300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3850</td>\n",
       "      <td>1.729300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3861</td>\n",
       "      <td>1.872200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3872</td>\n",
       "      <td>1.826500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3883</td>\n",
       "      <td>1.775100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3894</td>\n",
       "      <td>1.802800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3905</td>\n",
       "      <td>1.799000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3916</td>\n",
       "      <td>1.779500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3927</td>\n",
       "      <td>1.804900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3938</td>\n",
       "      <td>1.799100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3949</td>\n",
       "      <td>1.743300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3960</td>\n",
       "      <td>1.800700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3971</td>\n",
       "      <td>1.755300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3982</td>\n",
       "      <td>1.767500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3993</td>\n",
       "      <td>1.784100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4004</td>\n",
       "      <td>1.799900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4015</td>\n",
       "      <td>1.812900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4026</td>\n",
       "      <td>1.837700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4037</td>\n",
       "      <td>1.809400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4048</td>\n",
       "      <td>1.812300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4059</td>\n",
       "      <td>1.807400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4070</td>\n",
       "      <td>1.801800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4081</td>\n",
       "      <td>1.745700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4092</td>\n",
       "      <td>1.778000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4103</td>\n",
       "      <td>1.748300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4114</td>\n",
       "      <td>1.807200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4125</td>\n",
       "      <td>1.792800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4136</td>\n",
       "      <td>1.790800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4147</td>\n",
       "      <td>1.780400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4158</td>\n",
       "      <td>1.790100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4169</td>\n",
       "      <td>1.775400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4180</td>\n",
       "      <td>1.804000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4191</td>\n",
       "      <td>1.780400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4202</td>\n",
       "      <td>1.790500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4213</td>\n",
       "      <td>1.837100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4224</td>\n",
       "      <td>1.800900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4235</td>\n",
       "      <td>1.749300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4246</td>\n",
       "      <td>1.765900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4257</td>\n",
       "      <td>1.766600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4268</td>\n",
       "      <td>1.789100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4279</td>\n",
       "      <td>1.713700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4290</td>\n",
       "      <td>1.813500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4301</td>\n",
       "      <td>1.775000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4312</td>\n",
       "      <td>1.788000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4323</td>\n",
       "      <td>1.789800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4334</td>\n",
       "      <td>1.747900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4345</td>\n",
       "      <td>1.766700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4356</td>\n",
       "      <td>1.761400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4367</td>\n",
       "      <td>1.814400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4378</td>\n",
       "      <td>1.767500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4389</td>\n",
       "      <td>1.726400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4400</td>\n",
       "      <td>1.774300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4411</td>\n",
       "      <td>1.746700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4422</td>\n",
       "      <td>1.779400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4433</td>\n",
       "      <td>1.770400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4444</td>\n",
       "      <td>1.688500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4455</td>\n",
       "      <td>1.782100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4466</td>\n",
       "      <td>1.730100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4477</td>\n",
       "      <td>1.739400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4488</td>\n",
       "      <td>1.778600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4499</td>\n",
       "      <td>1.746800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4510</td>\n",
       "      <td>1.721900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4521</td>\n",
       "      <td>1.694100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4532</td>\n",
       "      <td>1.703100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4543</td>\n",
       "      <td>1.732300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4554</td>\n",
       "      <td>1.775000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4565</td>\n",
       "      <td>1.769600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4576</td>\n",
       "      <td>1.796200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4587</td>\n",
       "      <td>1.732300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4598</td>\n",
       "      <td>1.775200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4609</td>\n",
       "      <td>1.720300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4620</td>\n",
       "      <td>1.739400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4631</td>\n",
       "      <td>1.719100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4642</td>\n",
       "      <td>1.729200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4653</td>\n",
       "      <td>1.776100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4664</td>\n",
       "      <td>1.835500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4675</td>\n",
       "      <td>1.756400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4686</td>\n",
       "      <td>1.752400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4697</td>\n",
       "      <td>1.694900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4708</td>\n",
       "      <td>1.678100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4719</td>\n",
       "      <td>1.689400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4730</td>\n",
       "      <td>1.708400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4741</td>\n",
       "      <td>1.650800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4752</td>\n",
       "      <td>1.618700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4763</td>\n",
       "      <td>1.684000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4774</td>\n",
       "      <td>1.657100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4785</td>\n",
       "      <td>1.616900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4796</td>\n",
       "      <td>1.698600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4807</td>\n",
       "      <td>1.658300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4818</td>\n",
       "      <td>1.651300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4829</td>\n",
       "      <td>1.712400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4840</td>\n",
       "      <td>1.680300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4851</td>\n",
       "      <td>1.668600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4862</td>\n",
       "      <td>1.631600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4873</td>\n",
       "      <td>1.637900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4884</td>\n",
       "      <td>1.665700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4895</td>\n",
       "      <td>1.713100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4906</td>\n",
       "      <td>1.679400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4917</td>\n",
       "      <td>1.684000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4928</td>\n",
       "      <td>1.676900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4939</td>\n",
       "      <td>1.701400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4950</td>\n",
       "      <td>1.668200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4961</td>\n",
       "      <td>1.614500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4972</td>\n",
       "      <td>1.671200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4983</td>\n",
       "      <td>1.693500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4994</td>\n",
       "      <td>1.721800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5005</td>\n",
       "      <td>1.666000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5016</td>\n",
       "      <td>1.625600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5027</td>\n",
       "      <td>1.648400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5038</td>\n",
       "      <td>1.648600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5049</td>\n",
       "      <td>1.631200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5060</td>\n",
       "      <td>1.633600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5071</td>\n",
       "      <td>1.656700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5082</td>\n",
       "      <td>1.646700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5093</td>\n",
       "      <td>1.616900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5104</td>\n",
       "      <td>1.614200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5115</td>\n",
       "      <td>1.675000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5126</td>\n",
       "      <td>1.656800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5137</td>\n",
       "      <td>1.608300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5148</td>\n",
       "      <td>1.658300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5159</td>\n",
       "      <td>1.627100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5170</td>\n",
       "      <td>1.649200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5181</td>\n",
       "      <td>1.670400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5192</td>\n",
       "      <td>1.724700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5203</td>\n",
       "      <td>1.669300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5214</td>\n",
       "      <td>1.666400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5225</td>\n",
       "      <td>1.658900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5236</td>\n",
       "      <td>1.663800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5247</td>\n",
       "      <td>1.659800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5258</td>\n",
       "      <td>1.665900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5269</td>\n",
       "      <td>1.629700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5280</td>\n",
       "      <td>1.617900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5291</td>\n",
       "      <td>1.676000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5302</td>\n",
       "      <td>1.613300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5313</td>\n",
       "      <td>1.595800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5324</td>\n",
       "      <td>1.667500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5335</td>\n",
       "      <td>1.722100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5346</td>\n",
       "      <td>1.637400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5357</td>\n",
       "      <td>1.614400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5368</td>\n",
       "      <td>1.598500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5379</td>\n",
       "      <td>1.643000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5390</td>\n",
       "      <td>1.604000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5401</td>\n",
       "      <td>1.669400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5412</td>\n",
       "      <td>1.664400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5423</td>\n",
       "      <td>1.632800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5434</td>\n",
       "      <td>1.653900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5445</td>\n",
       "      <td>1.650900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5456</td>\n",
       "      <td>1.606100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5467</td>\n",
       "      <td>1.617900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5478</td>\n",
       "      <td>1.621900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5489</td>\n",
       "      <td>1.645800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5500</td>\n",
       "      <td>1.628500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5511</td>\n",
       "      <td>1.670400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5522</td>\n",
       "      <td>1.622900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5533</td>\n",
       "      <td>1.651200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5544</td>\n",
       "      <td>1.679900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5555</td>\n",
       "      <td>1.604000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5566</td>\n",
       "      <td>1.619400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5577</td>\n",
       "      <td>1.597200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5588</td>\n",
       "      <td>1.658000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5599</td>\n",
       "      <td>1.662000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5610</td>\n",
       "      <td>1.635300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5621</td>\n",
       "      <td>1.654100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5632</td>\n",
       "      <td>1.609000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5643</td>\n",
       "      <td>1.699900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5654</td>\n",
       "      <td>1.614000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5665</td>\n",
       "      <td>1.603200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5676</td>\n",
       "      <td>1.649000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5687</td>\n",
       "      <td>1.632000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5698</td>\n",
       "      <td>1.611800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5709</td>\n",
       "      <td>1.615400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5720</td>\n",
       "      <td>1.684600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5731</td>\n",
       "      <td>1.628500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5742</td>\n",
       "      <td>1.634800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5753</td>\n",
       "      <td>1.699400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5764</td>\n",
       "      <td>1.615500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5775</td>\n",
       "      <td>1.615200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5786</td>\n",
       "      <td>1.625300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5797</td>\n",
       "      <td>1.614200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5808</td>\n",
       "      <td>1.660000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5819</td>\n",
       "      <td>1.696700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5830</td>\n",
       "      <td>1.583900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5841</td>\n",
       "      <td>1.638000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5852</td>\n",
       "      <td>1.691400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5863</td>\n",
       "      <td>1.620100</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table><p>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "training_arguments = TrainingArguments(\n",
    "    output_dir=\"./results\",\n",
    "    num_train_epochs=1,\n",
    "    per_device_train_batch_size=10,\n",
    "    gradient_accumulation_steps=1,\n",
    "    evaluation_strategy=\"steps\",\n",
    "    eval_steps=1500,\n",
    "    logging_steps=1,\n",
    "    optim=\"paged_adamw_8bit\",\n",
    "    learning_rate=2e-4,\n",
    "    lr_scheduler_type=\"linear\",\n",
    "    warmup_steps=10,\n",
    "    report_to=\"wandb\",\n",
    "    #max_steps=25, # Remove this line for a real fine-tuning\n",
    ")\n",
    "model_args = TrainingArguments(\n",
    "    output_dir=\"cosmos-deneme-22/04(0)\",\n",
    "    num_train_epochs=5,\n",
    "    per_device_train_batch_size=10,\n",
    "    gradient_accumulation_steps=1,\n",
    "    gradient_checkpointing=True,\n",
    "    optim=\"paged_lion_8bit\",\n",
    "    logging_steps=11,\n",
    "    save_strategy=\"epoch\",\n",
    "    learning_rate=2e-4,\n",
    "    max_grad_norm=0.3,\n",
    "    warmup_ratio=0.03,\n",
    "    lr_scheduler_type=\"linear\",\n",
    "    disable_tqdm=False,\n",
    "    #max_steps=10 # disable line for full time training \n",
    ")\n",
    "\n",
    "max_seq_length = 512\n",
    "\n",
    "trainer = SFTTrainer(\n",
    "    model=model,\n",
    "    train_dataset=dataset['train'],\n",
    "    #peft_config=peft_config,\n",
    "    max_seq_length=max_seq_length,\n",
    "    tokenizer=tokenizer,\n",
    "    packing=True,\n",
    "    formatting_func=format_instruction,\n",
    "    args=model_args,\n",
    ")\n",
    "\n",
    "\n",
    "\n",
    "# train\n",
    "trainer.train()\n",
    "trainer.model.save_pretrained(new_model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "ebc2204f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-03-27T01:40:40.273240Z",
     "iopub.status.busy": "2024-03-27T01:40:40.272945Z",
     "iopub.status.idle": "2024-03-27T01:40:40.290545Z",
     "shell.execute_reply": "2024-03-27T01:40:40.289703Z"
    },
    "papermill": {
     "duration": 0.035648,
     "end_time": "2024-03-27T01:40:40.292389",
     "exception": false,
     "start_time": "2024-03-27T01:40:40.256741",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PeftModelForCausalLM(\n",
       "  (base_model): LoraModel(\n",
       "    (model): GPT2LMHeadModel(\n",
       "      (transformer): GPT2Model(\n",
       "        (wte): Embedding(50257, 1280)\n",
       "        (wpe): Embedding(1024, 1280)\n",
       "        (drop): Dropout(p=0.1, inplace=False)\n",
       "        (h): ModuleList(\n",
       "          (0-35): 36 x GPT2Block(\n",
       "            (ln_1): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
       "            (attn): GPT2Attention(\n",
       "              (c_attn): lora.Linear4bit(\n",
       "                (base_layer): Linear4bit(in_features=1280, out_features=3840, bias=True)\n",
       "                (lora_dropout): ModuleDict(\n",
       "                  (default): Dropout(p=0.05, inplace=False)\n",
       "                )\n",
       "                (lora_A): ModuleDict(\n",
       "                  (default): Linear(in_features=1280, out_features=16, bias=False)\n",
       "                )\n",
       "                (lora_B): ModuleDict(\n",
       "                  (default): Linear(in_features=16, out_features=3840, bias=False)\n",
       "                )\n",
       "                (lora_embedding_A): ParameterDict()\n",
       "                (lora_embedding_B): ParameterDict()\n",
       "              )\n",
       "              (c_proj): lora.Linear4bit(\n",
       "                (base_layer): Linear4bit(in_features=1280, out_features=1280, bias=True)\n",
       "                (lora_dropout): ModuleDict(\n",
       "                  (default): Dropout(p=0.05, inplace=False)\n",
       "                )\n",
       "                (lora_A): ModuleDict(\n",
       "                  (default): Linear(in_features=1280, out_features=16, bias=False)\n",
       "                )\n",
       "                (lora_B): ModuleDict(\n",
       "                  (default): Linear(in_features=16, out_features=1280, bias=False)\n",
       "                )\n",
       "                (lora_embedding_A): ParameterDict()\n",
       "                (lora_embedding_B): ParameterDict()\n",
       "              )\n",
       "              (attn_dropout): Dropout(p=0.1, inplace=False)\n",
       "              (resid_dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "            (ln_2): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
       "            (mlp): GPT2MLP(\n",
       "              (c_fc): lora.Linear4bit(\n",
       "                (base_layer): Linear4bit(in_features=1280, out_features=5120, bias=True)\n",
       "                (lora_dropout): ModuleDict(\n",
       "                  (default): Dropout(p=0.05, inplace=False)\n",
       "                )\n",
       "                (lora_A): ModuleDict(\n",
       "                  (default): Linear(in_features=1280, out_features=16, bias=False)\n",
       "                )\n",
       "                (lora_B): ModuleDict(\n",
       "                  (default): Linear(in_features=16, out_features=5120, bias=False)\n",
       "                )\n",
       "                (lora_embedding_A): ParameterDict()\n",
       "                (lora_embedding_B): ParameterDict()\n",
       "              )\n",
       "              (c_proj): lora.Linear4bit(\n",
       "                (base_layer): Linear4bit(in_features=5120, out_features=1280, bias=True)\n",
       "                (lora_dropout): ModuleDict(\n",
       "                  (default): Dropout(p=0.05, inplace=False)\n",
       "                )\n",
       "                (lora_A): ModuleDict(\n",
       "                  (default): Linear(in_features=5120, out_features=16, bias=False)\n",
       "                )\n",
       "                (lora_B): ModuleDict(\n",
       "                  (default): Linear(in_features=16, out_features=1280, bias=False)\n",
       "                )\n",
       "                (lora_embedding_A): ParameterDict()\n",
       "                (lora_embedding_B): ParameterDict()\n",
       "              )\n",
       "              (act): NewGELUActivation()\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "          )\n",
       "        )\n",
       "        (ln_f): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
       "      )\n",
       "      (lm_head): Linear(in_features=1280, out_features=50257, bias=False)\n",
       "    )\n",
       "  )\n",
       ")"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "6af09a94",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-03-27T01:40:40.521925Z",
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     "shell.execute_reply": "2024-03-27T01:40:41.109705Z"
    },
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "del model \n",
    "#del pipe \n",
    "del trainer \n",
    "import gc \n",
    "gc.collect()\n",
    "gc.collect()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "103e5979",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-03-27T01:40:41.227951Z",
     "iopub.status.busy": "2024-03-27T01:40:41.227692Z",
     "iopub.status.idle": "2024-03-27T01:40:41.231406Z",
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    },
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "from peft import PeftModel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "45e0a17d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-03-27T01:40:41.267472Z",
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "model = AutoModelForCausalLM.from_pretrained(base_model,\n",
    "                            low_cpu_mem_usage=True,\n",
    "                            return_dict=True,\n",
    "                            torch_dtype=torch.float16,\n",
    "                            device_map=\"auto\")\n",
    "\n",
    "model = PeftModel.from_pretrained(model, new_model)\n",
    "model = model.merge_and_unload()\n",
    "\n",
    "tokenizer = AutoTokenizer.from_pretrained(base_model, trust_remote_code=True)\n",
    "tokenizer.pad_token=tokenizer.eos_token\n",
    "tokenizer.padding_side=\"rigt\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "43142c70",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-03-27T01:40:45.134856Z",
     "iopub.status.busy": "2024-03-27T01:40:45.134588Z",
     "iopub.status.idle": "2024-03-27T01:42:00.166028Z",
     "shell.execute_reply": "2024-03-27T01:42:00.165151Z"
    },
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
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      "application/vnd.jupyter.widget-view+json": {
       "model_id": "b0b37b6ea28a46669bdf27b3893869d0",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "model.safetensors:   0%|          | 0.00/1.55G [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "634e87b00e00474388b8cacfac768ab9",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "README.md:   0%|          | 0.00/5.18k [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
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    },
    {
     "data": {
      "text/plain": [
       "CommitInfo(commit_url='https://huggingface.co/ardaorcun/finetuned_cosmos2603/commit/ec52ab81283c750d6270414d6c88c936f1363ae7', commit_message='Upload tokenizer', commit_description='', oid='ec52ab81283c750d6270414d6c88c936f1363ae7', pr_url=None, pr_revision=None, pr_num=None)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_name='finetuned_cosmos2603'\n",
    "model.push_to_hub(model_name, token = auth_token)\n",
    "tokenizer.push_to_hub(model_name, token=auth_token)"
   ]
  }
 ],
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