diff --git a/data/procToxicQA.js b/data/procToxicQA.js
new file mode 100644
index 0000000..7ecc75e
--- /dev/null
+++ b/data/procToxicQA.js
@@ -0,0 +1,22 @@
+const fs = require('node:fs');
+var lineReader = require('readline').createInterface({
+    input: fs.createReadStream('toxicQA.json')
+});
+var outstream = fs.createWriteStream('toxicQAfinal.json');
+fs.unlinkSync('toxicQAfinal.json');
+
+lineReader.on('line', function (line) {
+    const dialogue = JSON.parse(line)["conversations"];
+    const newdialogue = [];
+    for (const dialogueLine of dialogue) {
+        newdialogue.push({
+            role: dialogueLine["from"] === "human" ? "user" : "assistant",
+            content: dialogueLine["value"]
+        });
+    }
+    outstream.write(JSON.stringify(newdialogue) + '\n');
+});
+  
+lineReader.on('close', function () {
+    console.log('all done, son');
+});
diff --git a/data/process.js b/data/process.js
index 1fa36b8..4461d5d 100644
--- a/data/process.js
+++ b/data/process.js
@@ -1,7 +1,24 @@
 const fs = require('node:fs');
 const JSONStream = require('JSONStream');
 
-const MIKU_FREQ = 5;   // 1/5 = 20% of message chains are randomly chosen to be from Miku
+const MIKU_FREQ = 5;            // 1/5 = 20% of message chains are randomly chosen to be from Miku
+const DROPOUT_UNFUNNY = 0.75;   // 75% dropout rate for messages chains which have NO reactions
+const USERNAMES = [
+    'vinny volcano\uD83C\uDF0B (伊胜焱)',
+    'Server Comp!',
+    'Make The Map \uD83D\uDDFA',
+    '1981 Celical Man\uD83C\uDF41\uD83C\uDF42',
+    'Hatsune Miku',
+    'Cutie Kazerounian\uD83C\uDF41\uD83C\uDF42',
+    'David Pan (Fembooru)\uD83C\uDF41\uD83C\uDF42',
+    'Exiled Sammy \uD83D\uDD12\uD83C\uDFDD⏱',
+    'shibe.mp4❄☃',
+    'Today Man-San(1990)\uD83C\uDF41\uD83C\uDF42',
+    'owner',
+    'cj7 by stephen chow (gmod PC)\uD83C\uDF41\uD83C\uDF42',
+    'Nicolaid',
+    'epbic',
+];
 
 async function main() {
     let counter = 0;
@@ -14,6 +31,7 @@ async function main() {
     let lastMsgTime;
     let botAuthoredMsgSequence;
     let convoMsgSeqCount = 0;
+    let convoReactCount = 0;
     let convoMsgs = [];
 
     stream.on('data', async (msg) => {
@@ -35,11 +53,18 @@ async function main() {
          */
 
         // scrub links
-        const cleanContent = msg.content.replaceAll(/https?:\/\/\S+/gi, '');
+        let cleanContent = msg.content.replaceAll(/https?:\/\/\S+/gi, '');
+        // scrub @mentions
+        for (const username of USERNAMES) {
+            cleanContent = cleanContent.replaceAll(`@${username}`, "");
+        }
         if (!cleanContent) {
             return;
         }
 
+        // count reaction
+        convoReactCount += msg.reactions.length;
+
         // determine continuity of message sequences
         let msgTime = new Date(msg.timestamp);
         if (lastMsgAuthor !== msg.author.id || (msgTime - lastMsgTime)/60000 >= 7) {
@@ -54,15 +79,19 @@ async function main() {
 
         // 10 msg sequences per "conversation"
         if (convoMsgSeqCount === 10) {
-            // write JSONL format
-            fs.appendFileSync('output.json', JSON.stringify(convoMsgs) + '\n');
-            convoMsgSeqCount = 0;
+            // dropout
+            const convoKeep = convoReactCount > 0 || Math.random() >= DROPOUT_UNFUNNY;
+            if (convoKeep) {
+                // write JSONL format
+                fs.appendFileSync('output.json', JSON.stringify(convoMsgs) + '\n');
+            }
+            convoMsgSeqCount = convoReactCount = 0;
             convoMsgs = [];
         }
 
         // follow chatML chat template
         const outMsg = {
-            role: botAuthoredMsgSequence ? "assistant" : "user",
+            role: botAuthoredMsgSequence ? "assistant" : msg.author.name,
             content: cleanContent
         };
         convoMsgs.push(outMsg);
diff --git a/train_unsloth.ipynb b/train_unsloth.ipynb
index 9d62008..ddcffeb 100644
--- a/train_unsloth.ipynb
+++ b/train_unsloth.ipynb
@@ -1,10069 +1 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "f9f8a4ee",
-   "metadata": {
-    "id": "IqM-T1RTzY6C",
-    "papermill": {
-     "duration": 0.038159,
-     "end_time": "2024-03-28T00:08:52.505173",
-     "exception": false,
-     "start_time": "2024-03-28T00:08:52.467014",
-     "status": "completed"
-    },
-    "tags": []
-   },
-   "source": [
-    "To run this, press \"*Runtime*\" and press \"*Run all*\" on a **free** Tesla T4 Google Colab instance!\n",
-    "<div class=\"align-center\">\n",
-    "  <a href=\"https://github.com/unslothai/unsloth\"><img src=\"https://github.com/unslothai/unsloth/raw/main/images/unsloth%20new%20logo.png\" width=\"115\"></a>\n",
-    "  <a href=\"https://discord.gg/u54VK8m8tk\"><img src=\"https://github.com/unslothai/unsloth/raw/main/images/Discord button.png\" width=\"145\"></a>\n",
-    "  <a href=\"https://ko-fi.com/unsloth\"><img src=\"https://github.com/unslothai/unsloth/raw/main/images/Kofi button.png\" width=\"145\"></a></a> Join Discord if you need help + support us if you can!\n",
-    "</div>\n",
-    "\n",
-    "To install Unsloth on your own computer, follow the installation instructions on our Github page [here](https://github.com/unslothai/unsloth#installation-instructions---conda).\n",
-    "\n",
-    "You will learn how to do [data prep](#Data), how to [train](#Train), how to [run the model](#Inference), & [how to save it](#Save) (eg for Llama.cpp).\n",
-    "\n",
-    "This notebook uses the `ChatML` format for conversation style finetunes. We use [Open Assistant conversations](https://huggingface.co/datasets/philschmid/guanaco-sharegpt-style) in ShareGPT style."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "4c970fa0",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-03-28T00:08:52.578683Z",
-     "iopub.status.busy": "2024-03-28T00:08:52.577956Z",
-     "iopub.status.idle": "2024-03-28T00:12:44.149130Z",
-     "shell.execute_reply": "2024-03-28T00:12:44.147764Z"
-    },
-    "id": "2eSvM9zX_2d3",
-    "papermill": {
-     "duration": 231.609576,
-     "end_time": "2024-03-28T00:12:44.151750",
-     "exception": false,
-     "start_time": "2024-03-28T00:08:52.542174",
-     "status": "completed"
-    },
-    "tags": []
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Looking in indexes: https://download.pytorch.org/whl/cu121\r\n",
-      "Collecting xformers\r\n",
-      "  Downloading https://download.pytorch.org/whl/cu121/xformers-0.0.25-cp310-cp310-manylinux2014_x86_64.whl (222.5 MB)\r\n",
-      "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m222.5/222.5 MB\u001b[0m \u001b[31m7.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\r\n",
-      "\u001b[?25hRequirement already satisfied: numpy in /opt/conda/lib/python3.10/site-packages (from xformers) (1.26.4)\r\n",
-      "Collecting torch==2.2.1 (from xformers)\r\n",
-      "  Downloading https://download.pytorch.org/whl/cu121/torch-2.2.1%2Bcu121-cp310-cp310-linux_x86_64.whl (757.3 MB)\r\n",
-      "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m757.3/757.3 MB\u001b[0m \u001b[31m2.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\r\n",
-      "\u001b[?25hRequirement already satisfied: filelock in /opt/conda/lib/python3.10/site-packages (from torch==2.2.1->xformers) (3.13.1)\r\n",
-      "Requirement already satisfied: typing-extensions>=4.8.0 in /opt/conda/lib/python3.10/site-packages (from torch==2.2.1->xformers) (4.9.0)\r\n",
-      "Requirement already satisfied: sympy in /opt/conda/lib/python3.10/site-packages (from torch==2.2.1->xformers) (1.12)\r\n",
-      "Requirement already satisfied: networkx in /opt/conda/lib/python3.10/site-packages (from torch==2.2.1->xformers) (3.2.1)\r\n",
-      "Requirement already satisfied: jinja2 in /opt/conda/lib/python3.10/site-packages (from torch==2.2.1->xformers) (3.1.2)\r\n",
-      "Requirement already satisfied: fsspec in /opt/conda/lib/python3.10/site-packages (from torch==2.2.1->xformers) (2024.3.0)\r\n",
-      "Collecting nvidia-cuda-nvrtc-cu12==12.1.105 (from torch==2.2.1->xformers)\r\n",
-      "  Downloading https://download.pytorch.org/whl/cu121/nvidia_cuda_nvrtc_cu12-12.1.105-py3-none-manylinux1_x86_64.whl (23.7 MB)\r\n",
-      "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m23.7/23.7 MB\u001b[0m \u001b[31m26.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\r\n",
-      "\u001b[?25hCollecting nvidia-cuda-runtime-cu12==12.1.105 (from torch==2.2.1->xformers)\r\n",
-      "  Downloading https://download.pytorch.org/whl/cu121/nvidia_cuda_runtime_cu12-12.1.105-py3-none-manylinux1_x86_64.whl (823 kB)\r\n",
-      "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m823.6/823.6 kB\u001b[0m \u001b[31m40.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\r\n",
-      "\u001b[?25hCollecting nvidia-cuda-cupti-cu12==12.1.105 (from torch==2.2.1->xformers)\r\n",
-      "  Downloading https://download.pytorch.org/whl/cu121/nvidia_cuda_cupti_cu12-12.1.105-py3-none-manylinux1_x86_64.whl (14.1 MB)\r\n",
-      "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m14.1/14.1 MB\u001b[0m \u001b[31m84.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\r\n",
-      "\u001b[?25hCollecting nvidia-cudnn-cu12==8.9.2.26 (from torch==2.2.1->xformers)\r\n",
-      "  Downloading https://download.pytorch.org/whl/cu121/nvidia_cudnn_cu12-8.9.2.26-py3-none-manylinux1_x86_64.whl (731.7 MB)\r\n",
-      "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m731.7/731.7 MB\u001b[0m \u001b[31m2.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\r\n",
-      "\u001b[?25hCollecting nvidia-cublas-cu12==12.1.3.1 (from torch==2.2.1->xformers)\r\n",
-      "  Downloading https://download.pytorch.org/whl/cu121/nvidia_cublas_cu12-12.1.3.1-py3-none-manylinux1_x86_64.whl (410.6 MB)\r\n",
-      "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m410.6/410.6 MB\u001b[0m \u001b[31m1.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\r\n",
-      "\u001b[?25hCollecting nvidia-cufft-cu12==11.0.2.54 (from torch==2.2.1->xformers)\r\n",
-      "  Downloading https://download.pytorch.org/whl/cu121/nvidia_cufft_cu12-11.0.2.54-py3-none-manylinux1_x86_64.whl (121.6 MB)\r\n",
-      "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m121.6/121.6 MB\u001b[0m \u001b[31m4.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\r\n",
-      "\u001b[?25hCollecting nvidia-curand-cu12==10.3.2.106 (from torch==2.2.1->xformers)\r\n",
-      "  Downloading https://download.pytorch.org/whl/cu121/nvidia_curand_cu12-10.3.2.106-py3-none-manylinux1_x86_64.whl (56.5 MB)\r\n",
-      "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m56.5/56.5 MB\u001b[0m \u001b[31m28.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\r\n",
-      "\u001b[?25hCollecting nvidia-cusolver-cu12==11.4.5.107 (from torch==2.2.1->xformers)\r\n",
-      "  Downloading https://download.pytorch.org/whl/cu121/nvidia_cusolver_cu12-11.4.5.107-py3-none-manylinux1_x86_64.whl (124.2 MB)\r\n",
-      "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m124.2/124.2 MB\u001b[0m \u001b[31m12.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\r\n",
-      "\u001b[?25hCollecting nvidia-cusparse-cu12==12.1.0.106 (from torch==2.2.1->xformers)\r\n",
-      "  Downloading https://download.pytorch.org/whl/cu121/nvidia_cusparse_cu12-12.1.0.106-py3-none-manylinux1_x86_64.whl (196.0 MB)\r\n",
-      "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m196.0/196.0 MB\u001b[0m \u001b[31m8.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\r\n",
-      "\u001b[?25hCollecting nvidia-nccl-cu12==2.19.3 (from torch==2.2.1->xformers)\r\n",
-      "  Downloading https://download.pytorch.org/whl/cu121/nvidia_nccl_cu12-2.19.3-py3-none-manylinux1_x86_64.whl (166.0 MB)\r\n",
-      "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m166.0/166.0 MB\u001b[0m \u001b[31m9.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\r\n",
-      "\u001b[?25hCollecting nvidia-nvtx-cu12==12.1.105 (from torch==2.2.1->xformers)\r\n",
-      "  Downloading https://download.pytorch.org/whl/cu121/nvidia_nvtx_cu12-12.1.105-py3-none-manylinux1_x86_64.whl (99 kB)\r\n",
-      "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m99.1/99.1 kB\u001b[0m \u001b[31m6.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\r\n",
-      "\u001b[?25hCollecting triton==2.2.0 (from torch==2.2.1->xformers)\r\n",
-      "  Downloading https://download.pytorch.org/whl/triton-2.2.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (167.9 MB)\r\n",
-      "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m167.9/167.9 MB\u001b[0m \u001b[31m8.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\r\n",
-      "\u001b[?25hCollecting nvidia-nvjitlink-cu12 (from nvidia-cusolver-cu12==11.4.5.107->torch==2.2.1->xformers)\r\n",
-      "  Downloading https://download.pytorch.org/whl/cu121/nvidia_nvjitlink_cu12-12.1.105-py3-none-manylinux1_x86_64.whl (19.8 MB)\r\n",
-      "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m19.8/19.8 MB\u001b[0m \u001b[31m46.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\r\n",
-      "\u001b[?25hRequirement already satisfied: MarkupSafe>=2.0 in /opt/conda/lib/python3.10/site-packages (from jinja2->torch==2.2.1->xformers) (2.1.3)\r\n",
-      "Requirement already satisfied: mpmath>=0.19 in /opt/conda/lib/python3.10/site-packages (from sympy->torch==2.2.1->xformers) (1.3.0)\r\n",
-      "Installing collected packages: triton, nvidia-nvtx-cu12, nvidia-nvjitlink-cu12, nvidia-nccl-cu12, nvidia-curand-cu12, nvidia-cufft-cu12, nvidia-cuda-runtime-cu12, nvidia-cuda-nvrtc-cu12, nvidia-cuda-cupti-cu12, nvidia-cublas-cu12, nvidia-cusparse-cu12, nvidia-cudnn-cu12, nvidia-cusolver-cu12, torch, xformers\r\n",
-      "  Attempting uninstall: torch\r\n",
-      "    Found existing installation: torch 2.1.2\r\n",
-      "    Uninstalling torch-2.1.2:\r\n",
-      "      Successfully uninstalled torch-2.1.2\r\n",
-      "Successfully installed nvidia-cublas-cu12-12.1.3.1 nvidia-cuda-cupti-cu12-12.1.105 nvidia-cuda-nvrtc-cu12-12.1.105 nvidia-cuda-runtime-cu12-12.1.105 nvidia-cudnn-cu12-8.9.2.26 nvidia-cufft-cu12-11.0.2.54 nvidia-curand-cu12-10.3.2.106 nvidia-cusolver-cu12-11.4.5.107 nvidia-cusparse-cu12-12.1.0.106 nvidia-nccl-cu12-2.19.3 nvidia-nvjitlink-cu12-12.1.105 nvidia-nvtx-cu12-12.1.105 torch-2.2.1+cu121 triton-2.2.0 xformers-0.0.25\r\n",
-      "Collecting unsloth@ git+https://github.com/unslothai/unsloth.git (from unsloth[kaggle-new]@ git+https://github.com/unslothai/unsloth.git)\r\n",
-      "  Cloning https://github.com/unslothai/unsloth.git to /tmp/pip-install-yfpl0t85/unsloth_63990d9a4b8e4d6ca74bbfccdc6198cb\r\n",
-      "  Running command git clone --filter=blob:none --quiet https://github.com/unslothai/unsloth.git /tmp/pip-install-yfpl0t85/unsloth_63990d9a4b8e4d6ca74bbfccdc6198cb\r\n",
-      "  Resolved https://github.com/unslothai/unsloth.git to commit a68aebc1fa17755ffbcdafc9239e7ca37ab21657\r\n",
-      "  Installing build dependencies ... \u001b[?25l-\b \b\\\b \b|\b \b/\b \b-\b \b\\\b \b|\b \b/\b \b-\b \bdone\r\n",
-      "\u001b[?25h  Getting requirements to build wheel ... \u001b[?25l-\b \b\\\b \bdone\r\n",
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-      "Collecting tyro (from unsloth@ git+https://github.com/unslothai/unsloth.git->unsloth[kaggle-new]@ git+https://github.com/unslothai/unsloth.git)\r\n",
-      "  Downloading tyro-0.7.3-py3-none-any.whl.metadata (7.7 kB)\r\n",
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-      "Collecting trl>=0.7.9 (from unsloth@ git+https://github.com/unslothai/unsloth.git->unsloth[kaggle-new]@ git+https://github.com/unslothai/unsloth.git)\r\n",
-      "  Downloading trl-0.8.1-py3-none-any.whl.metadata (11 kB)\r\n",
-      "Collecting peft>=0.7.1 (from unsloth@ git+https://github.com/unslothai/unsloth.git->unsloth[kaggle-new]@ git+https://github.com/unslothai/unsloth.git)\r\n",
-      "  Downloading peft-0.10.0-py3-none-any.whl.metadata (13 kB)\r\n",
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-      "Requirement already satisfied: nvidia-cuda-nvrtc-cu12==12.1.105 in /opt/conda/lib/python3.10/site-packages (from torch->bitsandbytes->unsloth@ git+https://github.com/unslothai/unsloth.git->unsloth[kaggle-new]@ git+https://github.com/unslothai/unsloth.git) (12.1.105)\r\n",
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-      "Requirement already satisfied: nvidia-cudnn-cu12==8.9.2.26 in /opt/conda/lib/python3.10/site-packages (from torch->bitsandbytes->unsloth@ git+https://github.com/unslothai/unsloth.git->unsloth[kaggle-new]@ git+https://github.com/unslothai/unsloth.git) (8.9.2.26)\r\n",
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-      "Requirement already satisfied: nvidia-cufft-cu12==11.0.2.54 in /opt/conda/lib/python3.10/site-packages (from torch->bitsandbytes->unsloth@ git+https://github.com/unslothai/unsloth.git->unsloth[kaggle-new]@ git+https://github.com/unslothai/unsloth.git) (11.0.2.54)\r\n",
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-      "Requirement already satisfied: nvidia-cusparse-cu12==12.1.0.106 in /opt/conda/lib/python3.10/site-packages (from torch->bitsandbytes->unsloth@ git+https://github.com/unslothai/unsloth.git->unsloth[kaggle-new]@ git+https://github.com/unslothai/unsloth.git) (12.1.0.106)\r\n",
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-      "Requirement already satisfied: nvidia-nvjitlink-cu12 in /opt/conda/lib/python3.10/site-packages (from nvidia-cusolver-cu12==11.4.5.107->torch->bitsandbytes->unsloth@ git+https://github.com/unslothai/unsloth.git->unsloth[kaggle-new]@ git+https://github.com/unslothai/unsloth.git) (12.1.105)\r\n",
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-      "Requirement already satisfied: rich>=11.1.0 in /opt/conda/lib/python3.10/site-packages (from tyro->unsloth@ git+https://github.com/unslothai/unsloth.git->unsloth[kaggle-new]@ git+https://github.com/unslothai/unsloth.git) (13.7.0)\r\n",
-      "Collecting shtab>=1.5.6 (from tyro->unsloth@ git+https://github.com/unslothai/unsloth.git->unsloth[kaggle-new]@ git+https://github.com/unslothai/unsloth.git)\r\n",
-      "  Downloading shtab-1.7.1-py3-none-any.whl.metadata (7.3 kB)\r\n",
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-      "\u001b[?25hDownloading shtab-1.7.1-py3-none-any.whl (14 kB)\r\n",
-      "Building wheels for collected packages: unsloth\r\n",
-      "  Building wheel for unsloth (pyproject.toml) ... \u001b[?25l-\b \b\\\b \b|\b \bdone\r\n",
-      "\u001b[?25h  Created wheel for unsloth: filename=unsloth-2024.3-py3-none-any.whl size=93934 sha256=34861411793a48098b4d9e04f35bc2ce841bfae25a980dd6ce151eecc1321a1a\r\n",
-      "  Stored in directory: /tmp/pip-ephem-wheel-cache-6kf3ks_c/wheels/ed/d4/e9/76fb290ee3df0a5fc21ce5c2c788e29e9607a2353d8342fd0d\r\n",
-      "Successfully built unsloth\r\n",
-      "Installing collected packages: unsloth, shtab, pyarrow-hotfix, pyarrow, fsspec, tyro, datasets, bitsandbytes, trl, peft\r\n",
-      "  Attempting uninstall: pyarrow\r\n",
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-      "    Uninstalling pyarrow-11.0.0:\r\n",
-      "      Successfully uninstalled pyarrow-11.0.0\r\n",
-      "  Attempting uninstall: fsspec\r\n",
-      "    Found existing installation: fsspec 2024.3.0\r\n",
-      "    Uninstalling fsspec-2024.3.0:\r\n",
-      "      Successfully uninstalled fsspec-2024.3.0\r\n",
-      "  Attempting uninstall: datasets\r\n",
-      "    Found existing installation: datasets 2.1.0\r\n",
-      "    Uninstalling datasets-2.1.0:\r\n",
-      "      Successfully uninstalled datasets-2.1.0\r\n",
-      "\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 4.1.5 which is incompatible.\r\n",
-      "cudf 23.8.0 requires cuda-python<12.0a0,>=11.7.1, but you have cuda-python 12.4.0 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 2024.3.1 which is incompatible.\r\n",
-      "dask-cuda 23.8.0 requires dask==2023.7.1, but you have dask 2024.3.1 which is incompatible.\r\n",
-      "dask-cuda 23.8.0 requires pandas<1.6.0dev0,>=1.3, but you have pandas 2.1.4 which is incompatible.\r\n",
-      "dask-cudf 23.8.0 requires dask==2023.7.1, but you have dask 2024.3.1 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.\r\n",
-      "distributed 2023.7.1 requires dask==2023.7.1, but you have dask 2024.3.1 which is incompatible.\r\n",
-      "gcsfs 2023.12.2.post1 requires fsspec==2023.12.2, but you have fsspec 2023.10.0 which is incompatible.\r\n",
-      "raft-dask 23.8.0 requires dask==2023.7.1, but you have dask 2024.3.1 which is incompatible.\r\n",
-      "s3fs 2024.3.0 requires fsspec==2024.3.0, but you have fsspec 2023.10.0 which is incompatible.\u001b[0m\u001b[31m\r\n",
-      "\u001b[0mSuccessfully installed bitsandbytes-0.43.0 datasets-2.17.1 fsspec-2023.10.0 peft-0.10.0 pyarrow-15.0.2 pyarrow-hotfix-0.6 shtab-1.7.1 trl-0.8.1 tyro-0.7.3 unsloth-2024.3\r\n"
-     ]
-    }
-   ],
-   "source": [
-    "#%%capture\n",
-    "#import torch\n",
-    "#major_version, minor_version = torch.cuda.get_device_capability()\n",
-    "\n",
-    "!pip install -U xformers --index-url https://download.pytorch.org/whl/cu121\n",
-    "!pip install \"unsloth[kaggle-new] @ git+https://github.com/unslothai/unsloth.git\" triton datasets==2.17.1\n",
-    "#if major_version >= 8:\n",
-    "#    # Use this for new GPUs like Ampere, Hopper GPUs (RTX 30xx, RTX 40xx, A100, H100, L40)\n",
-    "#    !pip install --no-deps packaging ninja einops flash-attn xformers trl peft accelerate bitsandbytes\n",
-    "#else:\n",
-    "#    # Use this for older GPUs (V100, Tesla T4, RTX 20xx)\n",
-    "#    !pip install --no-deps xformers trl peft accelerate bitsandbytes\n",
-    "\n",
-    "import os\n",
-    "os.environ[\"WANDB_DISABLED\"] = \"true\""
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "c963a9d2",
-   "metadata": {
-    "id": "r2v_X2fA0Df5",
-    "papermill": {
-     "duration": 0.123192,
-     "end_time": "2024-03-28T00:12:44.398766",
-     "exception": false,
-     "start_time": "2024-03-28T00:12:44.275574",
-     "status": "completed"
-    },
-    "tags": []
-   },
-   "source": [
-    "* We support Llama, Mistral, CodeLlama, TinyLlama, Vicuna, Open Hermes etc\n",
-    "* And Yi, Qwen ([llamafied](https://huggingface.co/models?sort=trending&search=qwen+llama)), Deepseek, all Llama, Mistral derived archs.\n",
-    "* We support 16bit LoRA or 4bit QLoRA. Both 2x faster.\n",
-    "* `max_seq_length` can be set to anything, since we do automatic RoPE Scaling via [kaiokendev's](https://kaiokendev.github.io/til) method.\n",
-    "* With [PR 26037](https://github.com/huggingface/transformers/pull/26037), we support downloading 4bit models **4x faster**! [Our repo](https://huggingface.co/unsloth) has Llama, Mistral 4bit models.\n",
-    "* [**NEW**] We make Gemma 6 trillion tokens **2.5x faster**! See our [Gemma notebook](https://colab.research.google.com/drive/10NbwlsRChbma1v55m8LAPYG15uQv6HLo?usp=sharing)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "88a40779",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-03-28T00:12:44.654372Z",
-     "iopub.status.busy": "2024-03-28T00:12:44.653667Z",
-     "iopub.status.idle": "2024-03-28T00:13:38.483179Z",
-     "shell.execute_reply": "2024-03-28T00:13:38.482338Z"
-    },
-    "id": "QmUBVEnvCDJv",
-    "outputId": "40383ec5-b379-4fcd-ba5c-b5656b0ff129",
-    "papermill": {
-     "duration": 53.95967,
-     "end_time": "2024-03-28T00:13:38.485764",
-     "exception": false,
-     "start_time": "2024-03-28T00:12:44.526094",
-     "status": "completed"
-    },
-    "tags": []
-   },
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "58648d0ab785418089b24914c46df7a4",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "config.json:   0%|          | 0.00/1.05k [00:00<?, ?B/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "==((====))==  Unsloth: Fast Mistral patching release 2024.3\n",
-      "   \\\\   /|    GPU: Tesla T4. Max memory: 14.748 GB. Platform = Linux.\n",
-      "O^O/ \\_/ \\    Pytorch: 2.2.1+cu121. CUDA = 7.5. CUDA Toolkit = 12.1.\n",
-      "\\        /    Bfloat16 = FALSE. Xformers = 0.0.25. FA = False.\n",
-      " \"-____-\"     Free Apache license: http://github.com/unslothai/unsloth\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "0291f4d1f4734954946a71afef1e1519",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "model.safetensors:   0%|          | 0.00/4.13G [00:00<?, ?B/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
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-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "generation_config.json:   0%|          | 0.00/116 [00:00<?, ?B/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "9aeb5170934f4e7da9748e8859e40e30",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "tokenizer_config.json:   0%|          | 0.00/971 [00:00<?, ?B/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
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-       "version_minor": 0
-      },
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-       "tokenizer.model:   0%|          | 0.00/493k [00:00<?, ?B/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "59233cb5bb5849d0800cde9d3c129184",
-       "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"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "84af480c5f6c4ad490074ef103af5628",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
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-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "2024-03-28 00:13:27.566798: 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",
-      "2024-03-28 00:13:27.566934: 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",
-      "2024-03-28 00:13:27.741422: 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": [
-    "from unsloth import FastLanguageModel\n",
-    "import torch\n",
-    "max_seq_length = 2048 # Choose any! We auto support RoPE Scaling internally!\n",
-    "dtype = None # None for auto detection. Float16 for Tesla T4, V100, Bfloat16 for Ampere+\n",
-    "load_in_4bit = True # Use 4bit quantization to reduce memory usage. Can be False.\n",
-    "\n",
-    "# 4bit pre quantized models we support for 4x faster downloading + no OOMs.\n",
-    "fourbit_models = [\n",
-    "    \"unsloth/mistral-7b-bnb-4bit\",\n",
-    "    \"unsloth/mistral-7b-instruct-v0.2-bnb-4bit\",\n",
-    "    \"unsloth/llama-2-7b-bnb-4bit\",\n",
-    "    \"unsloth/llama-2-13b-bnb-4bit\",\n",
-    "    \"unsloth/codellama-34b-bnb-4bit\",\n",
-    "    \"unsloth/tinyllama-bnb-4bit\",\n",
-    "    \"unsloth/gemma-7b-bnb-4bit\", # New Google 6 trillion tokens model 2.5x faster!\n",
-    "    \"unsloth/gemma-2b-bnb-4bit\",\n",
-    "] # More models at https://huggingface.co/unsloth\n",
-    "\n",
-    "model, tokenizer = FastLanguageModel.from_pretrained(\n",
-    "    model_name = \"unsloth/mistral-7b-bnb-4bit\", # Choose ANY! eg teknium/OpenHermes-2.5-Mistral-7B\n",
-    "    max_seq_length = max_seq_length,\n",
-    "    dtype = dtype,\n",
-    "    load_in_4bit = load_in_4bit,\n",
-    "    # token = \"hf_...\", # use one if using gated models like meta-llama/Llama-2-7b-hf\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "ca908244",
-   "metadata": {
-    "id": "SXd9bTZd1aaL",
-    "papermill": {
-     "duration": 0.12735,
-     "end_time": "2024-03-28T00:13:38.741441",
-     "exception": false,
-     "start_time": "2024-03-28T00:13:38.614091",
-     "status": "completed"
-    },
-    "tags": []
-   },
-   "source": [
-    "We now add LoRA adapters so we only need to update 1 to 10% of all parameters!"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "9a50c1ab",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-03-28T00:13:38.993225Z",
-     "iopub.status.busy": "2024-03-28T00:13:38.992478Z",
-     "iopub.status.idle": "2024-03-28T00:13:39.865675Z",
-     "shell.execute_reply": "2024-03-28T00:13:39.864586Z"
-    },
-    "id": "6bZsfBuZDeCL",
-    "outputId": "4c986b9b-ee42-48d6-ba35-6a709e919c82",
-    "papermill": {
-     "duration": 1.001126,
-     "end_time": "2024-03-28T00:13:39.869351",
-     "exception": false,
-     "start_time": "2024-03-28T00:13:38.868225",
-     "status": "completed"
-    },
-    "tags": []
-   },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Unsloth 2024.3 patched 32 layers with 32 QKV layers, 32 O layers and 32 MLP layers.\n"
-     ]
-    }
-   ],
-   "source": [
-    "model = FastLanguageModel.get_peft_model(\n",
-    "    model,\n",
-    "    r = 16, # Choose any number > 0 ! Suggested 8, 16, 32, 64, 128\n",
-    "    target_modules = [\"q_proj\", \"k_proj\", \"v_proj\", \"o_proj\",\n",
-    "                      \"gate_proj\", \"up_proj\", \"down_proj\",],\n",
-    "    lora_alpha = 16,\n",
-    "    lora_dropout = 0, # Supports any, but = 0 is optimized\n",
-    "    bias = \"none\",    # Supports any, but = \"none\" is optimized\n",
-    "    use_gradient_checkpointing = True,\n",
-    "    random_state = 3407,\n",
-    "    use_rslora = False,  # We support rank stabilized LoRA\n",
-    "    loftq_config = None, # And LoftQ\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "0b7c4848",
-   "metadata": {
-    "id": "vITh0KVJ10qX",
-    "papermill": {
-     "duration": 0.124172,
-     "end_time": "2024-03-28T00:13:40.129776",
-     "exception": false,
-     "start_time": "2024-03-28T00:13:40.005604",
-     "status": "completed"
-    },
-    "tags": []
-   },
-   "source": [
-    "<a name=\"Data\"></a>\n",
-    "### Data Prep\n",
-    "We now use the `ChatML` format for conversation style finetunes. We use [Open Assistant conversations](https://huggingface.co/datasets/philschmid/guanaco-sharegpt-style) in ShareGPT style. ChatML renders multi turn conversations like below:\n",
-    "\n",
-    "```\n",
-    "<|im_start|>system\n",
-    "You are a helpful assistant.<|im_end|>\n",
-    "<|im_start|>user\n",
-    "What's the capital of France?<|im_end|>\n",
-    "<|im_start|>assistant\n",
-    "Paris.\n",
-    "```\n",
-    "\n",
-    "**[NOTE]** To train only on completions (ignoring the user's input) read TRL's docs [here](https://huggingface.co/docs/trl/sft_trainer#train-on-completions-only).\n",
-    "\n",
-    "We use our `get_chat_template` function to get the correct chat template. We support `zephyr, chatml, mistral, llama, alpaca, vicuna, vicuna_old` and our own optimized `unsloth` template.\n",
-    "\n",
-    "Normally one has to train `<|im_start|>` and `<|im_end|>`. We instead map `<|im_end|>` to be the EOS token, and leave `<|im_start|>` as is. This requires no additional training of additional tokens.\n",
-    "\n",
-    "Note ShareGPT uses `{\"from\": \"human\", \"value\" : \"Hi\"}` and not `{\"role\": \"user\", \"content\" : \"Hi\"}`, so we use `mapping` to map it.\n",
-    "\n",
-    "For text completions like novel writing, try this [notebook](https://colab.research.google.com/drive/1ef-tab5bhkvWmBOObepl1WgJvfvSzn5Q?usp=sharing)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "0d33d99d",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-03-28T00:13:40.375057Z",
-     "iopub.status.busy": "2024-03-28T00:13:40.374718Z",
-     "iopub.status.idle": "2024-03-28T00:13:41.633504Z",
-     "shell.execute_reply": "2024-03-28T00:13:41.632366Z"
-    },
-    "id": "LjY75GoYUCB8",
-    "outputId": "50c7b539-b750-4964-fa4a-45a99d5923f1",
-    "papermill": {
-     "duration": 1.382761,
-     "end_time": "2024-03-28T00:13:41.635817",
-     "exception": false,
-     "start_time": "2024-03-28T00:13:40.253056",
-     "status": "completed"
-    },
-    "tags": []
-   },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Unsloth: Will map <|im_end|> to EOS = </s>.\n"
-     ]
-    }
-   ],
-   "source": [
-    "from datasets import load_dataset\n",
-    "import json\n",
-    "from unsloth.chat_templates import get_chat_template\n",
-    "\n",
-    "tokenizer = get_chat_template(\n",
-    "    tokenizer,\n",
-    "    chat_template = \"chatml\", # Supports zephyr, chatml, mistral, llama, alpaca, vicuna, vicuna_old, unsloth\n",
-    "    #mapping = {\"role\" : \"from\", \"content\" : \"value\", \"user\" : \"human\", \"assistant\" : \"gpt\"}, # ShareGPT style\n",
-    "    map_eos_token = True, # Maps <|im_end|> to </s> instead\n",
-    ")\n",
-    "\n",
-    "def formatting_prompts_func(convos):\n",
-    "    texts = [tokenizer.apply_chat_template(convo, tokenize = False, add_generation_prompt = False) for convo in convos]\n",
-    "    return { \"text\" : texts, }\n",
-    "\n",
-    "with open(\"/kaggle/input/the-group-chat/output-10k-c.json\") as chatfile:\n",
-    "    convos = [json.loads(j) for j in chatfile.readlines()]\n",
-    "\n",
-    "dataset = formatting_prompts_func(convos)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "f75a3f33",
-   "metadata": {
-    "id": "cHiVoToneynS",
-    "papermill": {
-     "duration": 0.127199,
-     "end_time": "2024-03-28T00:13:41.890438",
-     "exception": false,
-     "start_time": "2024-03-28T00:13:41.763239",
-     "status": "completed"
-    },
-    "tags": []
-   },
-   "source": [
-    "Let's see how the `ChatML` format works by printing the 5th element"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "08ef098f",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-03-28T00:13:42.144988Z",
-     "iopub.status.busy": "2024-03-28T00:13:42.144281Z",
-     "iopub.status.idle": "2024-03-28T00:13:42.148878Z",
-     "shell.execute_reply": "2024-03-28T00:13:42.147833Z"
-    },
-    "id": "U5iEWrUkevpE",
-    "outputId": "e28b6889-29f9-400f-a08c-5fc7d5cbc5db",
-    "papermill": {
-     "duration": 0.133687,
-     "end_time": "2024-03-28T00:13:42.150735",
-     "exception": false,
-     "start_time": "2024-03-28T00:13:42.017048",
-     "status": "completed"
-    },
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "#dataset[5][\"conversations\"]\n",
-    "#print(dataset[\"text\"])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "a77a6d20",
-   "metadata": {
-    "id": "GuKOAUDpUeDL",
-    "papermill": {
-     "duration": 0.121878,
-     "end_time": "2024-03-28T00:13:42.399195",
-     "exception": false,
-     "start_time": "2024-03-28T00:13:42.277317",
-     "status": "completed"
-    },
-    "tags": []
-   },
-   "source": [
-    "If you're looking to make your own chat template, that also is possible! You must use the Jinja templating regime. We provide our own stripped down version of the `Unsloth template` which we find to be more efficient, and leverages ChatML, Zephyr and Alpaca styles.\n",
-    "\n",
-    "More info on chat templates on [our wiki page!](https://github.com/unslothai/unsloth/wiki#chat-templates)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "cdd24991",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-03-28T00:13:42.653294Z",
-     "iopub.status.busy": "2024-03-28T00:13:42.652894Z",
-     "iopub.status.idle": "2024-03-28T00:13:42.658835Z",
-     "shell.execute_reply": "2024-03-28T00:13:42.657902Z"
-    },
-    "id": "p31Z-S6FUieB",
-    "papermill": {
-     "duration": 0.136303,
-     "end_time": "2024-03-28T00:13:42.660931",
-     "exception": false,
-     "start_time": "2024-03-28T00:13:42.524628",
-     "status": "completed"
-    },
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "unsloth_template = \\\n",
-    "    \"{{ bos_token }}\"\\\n",
-    "    \"{{ 'You are a helpful assistant to the user\\n' }}\"\\\n",
-    "    \"{% endif %}\"\\\n",
-    "    \"{% for message in messages %}\"\\\n",
-    "        \"{% if message['role'] == 'user' %}\"\\\n",
-    "            \"{{ '>>> User: ' + message['content'] + '\\n' }}\"\\\n",
-    "        \"{% elif message['role'] == 'assistant' %}\"\\\n",
-    "            \"{{ '>>> Assistant: ' + message['content'] + eos_token + '\\n' }}\"\\\n",
-    "        \"{% endif %}\"\\\n",
-    "    \"{% endfor %}\"\\\n",
-    "    \"{% if add_generation_prompt %}\"\\\n",
-    "        \"{{ '>>> Assistant: ' }}\"\\\n",
-    "    \"{% endif %}\"\n",
-    "unsloth_eos_token = \"eos_token\"\n",
-    "\n",
-    "if False:\n",
-    "    tokenizer = get_chat_template(\n",
-    "        tokenizer,\n",
-    "        chat_template = (unsloth_template, unsloth_eos_token,), # You must provide a template and EOS token\n",
-    "        mapping = {\"role\" : \"from\", \"content\" : \"value\", \"user\" : \"human\", \"assistant\" : \"gpt\"}, # ShareGPT style\n",
-    "        map_eos_token = True, # Maps <|im_end|> to </s> instead\n",
-    "    )"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "44e5c271",
-   "metadata": {
-    "id": "idAEIeSQ3xdS",
-    "papermill": {
-     "duration": 0.127599,
-     "end_time": "2024-03-28T00:13:42.915115",
-     "exception": false,
-     "start_time": "2024-03-28T00:13:42.787516",
-     "status": "completed"
-    },
-    "tags": []
-   },
-   "source": [
-    "<a name=\"Train\"></a>\n",
-    "### Train the model\n",
-    "Now let's use Huggingface TRL's `SFTTrainer`! More docs here: [TRL SFT docs](https://huggingface.co/docs/trl/sft_trainer). We do 60 steps to speed things up, but you can set `num_train_epochs=1` for a full run, and turn off `max_steps=None`. We also support TRL's `DPOTrainer`!"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "84d94e51",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-03-28T00:13:43.163495Z",
-     "iopub.status.busy": "2024-03-28T00:13:43.162623Z",
-     "iopub.status.idle": "2024-03-28T00:13:43.243458Z",
-     "shell.execute_reply": "2024-03-28T00:13:43.242622Z"
-    },
-    "papermill": {
-     "duration": 0.20747,
-     "end_time": "2024-03-28T00:13:43.245965",
-     "exception": false,
-     "start_time": "2024-03-28T00:13:43.038495",
-     "status": "completed"
-    },
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "from datasets import Dataset\n",
-    "dataset = Dataset.from_dict(dataset)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "099afa9e",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-03-28T00:13:43.492984Z",
-     "iopub.status.busy": "2024-03-28T00:13:43.492622Z",
-     "iopub.status.idle": "2024-03-28T00:13:48.324291Z",
-     "shell.execute_reply": "2024-03-28T00:13:48.323307Z"
-    },
-    "id": "95_Nn-89DhsL",
-    "outputId": "c13d3e90-5342-4535-9541-98f9120dfe2b",
-    "papermill": {
-     "duration": 4.95752,
-     "end_time": "2024-03-28T00:13:48.326701",
-     "exception": false,
-     "start_time": "2024-03-28T00:13:43.369181",
-     "status": "completed"
-    },
-    "tags": []
-   },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Using the `WANDB_DISABLED` environment variable is deprecated and will be removed in v5. Use the --report_to flag to control the integrations used for logging result (for instance --report_to none).\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "e363d483a5134f5d873c11f936d2d9f5",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Map (num_proc=2):   0%|          | 0/10000 [00:00<?, ? examples/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "from trl import SFTTrainer\n",
-    "from transformers import TrainingArguments\n",
-    "\n",
-    "trainer = SFTTrainer(\n",
-    "    model = model,\n",
-    "    tokenizer = tokenizer,\n",
-    "    train_dataset = dataset,\n",
-    "    dataset_text_field = \"text\",\n",
-    "    max_seq_length = max_seq_length,\n",
-    "    dataset_num_proc = 2,\n",
-    "    packing = False, # Can make training 5x faster for short sequences.\n",
-    "    args = TrainingArguments(\n",
-    "        per_device_train_batch_size = 2,\n",
-    "        gradient_accumulation_steps = 4,\n",
-    "        warmup_steps = 5,\n",
-    "        num_train_epochs=1,\n",
-    "        learning_rate = 2e-4,\n",
-    "        fp16 = not torch.cuda.is_bf16_supported(),\n",
-    "        bf16 = torch.cuda.is_bf16_supported(),\n",
-    "        logging_steps = 1,\n",
-    "        optim = \"adamw_8bit\",\n",
-    "        weight_decay = 0.01,\n",
-    "        lr_scheduler_type = \"linear\",\n",
-    "        seed = 3407,\n",
-    "        output_dir = \"outputs\",\n",
-    "    ),\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "id": "56281856",
-   "metadata": {
-    "cellView": "form",
-    "execution": {
-     "iopub.execute_input": "2024-03-28T00:13:48.575758Z",
-     "iopub.status.busy": "2024-03-28T00:13:48.575334Z",
-     "iopub.status.idle": "2024-03-28T00:13:48.582620Z",
-     "shell.execute_reply": "2024-03-28T00:13:48.581689Z"
-    },
-    "id": "2ejIt2xSNKKp",
-    "outputId": "a537db02-e673-44da-8889-5fa95a5e2d51",
-    "papermill": {
-     "duration": 0.137429,
-     "end_time": "2024-03-28T00:13:48.585471",
-     "exception": false,
-     "start_time": "2024-03-28T00:13:48.448042",
-     "status": "completed"
-    },
-    "tags": []
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "GPU = Tesla T4. Max memory = 14.748 GB.\n",
-      "4.5 GB of memory reserved.\n"
-     ]
-    }
-   ],
-   "source": [
-    "#@title Show current memory stats\n",
-    "gpu_stats = torch.cuda.get_device_properties(0)\n",
-    "start_gpu_memory = round(torch.cuda.max_memory_reserved() / 1024 / 1024 / 1024, 3)\n",
-    "max_memory = round(gpu_stats.total_memory / 1024 / 1024 / 1024, 3)\n",
-    "print(f\"GPU = {gpu_stats.name}. Max memory = {max_memory} GB.\")\n",
-    "print(f\"{start_gpu_memory} GB of memory reserved.\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "id": "a4e1702c",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-03-28T00:13:48.854943Z",
-     "iopub.status.busy": "2024-03-28T00:13:48.854292Z",
-     "iopub.status.idle": "2024-03-28T03:52:49.428064Z",
-     "shell.execute_reply": "2024-03-28T03:52:49.427099Z"
-    },
-    "id": "yqxqAZ7KJ4oL",
-    "outputId": "db7bae40-bf0a-4908-8867-a5dfe933e1f3",
-    "papermill": {
-     "duration": 13140.716117,
-     "end_time": "2024-03-28T03:52:49.430510",
-     "exception": false,
-     "start_time": "2024-03-28T00:13:48.714393",
-     "status": "completed"
-    },
-    "tags": []
-   },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "==((====))==  Unsloth - 2x faster free finetuning | Num GPUs = 1\n",
-      "   \\\\   /|    Num examples = 10,000 | Num Epochs = 1\n",
-      "O^O/ \\_/ \\    Batch size per device = 2 | Gradient Accumulation steps = 4\n",
-      "\\        /    Total batch size = 8 | Total steps = 1,250\n",
-      " \"-____-\"     Number of trainable parameters = 41,943,040\n"
-     ]
-    },
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "    <div>\n",
-       "      \n",
-       "      <progress value='1250' max='1250' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
-       "      [1250/1250 3:38:46, Epoch 1/1]\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>1</td>\n",
-       "      <td>2.415600</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>2</td>\n",
-       "      <td>2.560600</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>3</td>\n",
-       "      <td>2.358100</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>4</td>\n",
-       "      <td>2.018800</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>5</td>\n",
-       "      <td>1.869800</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>6</td>\n",
-       "      <td>1.859900</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>7</td>\n",
-       "      <td>1.855700</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>8</td>\n",
-       "      <td>1.985000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>9</td>\n",
-       "      <td>1.739100</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>10</td>\n",
-       "      <td>1.857900</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>11</td>\n",
-       "      <td>1.858300</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>12</td>\n",
-       "      <td>1.574900</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>13</td>\n",
-       "      <td>1.680000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>14</td>\n",
-       "      <td>1.615100</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>15</td>\n",
-       "      <td>1.720000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>16</td>\n",
-       "      <td>1.731600</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>17</td>\n",
-       "      <td>1.727100</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>18</td>\n",
-       "      <td>1.587100</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>19</td>\n",
-       "      <td>1.579300</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>20</td>\n",
-       "      <td>1.642300</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>21</td>\n",
-       "      <td>1.487200</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>22</td>\n",
-       "      <td>1.585400</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>23</td>\n",
-       "      <td>1.611900</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>24</td>\n",
-       "      <td>1.598700</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>25</td>\n",
-       "      <td>1.617600</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>26</td>\n",
-       "      <td>1.511700</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>27</td>\n",
-       "      <td>1.805500</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>28</td>\n",
-       "      <td>1.569000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>29</td>\n",
-       "      <td>1.652700</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>30</td>\n",
-       "      <td>1.421700</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>31</td>\n",
-       "      <td>1.666500</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>32</td>\n",
-       "      <td>1.633400</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>33</td>\n",
-       "      <td>1.630900</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>34</td>\n",
-       "      <td>1.744100</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>35</td>\n",
-       "      <td>1.577500</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>36</td>\n",
-       "      <td>1.665400</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>37</td>\n",
-       "      <td>1.569500</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>38</td>\n",
-       "      <td>1.597500</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>39</td>\n",
-       "      <td>1.703800</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>40</td>\n",
-       "      <td>1.556500</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>41</td>\n",
-       "      <td>1.451800</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>42</td>\n",
-       "      <td>1.629500</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>43</td>\n",
-       "      <td>1.538500</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>44</td>\n",
-       "      <td>1.508600</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>45</td>\n",
-       "      <td>1.439400</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>46</td>\n",
-       "      <td>1.590000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>47</td>\n",
-       "      <td>1.568200</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>48</td>\n",
-       "      <td>1.554900</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>49</td>\n",
-       "      <td>1.486900</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>50</td>\n",
-       "      <td>1.617100</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>51</td>\n",
-       "      <td>1.695700</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>52</td>\n",
-       "      <td>1.470600</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>53</td>\n",
-       "      <td>1.680400</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>54</td>\n",
-       "      <td>1.605500</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>55</td>\n",
-       "      <td>1.472900</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>56</td>\n",
-       "      <td>1.636600</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>57</td>\n",
-       "      <td>1.527600</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>58</td>\n",
-       "      <td>1.579300</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>59</td>\n",
-       "      <td>1.551700</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>60</td>\n",
-       "      <td>1.503900</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>61</td>\n",
-       "      <td>1.364500</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>62</td>\n",
-       "      <td>1.575300</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>63</td>\n",
-       "      <td>1.516700</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>64</td>\n",
-       "      <td>1.632000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>65</td>\n",
-       "      <td>1.430900</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>66</td>\n",
-       "      <td>1.542000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>67</td>\n",
-       "      <td>1.609800</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>68</td>\n",
-       "      <td>1.647700</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>69</td>\n",
-       "      <td>1.478100</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>70</td>\n",
-       "      <td>1.328200</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>71</td>\n",
-       "      <td>1.725000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>72</td>\n",
-       "      <td>1.522400</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>73</td>\n",
-       "      <td>1.557200</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>74</td>\n",
-       "      <td>1.670000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>75</td>\n",
-       "      <td>1.648900</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>76</td>\n",
-       "      <td>1.670400</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>77</td>\n",
-       "      <td>1.615300</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>78</td>\n",
-       "      <td>1.541800</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>79</td>\n",
-       "      <td>1.549200</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>80</td>\n",
-       "      <td>1.544500</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>81</td>\n",
-       "      <td>1.423300</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>82</td>\n",
-       "      <td>1.300900</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>83</td>\n",
-       "      <td>1.626600</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>84</td>\n",
-       "      <td>1.585000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>85</td>\n",
-       "      <td>1.444500</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>86</td>\n",
-       "      <td>1.598200</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>87</td>\n",
-       "      <td>1.541000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>88</td>\n",
-       "      <td>1.429500</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>89</td>\n",
-       "      <td>1.517300</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>90</td>\n",
-       "      <td>1.539100</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>91</td>\n",
-       "      <td>1.604200</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>92</td>\n",
-       "      <td>1.504300</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>93</td>\n",
-       "      <td>1.520200</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>94</td>\n",
-       "      <td>1.459000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>95</td>\n",
-       "      <td>1.619900</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>96</td>\n",
-       "      <td>1.629000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>97</td>\n",
-       "      <td>1.507000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>98</td>\n",
-       "      <td>1.455300</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>99</td>\n",
-       "      <td>1.461700</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>100</td>\n",
-       "      <td>1.513500</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>101</td>\n",
-       "      <td>1.521500</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>102</td>\n",
-       "      <td>1.658100</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>103</td>\n",
-       "      <td>1.579500</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>104</td>\n",
-       "      <td>1.430100</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>105</td>\n",
-       "      <td>1.591500</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>106</td>\n",
-       "      <td>1.620900</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>107</td>\n",
-       "      <td>1.681300</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>108</td>\n",
-       "      <td>1.662900</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>109</td>\n",
-       "      <td>1.717200</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>110</td>\n",
-       "      <td>1.656000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>111</td>\n",
-       "      <td>1.545400</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>112</td>\n",
-       "      <td>1.434400</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>113</td>\n",
-       "      <td>1.665900</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>114</td>\n",
-       "      <td>1.483000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>115</td>\n",
-       "      <td>1.411300</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>116</td>\n",
-       "      <td>1.549000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>117</td>\n",
-       "      <td>1.627200</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>118</td>\n",
-       "      <td>1.608600</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>119</td>\n",
-       "      <td>1.549700</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>120</td>\n",
-       "      <td>1.560800</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>121</td>\n",
-       "      <td>1.581400</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>122</td>\n",
-       "      <td>1.586100</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>123</td>\n",
-       "      <td>1.442700</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>124</td>\n",
-       "      <td>1.666800</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>125</td>\n",
-       "      <td>1.563900</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>126</td>\n",
-       "      <td>1.550300</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>127</td>\n",
-       "      <td>1.475600</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>128</td>\n",
-       "      <td>1.470400</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>129</td>\n",
-       "      <td>1.605000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>130</td>\n",
-       "      <td>1.546100</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>131</td>\n",
-       "      <td>1.552900</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>132</td>\n",
-       "      <td>1.562300</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>133</td>\n",
-       "      <td>1.468900</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>134</td>\n",
-       "      <td>1.368200</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>135</td>\n",
-       "      <td>1.545800</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>136</td>\n",
-       "      <td>1.519900</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>137</td>\n",
-       "      <td>1.646300</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>138</td>\n",
-       "      <td>1.588800</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>139</td>\n",
-       "      <td>1.550300</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>140</td>\n",
-       "      <td>1.484800</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>141</td>\n",
-       "      <td>1.581600</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>142</td>\n",
-       "      <td>1.623200</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>143</td>\n",
-       "      <td>1.664700</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>144</td>\n",
-       "      <td>1.538800</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>145</td>\n",
-       "      <td>1.662800</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>146</td>\n",
-       "      <td>1.593500</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>147</td>\n",
-       "      <td>1.419500</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>148</td>\n",
-       "      <td>1.656200</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>149</td>\n",
-       "      <td>1.479400</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>150</td>\n",
-       "      <td>1.512500</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>151</td>\n",
-       "      <td>1.528800</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>152</td>\n",
-       "      <td>1.500800</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>153</td>\n",
-       "      <td>1.597800</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>154</td>\n",
-       "      <td>1.548600</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>155</td>\n",
-       "      <td>1.626200</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>156</td>\n",
-       "      <td>1.633400</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>157</td>\n",
-       "      <td>1.536100</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>158</td>\n",
-       "      <td>1.535300</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>159</td>\n",
-       "      <td>1.571300</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>160</td>\n",
-       "      <td>1.461200</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>161</td>\n",
-       "      <td>1.516200</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>162</td>\n",
-       "      <td>1.465500</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>163</td>\n",
-       "      <td>1.563900</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>164</td>\n",
-       "      <td>1.599900</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>165</td>\n",
-       "      <td>1.494400</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>166</td>\n",
-       "      <td>1.550500</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>167</td>\n",
-       "      <td>1.382100</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>168</td>\n",
-       "      <td>1.550800</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>169</td>\n",
-       "      <td>1.554000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>170</td>\n",
-       "      <td>1.499200</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>171</td>\n",
-       "      <td>1.619500</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>172</td>\n",
-       "      <td>1.571800</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>173</td>\n",
-       "      <td>1.552700</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>174</td>\n",
-       "      <td>1.360500</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>175</td>\n",
-       "      <td>1.457600</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>176</td>\n",
-       "      <td>1.528500</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>177</td>\n",
-       "      <td>1.450600</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>178</td>\n",
-       "      <td>1.497100</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>179</td>\n",
-       "      <td>1.415400</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>180</td>\n",
-       "      <td>1.549900</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>181</td>\n",
-       "      <td>1.459800</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>182</td>\n",
-       "      <td>1.653100</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>183</td>\n",
-       "      <td>1.255300</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>184</td>\n",
-       "      <td>1.511100</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>185</td>\n",
-       "      <td>1.487700</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>186</td>\n",
-       "      <td>1.678500</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>187</td>\n",
-       "      <td>1.566400</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>188</td>\n",
-       "      <td>1.479300</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>189</td>\n",
-       "      <td>1.503900</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>190</td>\n",
-       "      <td>1.493700</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>191</td>\n",
-       "      <td>1.468400</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>192</td>\n",
-       "      <td>1.499400</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>193</td>\n",
-       "      <td>1.462300</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>194</td>\n",
-       "      <td>1.606200</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>195</td>\n",
-       "      <td>1.726000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>196</td>\n",
-       "      <td>1.424700</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>197</td>\n",
-       "      <td>1.560500</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>198</td>\n",
-       "      <td>1.572200</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>199</td>\n",
-       "      <td>1.694600</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>200</td>\n",
-       "      <td>1.508900</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>201</td>\n",
-       "      <td>1.465600</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>202</td>\n",
-       "      <td>1.533500</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>203</td>\n",
-       "      <td>1.531400</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>204</td>\n",
-       "      <td>1.543200</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>205</td>\n",
-       "      <td>1.546500</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>206</td>\n",
-       "      <td>1.568600</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>207</td>\n",
-       "      <td>1.437200</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>208</td>\n",
-       "      <td>1.524100</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>209</td>\n",
-       "      <td>1.644300</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>210</td>\n",
-       "      <td>1.412500</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>211</td>\n",
-       "      <td>1.604700</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>212</td>\n",
-       "      <td>1.538300</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>213</td>\n",
-       "      <td>1.552600</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>214</td>\n",
-       "      <td>1.654100</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>215</td>\n",
-       "      <td>1.632300</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>216</td>\n",
-       "      <td>1.634200</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>217</td>\n",
-       "      <td>1.562400</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>218</td>\n",
-       "      <td>1.528000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>219</td>\n",
-       "      <td>1.444400</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>220</td>\n",
-       "      <td>1.449800</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>221</td>\n",
-       "      <td>1.561900</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>222</td>\n",
-       "      <td>1.565400</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>223</td>\n",
-       "      <td>1.526800</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>224</td>\n",
-       "      <td>1.422900</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>225</td>\n",
-       "      <td>1.514200</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>226</td>\n",
-       "      <td>1.663700</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>227</td>\n",
-       "      <td>1.402100</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>228</td>\n",
-       "      <td>1.536400</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>229</td>\n",
-       "      <td>1.411200</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>230</td>\n",
-       "      <td>1.582300</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>231</td>\n",
-       "      <td>1.489300</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>232</td>\n",
-       "      <td>1.531800</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>233</td>\n",
-       "      <td>1.509500</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>234</td>\n",
-       "      <td>1.514100</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>235</td>\n",
-       "      <td>1.503800</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>236</td>\n",
-       "      <td>1.558800</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>237</td>\n",
-       "      <td>1.433500</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>238</td>\n",
-       "      <td>1.593100</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>239</td>\n",
-       "      <td>1.442500</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>240</td>\n",
-       "      <td>1.458900</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>241</td>\n",
-       "      <td>1.609300</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>242</td>\n",
-       "      <td>1.368500</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>243</td>\n",
-       "      <td>1.488500</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>244</td>\n",
-       "      <td>1.495500</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>245</td>\n",
-       "      <td>1.587800</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>246</td>\n",
-       "      <td>1.597700</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>247</td>\n",
-       "      <td>1.337800</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>248</td>\n",
-       "      <td>1.527200</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>249</td>\n",
-       "      <td>1.343900</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>250</td>\n",
-       "      <td>1.376000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>251</td>\n",
-       "      <td>1.506100</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>252</td>\n",
-       "      <td>1.415800</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>253</td>\n",
-       "      <td>1.528500</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>254</td>\n",
-       "      <td>1.499300</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>255</td>\n",
-       "      <td>1.605400</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>256</td>\n",
-       "      <td>1.471000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>257</td>\n",
-       "      <td>1.507400</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>258</td>\n",
-       "      <td>1.471800</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>259</td>\n",
-       "      <td>1.460100</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>260</td>\n",
-       "      <td>1.623500</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>261</td>\n",
-       "      <td>1.470000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>262</td>\n",
-       "      <td>1.317300</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>263</td>\n",
-       "      <td>1.381800</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>264</td>\n",
-       "      <td>1.381500</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>265</td>\n",
-       "      <td>1.475200</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>266</td>\n",
-       "      <td>1.511700</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>267</td>\n",
-       "      <td>1.524100</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>268</td>\n",
-       "      <td>1.487300</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>269</td>\n",
-       "      <td>1.331600</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>270</td>\n",
-       "      <td>1.479500</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>271</td>\n",
-       "      <td>1.474400</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>272</td>\n",
-       "      <td>1.530400</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>273</td>\n",
-       "      <td>1.520800</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>274</td>\n",
-       "      <td>1.613700</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>275</td>\n",
-       "      <td>1.543800</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>276</td>\n",
-       "      <td>1.588600</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>277</td>\n",
-       "      <td>1.462600</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>278</td>\n",
-       "      <td>1.433200</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>279</td>\n",
-       "      <td>1.508600</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>280</td>\n",
-       "      <td>1.401300</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>281</td>\n",
-       "      <td>1.486700</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>282</td>\n",
-       "      <td>1.590800</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>283</td>\n",
-       "      <td>1.455800</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>284</td>\n",
-       "      <td>1.442800</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>285</td>\n",
-       "      <td>1.660000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>286</td>\n",
-       "      <td>1.642900</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>287</td>\n",
-       "      <td>1.431400</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>288</td>\n",
-       "      <td>1.575100</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>289</td>\n",
-       "      <td>1.557800</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>290</td>\n",
-       "      <td>1.553200</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>291</td>\n",
-       "      <td>1.541500</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>292</td>\n",
-       "      <td>1.531600</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>293</td>\n",
-       "      <td>1.489800</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>294</td>\n",
-       "      <td>1.561100</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>295</td>\n",
-       "      <td>1.524400</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>296</td>\n",
-       "      <td>1.421400</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>297</td>\n",
-       "      <td>1.466800</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>298</td>\n",
-       "      <td>1.526200</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>299</td>\n",
-       "      <td>1.411400</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>300</td>\n",
-       "      <td>1.428100</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>301</td>\n",
-       "      <td>1.464500</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>302</td>\n",
-       "      <td>1.460000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>303</td>\n",
-       "      <td>1.522700</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>304</td>\n",
-       "      <td>1.533100</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>305</td>\n",
-       "      <td>1.464400</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>306</td>\n",
-       "      <td>1.545100</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>307</td>\n",
-       "      <td>1.506800</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>308</td>\n",
-       "      <td>1.508500</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>309</td>\n",
-       "      <td>1.576900</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>310</td>\n",
-       "      <td>1.587500</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>311</td>\n",
-       "      <td>1.397800</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>312</td>\n",
-       "      <td>1.478100</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>313</td>\n",
-       "      <td>1.484200</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>314</td>\n",
-       "      <td>1.428500</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>315</td>\n",
-       "      <td>1.520700</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>316</td>\n",
-       "      <td>1.464100</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>317</td>\n",
-       "      <td>1.412200</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>318</td>\n",
-       "      <td>1.493000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>319</td>\n",
-       "      <td>1.514200</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>320</td>\n",
-       "      <td>1.538200</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>321</td>\n",
-       "      <td>1.537100</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>322</td>\n",
-       "      <td>1.470500</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>323</td>\n",
-       "      <td>1.361800</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>324</td>\n",
-       "      <td>1.540100</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>325</td>\n",
-       "      <td>1.583800</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>326</td>\n",
-       "      <td>1.411400</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>327</td>\n",
-       "      <td>1.585000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>328</td>\n",
-       "      <td>1.561200</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>329</td>\n",
-       "      <td>1.441400</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>330</td>\n",
-       "      <td>1.443100</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>331</td>\n",
-       "      <td>1.487900</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>332</td>\n",
-       "      <td>1.441400</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>333</td>\n",
-       "      <td>1.502100</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>334</td>\n",
-       "      <td>1.680100</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>335</td>\n",
-       "      <td>1.718200</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>336</td>\n",
-       "      <td>1.613200</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>337</td>\n",
-       "      <td>1.428600</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>338</td>\n",
-       "      <td>1.659800</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>339</td>\n",
-       "      <td>1.550100</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>340</td>\n",
-       "      <td>1.479900</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>341</td>\n",
-       "      <td>1.512500</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>342</td>\n",
-       "      <td>1.371900</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>343</td>\n",
-       "      <td>1.418200</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>344</td>\n",
-       "      <td>1.605200</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>345</td>\n",
-       "      <td>1.455900</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>346</td>\n",
-       "      <td>1.413300</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>347</td>\n",
-       "      <td>1.463400</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>348</td>\n",
-       "      <td>1.459700</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>349</td>\n",
-       "      <td>1.473400</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>350</td>\n",
-       "      <td>1.467900</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>351</td>\n",
-       "      <td>1.424800</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>352</td>\n",
-       "      <td>1.607200</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>353</td>\n",
-       "      <td>1.697500</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>354</td>\n",
-       "      <td>1.510900</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>355</td>\n",
-       "      <td>1.606700</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>356</td>\n",
-       "      <td>1.639400</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>357</td>\n",
-       "      <td>1.460200</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>358</td>\n",
-       "      <td>1.456100</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>359</td>\n",
-       "      <td>1.393600</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>360</td>\n",
-       "      <td>1.477500</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>361</td>\n",
-       "      <td>1.438100</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>362</td>\n",
-       "      <td>1.412900</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>363</td>\n",
-       "      <td>1.564800</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>364</td>\n",
-       "      <td>1.423000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>365</td>\n",
-       "      <td>1.517000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>366</td>\n",
-       "      <td>1.378000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>367</td>\n",
-       "      <td>1.541300</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>368</td>\n",
-       "      <td>1.426400</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>369</td>\n",
-       "      <td>1.512400</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>370</td>\n",
-       "      <td>1.470800</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>371</td>\n",
-       "      <td>1.514200</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>372</td>\n",
-       "      <td>1.480300</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>373</td>\n",
-       "      <td>1.489100</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>374</td>\n",
-       "      <td>1.546200</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>375</td>\n",
-       "      <td>1.481200</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>376</td>\n",
-       "      <td>1.476000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>377</td>\n",
-       "      <td>1.385400</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>378</td>\n",
-       "      <td>1.613200</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>379</td>\n",
-       "      <td>1.245500</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>380</td>\n",
-       "      <td>1.312100</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>381</td>\n",
-       "      <td>1.396700</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>382</td>\n",
-       "      <td>1.501400</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>383</td>\n",
-       "      <td>1.405100</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>384</td>\n",
-       "      <td>1.481700</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>385</td>\n",
-       "      <td>1.520400</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>386</td>\n",
-       "      <td>1.596300</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>387</td>\n",
-       "      <td>1.585500</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>388</td>\n",
-       "      <td>1.557700</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>389</td>\n",
-       "      <td>1.432000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>390</td>\n",
-       "      <td>1.627200</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>391</td>\n",
-       "      <td>1.498900</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>392</td>\n",
-       "      <td>1.583700</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>393</td>\n",
-       "      <td>1.411800</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>394</td>\n",
-       "      <td>1.454600</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>395</td>\n",
-       "      <td>1.532200</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>396</td>\n",
-       "      <td>1.443000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>397</td>\n",
-       "      <td>1.358000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>398</td>\n",
-       "      <td>1.400200</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>399</td>\n",
-       "      <td>1.493300</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>400</td>\n",
-       "      <td>1.387900</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>401</td>\n",
-       "      <td>1.430900</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>402</td>\n",
-       "      <td>1.485400</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>403</td>\n",
-       "      <td>1.757100</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>404</td>\n",
-       "      <td>1.606100</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>405</td>\n",
-       "      <td>1.570100</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>406</td>\n",
-       "      <td>1.600700</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>407</td>\n",
-       "      <td>1.489300</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>408</td>\n",
-       "      <td>1.570900</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>409</td>\n",
-       "      <td>1.442300</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>410</td>\n",
-       "      <td>1.504900</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>411</td>\n",
-       "      <td>1.406900</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>412</td>\n",
-       "      <td>1.600600</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>413</td>\n",
-       "      <td>1.362500</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>414</td>\n",
-       "      <td>1.527700</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>415</td>\n",
-       "      <td>1.509400</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>416</td>\n",
-       "      <td>1.619800</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>417</td>\n",
-       "      <td>1.367200</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>418</td>\n",
-       "      <td>1.440800</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>419</td>\n",
-       "      <td>1.523200</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>420</td>\n",
-       "      <td>1.507500</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>421</td>\n",
-       "      <td>1.473100</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>422</td>\n",
-       "      <td>1.406900</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>423</td>\n",
-       "      <td>1.417000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>424</td>\n",
-       "      <td>1.462700</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>425</td>\n",
-       "      <td>1.536800</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>426</td>\n",
-       "      <td>1.545300</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>427</td>\n",
-       "      <td>1.457400</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>428</td>\n",
-       "      <td>1.471200</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>429</td>\n",
-       "      <td>1.470500</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>430</td>\n",
-       "      <td>1.550000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>431</td>\n",
-       "      <td>1.517700</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>432</td>\n",
-       "      <td>1.552500</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>433</td>\n",
-       "      <td>1.564900</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>434</td>\n",
-       "      <td>1.662400</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>435</td>\n",
-       "      <td>1.484900</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>436</td>\n",
-       "      <td>1.381200</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>437</td>\n",
-       "      <td>1.505900</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>438</td>\n",
-       "      <td>1.439100</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>439</td>\n",
-       "      <td>1.343900</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>440</td>\n",
-       "      <td>1.508700</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>441</td>\n",
-       "      <td>1.525400</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>442</td>\n",
-       "      <td>1.434000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>443</td>\n",
-       "      <td>1.470400</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>444</td>\n",
-       "      <td>1.544200</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>445</td>\n",
-       "      <td>1.380300</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>446</td>\n",
-       "      <td>1.475500</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>447</td>\n",
-       "      <td>1.653600</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>448</td>\n",
-       "      <td>1.636300</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>449</td>\n",
-       "      <td>1.525200</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>450</td>\n",
-       "      <td>1.500500</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>451</td>\n",
-       "      <td>1.438000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>452</td>\n",
-       "      <td>1.488800</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>453</td>\n",
-       "      <td>1.396300</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>454</td>\n",
-       "      <td>1.440200</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>455</td>\n",
-       "      <td>1.482000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>456</td>\n",
-       "      <td>1.461400</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>457</td>\n",
-       "      <td>1.471400</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>458</td>\n",
-       "      <td>1.315300</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>459</td>\n",
-       "      <td>1.587200</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>460</td>\n",
-       "      <td>1.452000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>461</td>\n",
-       "      <td>1.718700</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>462</td>\n",
-       "      <td>1.414400</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>463</td>\n",
-       "      <td>1.514500</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>464</td>\n",
-       "      <td>1.492100</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>465</td>\n",
-       "      <td>1.581400</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>466</td>\n",
-       "      <td>1.425000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>467</td>\n",
-       "      <td>1.476900</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>468</td>\n",
-       "      <td>1.403700</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>469</td>\n",
-       "      <td>1.438700</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>470</td>\n",
-       "      <td>1.563300</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>471</td>\n",
-       "      <td>1.475600</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>472</td>\n",
-       "      <td>1.610700</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>473</td>\n",
-       "      <td>1.348700</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>474</td>\n",
-       "      <td>1.470000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>475</td>\n",
-       "      <td>1.615400</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>476</td>\n",
-       "      <td>1.446700</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>477</td>\n",
-       "      <td>1.394500</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>478</td>\n",
-       "      <td>1.470600</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>479</td>\n",
-       "      <td>1.397700</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>480</td>\n",
-       "      <td>1.377500</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>481</td>\n",
-       "      <td>1.504900</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>482</td>\n",
-       "      <td>1.485500</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>483</td>\n",
-       "      <td>1.461600</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>484</td>\n",
-       "      <td>1.520600</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>485</td>\n",
-       "      <td>1.532300</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>486</td>\n",
-       "      <td>1.627200</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>487</td>\n",
-       "      <td>1.509800</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>488</td>\n",
-       "      <td>1.387400</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>489</td>\n",
-       "      <td>1.438900</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>490</td>\n",
-       "      <td>1.440700</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>491</td>\n",
-       "      <td>1.527900</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>492</td>\n",
-       "      <td>1.478900</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>493</td>\n",
-       "      <td>1.461900</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>494</td>\n",
-       "      <td>1.624800</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>495</td>\n",
-       "      <td>1.521600</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>496</td>\n",
-       "      <td>1.406800</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>497</td>\n",
-       "      <td>1.480600</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>498</td>\n",
-       "      <td>1.602300</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>499</td>\n",
-       "      <td>1.590400</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>500</td>\n",
-       "      <td>1.622000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>501</td>\n",
-       "      <td>1.582400</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>502</td>\n",
-       "      <td>1.548000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>503</td>\n",
-       "      <td>1.439800</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>504</td>\n",
-       "      <td>1.406300</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>505</td>\n",
-       "      <td>1.499700</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>506</td>\n",
-       "      <td>1.389400</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>507</td>\n",
-       "      <td>1.591000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>508</td>\n",
-       "      <td>1.453000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>509</td>\n",
-       "      <td>1.532200</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>510</td>\n",
-       "      <td>1.482900</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>511</td>\n",
-       "      <td>1.428800</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>512</td>\n",
-       "      <td>1.575800</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>513</td>\n",
-       "      <td>1.460300</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>514</td>\n",
-       "      <td>1.530200</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>515</td>\n",
-       "      <td>1.447100</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>516</td>\n",
-       "      <td>1.621300</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>517</td>\n",
-       "      <td>1.525500</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>518</td>\n",
-       "      <td>1.528700</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>519</td>\n",
-       "      <td>1.466200</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>520</td>\n",
-       "      <td>1.488700</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>521</td>\n",
-       "      <td>1.449400</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>522</td>\n",
-       "      <td>1.537600</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>523</td>\n",
-       "      <td>1.398400</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>524</td>\n",
-       "      <td>1.316700</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>525</td>\n",
-       "      <td>1.386100</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>526</td>\n",
-       "      <td>1.603900</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>527</td>\n",
-       "      <td>1.353800</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>528</td>\n",
-       "      <td>1.306700</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>529</td>\n",
-       "      <td>1.401600</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>530</td>\n",
-       "      <td>1.380400</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>531</td>\n",
-       "      <td>1.394900</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>532</td>\n",
-       "      <td>1.498300</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>533</td>\n",
-       "      <td>1.462200</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>534</td>\n",
-       "      <td>1.458100</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>535</td>\n",
-       "      <td>1.515000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>536</td>\n",
-       "      <td>1.483900</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>537</td>\n",
-       "      <td>1.508600</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>538</td>\n",
-       "      <td>1.612800</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>539</td>\n",
-       "      <td>1.443400</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>540</td>\n",
-       "      <td>1.455600</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>541</td>\n",
-       "      <td>1.568900</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>542</td>\n",
-       "      <td>1.547600</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>543</td>\n",
-       "      <td>1.432400</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>544</td>\n",
-       "      <td>1.583800</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>545</td>\n",
-       "      <td>1.365600</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>546</td>\n",
-       "      <td>1.596500</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>547</td>\n",
-       "      <td>1.450600</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>548</td>\n",
-       "      <td>1.485400</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>549</td>\n",
-       "      <td>1.457700</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>550</td>\n",
-       "      <td>1.390200</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>551</td>\n",
-       "      <td>1.399700</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>552</td>\n",
-       "      <td>1.417600</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>553</td>\n",
-       "      <td>1.579800</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>554</td>\n",
-       "      <td>1.472400</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>555</td>\n",
-       "      <td>1.386100</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>556</td>\n",
-       "      <td>1.439000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>557</td>\n",
-       "      <td>1.418300</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>558</td>\n",
-       "      <td>1.444300</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>559</td>\n",
-       "      <td>1.516500</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>560</td>\n",
-       "      <td>1.550100</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>561</td>\n",
-       "      <td>1.410800</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>562</td>\n",
-       "      <td>1.560600</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>563</td>\n",
-       "      <td>1.523800</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>564</td>\n",
-       "      <td>1.489200</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>565</td>\n",
-       "      <td>1.423400</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>566</td>\n",
-       "      <td>1.436900</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>567</td>\n",
-       "      <td>1.546700</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>568</td>\n",
-       "      <td>1.393200</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>569</td>\n",
-       "      <td>1.556600</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>570</td>\n",
-       "      <td>1.446700</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>571</td>\n",
-       "      <td>1.380600</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>572</td>\n",
-       "      <td>1.340500</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>573</td>\n",
-       "      <td>1.477000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>574</td>\n",
-       "      <td>1.367000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>575</td>\n",
-       "      <td>1.643500</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>576</td>\n",
-       "      <td>1.448600</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>577</td>\n",
-       "      <td>1.419600</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>578</td>\n",
-       "      <td>1.568400</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>579</td>\n",
-       "      <td>1.473300</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>580</td>\n",
-       "      <td>1.650400</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>581</td>\n",
-       "      <td>1.572000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>582</td>\n",
-       "      <td>1.499300</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>583</td>\n",
-       "      <td>1.613200</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>584</td>\n",
-       "      <td>1.566500</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>585</td>\n",
-       "      <td>1.477800</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>586</td>\n",
-       "      <td>1.507300</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>587</td>\n",
-       "      <td>1.374800</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>588</td>\n",
-       "      <td>1.480100</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>589</td>\n",
-       "      <td>1.357000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>590</td>\n",
-       "      <td>1.328300</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>591</td>\n",
-       "      <td>1.343400</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>592</td>\n",
-       "      <td>1.470600</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>593</td>\n",
-       "      <td>1.524700</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>594</td>\n",
-       "      <td>1.420600</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>595</td>\n",
-       "      <td>1.398400</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>596</td>\n",
-       "      <td>1.498600</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>597</td>\n",
-       "      <td>1.530700</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>598</td>\n",
-       "      <td>1.520700</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>599</td>\n",
-       "      <td>1.579800</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>600</td>\n",
-       "      <td>1.559300</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>601</td>\n",
-       "      <td>1.400800</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>602</td>\n",
-       "      <td>1.489000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>603</td>\n",
-       "      <td>1.532900</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>604</td>\n",
-       "      <td>1.507300</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>605</td>\n",
-       "      <td>1.447400</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>606</td>\n",
-       "      <td>1.527100</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>607</td>\n",
-       "      <td>1.433700</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>608</td>\n",
-       "      <td>1.533300</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>609</td>\n",
-       "      <td>1.469300</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>610</td>\n",
-       "      <td>1.504100</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>611</td>\n",
-       "      <td>1.416300</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>612</td>\n",
-       "      <td>1.601600</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>613</td>\n",
-       "      <td>1.526500</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>614</td>\n",
-       "      <td>1.491200</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>615</td>\n",
-       "      <td>1.605900</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>616</td>\n",
-       "      <td>1.561700</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>617</td>\n",
-       "      <td>1.384500</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>618</td>\n",
-       "      <td>1.561900</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>619</td>\n",
-       "      <td>1.416700</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>620</td>\n",
-       "      <td>1.484600</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>621</td>\n",
-       "      <td>1.558600</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>622</td>\n",
-       "      <td>1.449400</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>623</td>\n",
-       "      <td>1.477200</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>624</td>\n",
-       "      <td>1.557600</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>625</td>\n",
-       "      <td>1.550600</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>626</td>\n",
-       "      <td>1.575000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>627</td>\n",
-       "      <td>1.376900</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>628</td>\n",
-       "      <td>1.557200</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>629</td>\n",
-       "      <td>1.466200</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>630</td>\n",
-       "      <td>1.390700</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>631</td>\n",
-       "      <td>1.441400</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>632</td>\n",
-       "      <td>1.526600</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>633</td>\n",
-       "      <td>1.455400</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>634</td>\n",
-       "      <td>1.310500</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>635</td>\n",
-       "      <td>1.445300</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>636</td>\n",
-       "      <td>1.431300</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>637</td>\n",
-       "      <td>1.596800</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>638</td>\n",
-       "      <td>1.520600</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>639</td>\n",
-       "      <td>1.554900</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>640</td>\n",
-       "      <td>1.456100</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>641</td>\n",
-       "      <td>1.566200</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>642</td>\n",
-       "      <td>1.507100</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>643</td>\n",
-       "      <td>1.522700</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>644</td>\n",
-       "      <td>1.482700</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>645</td>\n",
-       "      <td>1.525900</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>646</td>\n",
-       "      <td>1.327800</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>647</td>\n",
-       "      <td>1.441400</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>648</td>\n",
-       "      <td>1.412400</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>649</td>\n",
-       "      <td>1.338100</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>650</td>\n",
-       "      <td>1.466900</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>651</td>\n",
-       "      <td>1.592100</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>652</td>\n",
-       "      <td>1.473300</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>653</td>\n",
-       "      <td>1.526600</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>654</td>\n",
-       "      <td>1.484900</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>655</td>\n",
-       "      <td>1.537900</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>656</td>\n",
-       "      <td>1.368500</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>657</td>\n",
-       "      <td>1.332000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>658</td>\n",
-       "      <td>1.545500</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>659</td>\n",
-       "      <td>1.425000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>660</td>\n",
-       "      <td>1.487300</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>661</td>\n",
-       "      <td>1.499200</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>662</td>\n",
-       "      <td>1.461900</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>663</td>\n",
-       "      <td>1.495800</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>664</td>\n",
-       "      <td>1.432700</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>665</td>\n",
-       "      <td>1.480300</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>666</td>\n",
-       "      <td>1.452000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>667</td>\n",
-       "      <td>1.516700</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>668</td>\n",
-       "      <td>1.465200</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>669</td>\n",
-       "      <td>1.455800</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>670</td>\n",
-       "      <td>1.402400</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>671</td>\n",
-       "      <td>1.377000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>672</td>\n",
-       "      <td>1.540900</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>673</td>\n",
-       "      <td>1.436500</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>674</td>\n",
-       "      <td>1.597800</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>675</td>\n",
-       "      <td>1.432400</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>676</td>\n",
-       "      <td>1.417700</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>677</td>\n",
-       "      <td>1.305100</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>678</td>\n",
-       "      <td>1.543400</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>679</td>\n",
-       "      <td>1.629200</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>680</td>\n",
-       "      <td>1.404100</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>681</td>\n",
-       "      <td>1.544200</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>682</td>\n",
-       "      <td>1.552600</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>683</td>\n",
-       "      <td>1.422000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>684</td>\n",
-       "      <td>1.477900</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>685</td>\n",
-       "      <td>1.293200</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>686</td>\n",
-       "      <td>1.411200</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>687</td>\n",
-       "      <td>1.480900</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>688</td>\n",
-       "      <td>1.486800</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>689</td>\n",
-       "      <td>1.316400</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>690</td>\n",
-       "      <td>1.466900</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>691</td>\n",
-       "      <td>1.376700</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>692</td>\n",
-       "      <td>1.440000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>693</td>\n",
-       "      <td>1.594300</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>694</td>\n",
-       "      <td>1.482100</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>695</td>\n",
-       "      <td>1.537500</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>696</td>\n",
-       "      <td>1.543200</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>697</td>\n",
-       "      <td>1.458800</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>698</td>\n",
-       "      <td>1.493900</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>699</td>\n",
-       "      <td>1.517100</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>700</td>\n",
-       "      <td>1.408600</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>701</td>\n",
-       "      <td>1.488700</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>702</td>\n",
-       "      <td>1.363300</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>703</td>\n",
-       "      <td>1.300900</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>704</td>\n",
-       "      <td>1.488000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>705</td>\n",
-       "      <td>1.377400</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>706</td>\n",
-       "      <td>1.526500</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>707</td>\n",
-       "      <td>1.392900</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>708</td>\n",
-       "      <td>1.536000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>709</td>\n",
-       "      <td>1.349900</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>710</td>\n",
-       "      <td>1.447300</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>711</td>\n",
-       "      <td>1.349600</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>712</td>\n",
-       "      <td>1.548100</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>713</td>\n",
-       "      <td>1.441000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>714</td>\n",
-       "      <td>1.418200</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>715</td>\n",
-       "      <td>1.434100</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>716</td>\n",
-       "      <td>1.387700</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>717</td>\n",
-       "      <td>1.293200</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>718</td>\n",
-       "      <td>1.396800</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>719</td>\n",
-       "      <td>1.430700</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>720</td>\n",
-       "      <td>1.363800</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>721</td>\n",
-       "      <td>1.471500</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>722</td>\n",
-       "      <td>1.502400</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>723</td>\n",
-       "      <td>1.394000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>724</td>\n",
-       "      <td>1.339500</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>725</td>\n",
-       "      <td>1.478800</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>726</td>\n",
-       "      <td>1.554500</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>727</td>\n",
-       "      <td>1.355800</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>728</td>\n",
-       "      <td>1.422100</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>729</td>\n",
-       "      <td>1.487600</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>730</td>\n",
-       "      <td>1.425300</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>731</td>\n",
-       "      <td>1.429600</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>732</td>\n",
-       "      <td>1.440100</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>733</td>\n",
-       "      <td>1.484700</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>734</td>\n",
-       "      <td>1.588300</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>735</td>\n",
-       "      <td>1.428800</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>736</td>\n",
-       "      <td>1.510200</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>737</td>\n",
-       "      <td>1.418300</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>738</td>\n",
-       "      <td>1.461400</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>739</td>\n",
-       "      <td>1.455600</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>740</td>\n",
-       "      <td>1.377100</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>741</td>\n",
-       "      <td>1.382400</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>742</td>\n",
-       "      <td>1.520200</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>743</td>\n",
-       "      <td>1.383200</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>744</td>\n",
-       "      <td>1.494000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>745</td>\n",
-       "      <td>1.567400</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>746</td>\n",
-       "      <td>1.437000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>747</td>\n",
-       "      <td>1.458000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>748</td>\n",
-       "      <td>1.483100</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>749</td>\n",
-       "      <td>1.473700</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>750</td>\n",
-       "      <td>1.644300</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>751</td>\n",
-       "      <td>1.348900</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>752</td>\n",
-       "      <td>1.442800</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>753</td>\n",
-       "      <td>1.616400</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>754</td>\n",
-       "      <td>1.459600</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>755</td>\n",
-       "      <td>1.478100</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>756</td>\n",
-       "      <td>1.469500</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>757</td>\n",
-       "      <td>1.510300</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>758</td>\n",
-       "      <td>1.402400</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>759</td>\n",
-       "      <td>1.477400</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>760</td>\n",
-       "      <td>1.597400</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>761</td>\n",
-       "      <td>1.470700</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>762</td>\n",
-       "      <td>1.586600</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>763</td>\n",
-       "      <td>1.316800</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>764</td>\n",
-       "      <td>1.298600</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>765</td>\n",
-       "      <td>1.482500</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>766</td>\n",
-       "      <td>1.544300</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>767</td>\n",
-       "      <td>1.396300</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>768</td>\n",
-       "      <td>1.321000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>769</td>\n",
-       "      <td>1.424400</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>770</td>\n",
-       "      <td>1.449300</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>771</td>\n",
-       "      <td>1.479900</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>772</td>\n",
-       "      <td>1.451300</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>773</td>\n",
-       "      <td>1.567600</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>774</td>\n",
-       "      <td>1.257600</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>775</td>\n",
-       "      <td>1.649800</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>776</td>\n",
-       "      <td>1.516400</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>777</td>\n",
-       "      <td>1.461400</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>778</td>\n",
-       "      <td>1.494800</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>779</td>\n",
-       "      <td>1.621100</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>780</td>\n",
-       "      <td>1.571900</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>781</td>\n",
-       "      <td>1.331500</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>782</td>\n",
-       "      <td>1.575500</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>783</td>\n",
-       "      <td>1.439000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>784</td>\n",
-       "      <td>1.347600</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>785</td>\n",
-       "      <td>1.522800</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>786</td>\n",
-       "      <td>1.584100</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>787</td>\n",
-       "      <td>1.419300</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>788</td>\n",
-       "      <td>1.385400</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>789</td>\n",
-       "      <td>1.435000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>790</td>\n",
-       "      <td>1.483800</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>791</td>\n",
-       "      <td>1.452200</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>792</td>\n",
-       "      <td>1.587100</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>793</td>\n",
-       "      <td>1.495600</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>794</td>\n",
-       "      <td>1.485100</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>795</td>\n",
-       "      <td>1.444100</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>796</td>\n",
-       "      <td>1.534800</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>797</td>\n",
-       "      <td>1.436100</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>798</td>\n",
-       "      <td>1.366400</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>799</td>\n",
-       "      <td>1.603100</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>800</td>\n",
-       "      <td>1.505600</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>801</td>\n",
-       "      <td>1.484300</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>802</td>\n",
-       "      <td>1.353700</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>803</td>\n",
-       "      <td>1.462300</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>804</td>\n",
-       "      <td>1.497700</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>805</td>\n",
-       "      <td>1.448300</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>806</td>\n",
-       "      <td>1.388900</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>807</td>\n",
-       "      <td>1.440900</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>808</td>\n",
-       "      <td>1.437100</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>809</td>\n",
-       "      <td>1.527300</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>810</td>\n",
-       "      <td>1.497200</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>811</td>\n",
-       "      <td>1.482200</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>812</td>\n",
-       "      <td>1.361200</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>813</td>\n",
-       "      <td>1.435700</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>814</td>\n",
-       "      <td>1.463700</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>815</td>\n",
-       "      <td>1.478700</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>816</td>\n",
-       "      <td>1.523100</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>817</td>\n",
-       "      <td>1.560500</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>818</td>\n",
-       "      <td>1.457100</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>819</td>\n",
-       "      <td>1.477400</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>820</td>\n",
-       "      <td>1.558200</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>821</td>\n",
-       "      <td>1.424400</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>822</td>\n",
-       "      <td>1.578200</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>823</td>\n",
-       "      <td>1.465400</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>824</td>\n",
-       "      <td>1.343500</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>825</td>\n",
-       "      <td>1.405000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>826</td>\n",
-       "      <td>1.476500</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>827</td>\n",
-       "      <td>1.458900</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>828</td>\n",
-       "      <td>1.458300</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>829</td>\n",
-       "      <td>1.497900</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>830</td>\n",
-       "      <td>1.436900</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>831</td>\n",
-       "      <td>1.575000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>832</td>\n",
-       "      <td>1.531200</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>833</td>\n",
-       "      <td>1.490700</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>834</td>\n",
-       "      <td>1.556900</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>835</td>\n",
-       "      <td>1.620300</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>836</td>\n",
-       "      <td>1.563400</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>837</td>\n",
-       "      <td>1.436300</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>838</td>\n",
-       "      <td>1.465600</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>839</td>\n",
-       "      <td>1.412700</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>840</td>\n",
-       "      <td>1.487900</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>841</td>\n",
-       "      <td>1.506800</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>842</td>\n",
-       "      <td>1.427100</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>843</td>\n",
-       "      <td>1.376300</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>844</td>\n",
-       "      <td>1.500300</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>845</td>\n",
-       "      <td>1.573100</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>846</td>\n",
-       "      <td>1.443300</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>847</td>\n",
-       "      <td>1.476400</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>848</td>\n",
-       "      <td>1.497100</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>849</td>\n",
-       "      <td>1.310600</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>850</td>\n",
-       "      <td>1.404200</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>851</td>\n",
-       "      <td>1.575800</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>852</td>\n",
-       "      <td>1.506100</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>853</td>\n",
-       "      <td>1.424900</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>854</td>\n",
-       "      <td>1.522100</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>855</td>\n",
-       "      <td>1.376900</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>856</td>\n",
-       "      <td>1.476000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>857</td>\n",
-       "      <td>1.339700</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>858</td>\n",
-       "      <td>1.440300</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>859</td>\n",
-       "      <td>1.518100</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>860</td>\n",
-       "      <td>1.411400</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>861</td>\n",
-       "      <td>1.394900</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>862</td>\n",
-       "      <td>1.522100</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>863</td>\n",
-       "      <td>1.436000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>864</td>\n",
-       "      <td>1.585100</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>865</td>\n",
-       "      <td>1.490100</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>866</td>\n",
-       "      <td>1.472400</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>867</td>\n",
-       "      <td>1.299200</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>868</td>\n",
-       "      <td>1.422200</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>869</td>\n",
-       "      <td>1.487800</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>870</td>\n",
-       "      <td>1.623900</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>871</td>\n",
-       "      <td>1.605000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>872</td>\n",
-       "      <td>1.580400</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>873</td>\n",
-       "      <td>1.275400</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>874</td>\n",
-       "      <td>1.452700</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>875</td>\n",
-       "      <td>1.400200</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>876</td>\n",
-       "      <td>1.473500</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>877</td>\n",
-       "      <td>1.359500</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>878</td>\n",
-       "      <td>1.495800</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>879</td>\n",
-       "      <td>1.451500</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>880</td>\n",
-       "      <td>1.420400</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>881</td>\n",
-       "      <td>1.528400</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>882</td>\n",
-       "      <td>1.397800</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>883</td>\n",
-       "      <td>1.597900</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>884</td>\n",
-       "      <td>1.509000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>885</td>\n",
-       "      <td>1.568300</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>886</td>\n",
-       "      <td>1.473000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>887</td>\n",
-       "      <td>1.553900</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>888</td>\n",
-       "      <td>1.588500</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>889</td>\n",
-       "      <td>1.442500</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>890</td>\n",
-       "      <td>1.415100</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>891</td>\n",
-       "      <td>1.357400</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>892</td>\n",
-       "      <td>1.311900</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>893</td>\n",
-       "      <td>1.405100</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>894</td>\n",
-       "      <td>1.464700</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>895</td>\n",
-       "      <td>1.495000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>896</td>\n",
-       "      <td>1.488900</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>897</td>\n",
-       "      <td>1.584100</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>898</td>\n",
-       "      <td>1.444000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>899</td>\n",
-       "      <td>1.414800</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>900</td>\n",
-       "      <td>1.465800</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>901</td>\n",
-       "      <td>1.523400</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>902</td>\n",
-       "      <td>1.518300</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>903</td>\n",
-       "      <td>1.488800</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>904</td>\n",
-       "      <td>1.305900</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>905</td>\n",
-       "      <td>1.549500</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>906</td>\n",
-       "      <td>1.580100</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>907</td>\n",
-       "      <td>1.603000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>908</td>\n",
-       "      <td>1.450600</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>909</td>\n",
-       "      <td>1.503000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>910</td>\n",
-       "      <td>1.450300</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>911</td>\n",
-       "      <td>1.382200</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>912</td>\n",
-       "      <td>1.439700</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>913</td>\n",
-       "      <td>1.561000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>914</td>\n",
-       "      <td>1.443600</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>915</td>\n",
-       "      <td>1.487600</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>916</td>\n",
-       "      <td>1.322300</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>917</td>\n",
-       "      <td>1.318500</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>918</td>\n",
-       "      <td>1.387300</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>919</td>\n",
-       "      <td>1.441600</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>920</td>\n",
-       "      <td>1.519100</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>921</td>\n",
-       "      <td>1.453000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>922</td>\n",
-       "      <td>1.407000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>923</td>\n",
-       "      <td>1.422700</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>924</td>\n",
-       "      <td>1.352900</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>925</td>\n",
-       "      <td>1.494900</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>926</td>\n",
-       "      <td>1.434600</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>927</td>\n",
-       "      <td>1.465200</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>928</td>\n",
-       "      <td>1.417500</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>929</td>\n",
-       "      <td>1.342500</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>930</td>\n",
-       "      <td>1.547600</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>931</td>\n",
-       "      <td>1.545800</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>932</td>\n",
-       "      <td>1.496000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>933</td>\n",
-       "      <td>1.398800</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>934</td>\n",
-       "      <td>1.327900</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>935</td>\n",
-       "      <td>1.587400</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>936</td>\n",
-       "      <td>1.347300</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>937</td>\n",
-       "      <td>1.543000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>938</td>\n",
-       "      <td>1.418500</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>939</td>\n",
-       "      <td>1.396600</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>940</td>\n",
-       "      <td>1.364200</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>941</td>\n",
-       "      <td>1.439700</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>942</td>\n",
-       "      <td>1.523800</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>943</td>\n",
-       "      <td>1.385000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>944</td>\n",
-       "      <td>1.491100</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>945</td>\n",
-       "      <td>1.528500</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>946</td>\n",
-       "      <td>1.536600</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>947</td>\n",
-       "      <td>1.292600</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>948</td>\n",
-       "      <td>1.522600</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>949</td>\n",
-       "      <td>1.438900</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>950</td>\n",
-       "      <td>1.423500</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>951</td>\n",
-       "      <td>1.468600</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>952</td>\n",
-       "      <td>1.486000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>953</td>\n",
-       "      <td>1.542800</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>954</td>\n",
-       "      <td>1.571000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>955</td>\n",
-       "      <td>1.455500</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>956</td>\n",
-       "      <td>1.434000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>957</td>\n",
-       "      <td>1.442600</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>958</td>\n",
-       "      <td>1.448800</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>959</td>\n",
-       "      <td>1.342500</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>960</td>\n",
-       "      <td>1.431400</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>961</td>\n",
-       "      <td>1.475000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>962</td>\n",
-       "      <td>1.483500</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>963</td>\n",
-       "      <td>1.493600</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>964</td>\n",
-       "      <td>1.417400</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>965</td>\n",
-       "      <td>1.352000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>966</td>\n",
-       "      <td>1.603600</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>967</td>\n",
-       "      <td>1.465300</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>968</td>\n",
-       "      <td>1.454300</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>969</td>\n",
-       "      <td>1.563800</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>970</td>\n",
-       "      <td>1.572700</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>971</td>\n",
-       "      <td>1.428400</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>972</td>\n",
-       "      <td>1.561200</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>973</td>\n",
-       "      <td>1.404200</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>974</td>\n",
-       "      <td>1.628700</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>975</td>\n",
-       "      <td>1.593300</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>976</td>\n",
-       "      <td>1.670900</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>977</td>\n",
-       "      <td>1.438500</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>978</td>\n",
-       "      <td>1.325400</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>979</td>\n",
-       "      <td>1.479200</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>980</td>\n",
-       "      <td>1.411100</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>981</td>\n",
-       "      <td>1.362000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>982</td>\n",
-       "      <td>1.348000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>983</td>\n",
-       "      <td>1.381000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>984</td>\n",
-       "      <td>1.415500</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>985</td>\n",
-       "      <td>1.583300</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>986</td>\n",
-       "      <td>1.465600</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>987</td>\n",
-       "      <td>1.495200</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>988</td>\n",
-       "      <td>1.499300</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>989</td>\n",
-       "      <td>1.455300</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>990</td>\n",
-       "      <td>1.452700</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>991</td>\n",
-       "      <td>1.296100</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>992</td>\n",
-       "      <td>1.356300</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>993</td>\n",
-       "      <td>1.505300</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>994</td>\n",
-       "      <td>1.429800</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>995</td>\n",
-       "      <td>1.423700</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>996</td>\n",
-       "      <td>1.547100</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>997</td>\n",
-       "      <td>1.512000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>998</td>\n",
-       "      <td>1.458500</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>999</td>\n",
-       "      <td>1.445100</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>1000</td>\n",
-       "      <td>1.381500</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>1001</td>\n",
-       "      <td>1.508700</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>1002</td>\n",
-       "      <td>1.457800</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>1003</td>\n",
-       "      <td>1.508300</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>1004</td>\n",
-       "      <td>1.370400</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>1005</td>\n",
-       "      <td>1.487900</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>1006</td>\n",
-       "      <td>1.517900</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>1007</td>\n",
-       "      <td>1.492000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>1008</td>\n",
-       "      <td>1.462700</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>1009</td>\n",
-       "      <td>1.397000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>1010</td>\n",
-       "      <td>1.522600</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>1011</td>\n",
-       "      <td>1.492100</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>1012</td>\n",
-       "      <td>1.318800</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>1013</td>\n",
-       "      <td>1.501300</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>1014</td>\n",
-       "      <td>1.491900</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>1015</td>\n",
-       "      <td>1.413900</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>1016</td>\n",
-       "      <td>1.453600</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>1017</td>\n",
-       "      <td>1.459800</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>1018</td>\n",
-       "      <td>1.492700</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>1019</td>\n",
-       "      <td>1.471900</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>1020</td>\n",
-       "      <td>1.328900</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>1021</td>\n",
-       "      <td>1.552300</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>1022</td>\n",
-       "      <td>1.300600</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>1023</td>\n",
-       "      <td>1.366600</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>1024</td>\n",
-       "      <td>1.365000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>1025</td>\n",
-       "      <td>1.420200</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>1026</td>\n",
-       "      <td>1.392600</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>1027</td>\n",
-       "      <td>1.492400</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>1028</td>\n",
-       "      <td>1.524600</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>1029</td>\n",
-       "      <td>1.371600</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>1030</td>\n",
-       "      <td>1.431100</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>1031</td>\n",
-       "      <td>1.471200</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>1032</td>\n",
-       "      <td>1.534200</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>1033</td>\n",
-       "      <td>1.417100</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>1034</td>\n",
-       "      <td>1.394700</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>1035</td>\n",
-       "      <td>1.455900</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>1036</td>\n",
-       "      <td>1.536200</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>1037</td>\n",
-       "      <td>1.626100</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>1038</td>\n",
-       "      <td>1.588400</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>1039</td>\n",
-       "      <td>1.538200</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>1040</td>\n",
-       "      <td>1.375200</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>1041</td>\n",
-       "      <td>1.589300</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>1042</td>\n",
-       "      <td>1.557200</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>1043</td>\n",
-       "      <td>1.526000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>1044</td>\n",
-       "      <td>1.349600</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>1045</td>\n",
-       "      <td>1.420000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>1046</td>\n",
-       "      <td>1.444700</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>1047</td>\n",
-       "      <td>1.411600</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>1048</td>\n",
-       "      <td>1.444600</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>1049</td>\n",
-       "      <td>1.591000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>1050</td>\n",
-       "      <td>1.384300</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>1051</td>\n",
-       "      <td>1.470500</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>1052</td>\n",
-       "      <td>1.380200</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>1053</td>\n",
-       "      <td>1.278600</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>1054</td>\n",
-       "      <td>1.276000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>1055</td>\n",
-       "      <td>1.363100</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>1056</td>\n",
-       "      <td>1.487500</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>1057</td>\n",
-       "      <td>1.583300</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>1058</td>\n",
-       "      <td>1.470100</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>1059</td>\n",
-       "      <td>1.450300</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>1060</td>\n",
-       "      <td>1.449600</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>1061</td>\n",
-       "      <td>1.509500</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>1062</td>\n",
-       "      <td>1.436600</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>1063</td>\n",
-       "      <td>1.538900</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>1064</td>\n",
-       "      <td>1.336300</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>1065</td>\n",
-       "      <td>1.403300</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>1066</td>\n",
-       "      <td>1.440900</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>1067</td>\n",
-       "      <td>1.482600</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>1068</td>\n",
-       "      <td>1.482000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>1069</td>\n",
-       "      <td>1.474700</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>1070</td>\n",
-       "      <td>1.539600</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>1071</td>\n",
-       "      <td>1.492200</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>1072</td>\n",
-       "      <td>1.409400</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>1073</td>\n",
-       "      <td>1.445600</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>1074</td>\n",
-       "      <td>1.339800</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>1075</td>\n",
-       "      <td>1.505300</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>1076</td>\n",
-       "      <td>1.513600</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>1077</td>\n",
-       "      <td>1.508100</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>1078</td>\n",
-       "      <td>1.592900</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>1079</td>\n",
-       "      <td>1.465400</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>1080</td>\n",
-       "      <td>1.285500</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>1081</td>\n",
-       "      <td>1.412400</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>1082</td>\n",
-       "      <td>1.588400</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>1083</td>\n",
-       "      <td>1.369300</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>1084</td>\n",
-       "      <td>1.412800</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>1085</td>\n",
-       "      <td>1.517000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>1086</td>\n",
-       "      <td>1.518100</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>1087</td>\n",
-       "      <td>1.453300</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>1088</td>\n",
-       "      <td>1.358200</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>1089</td>\n",
-       "      <td>1.441300</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>1090</td>\n",
-       "      <td>1.573100</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>1091</td>\n",
-       "      <td>1.470400</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>1092</td>\n",
-       "      <td>1.446200</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>1093</td>\n",
-       "      <td>1.404700</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>1094</td>\n",
-       "      <td>1.325000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>1095</td>\n",
-       "      <td>1.493900</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>1096</td>\n",
-       "      <td>1.340800</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>1097</td>\n",
-       "      <td>1.408600</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>1098</td>\n",
-       "      <td>1.440300</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>1099</td>\n",
-       "      <td>1.479400</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>1100</td>\n",
-       "      <td>1.390100</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>1101</td>\n",
-       "      <td>1.433100</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>1102</td>\n",
-       "      <td>1.412200</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>1103</td>\n",
-       "      <td>1.382300</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>1104</td>\n",
-       "      <td>1.555300</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>1105</td>\n",
-       "      <td>1.388700</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>1106</td>\n",
-       "      <td>1.450600</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>1107</td>\n",
-       "      <td>1.552400</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>1108</td>\n",
-       "      <td>1.364400</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>1109</td>\n",
-       "      <td>1.338100</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>1110</td>\n",
-       "      <td>1.367700</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>1111</td>\n",
-       "      <td>1.418500</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>1112</td>\n",
-       "      <td>1.449400</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>1113</td>\n",
-       "      <td>1.381700</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>1114</td>\n",
-       "      <td>1.358700</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>1115</td>\n",
-       "      <td>1.406300</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>1116</td>\n",
-       "      <td>1.406500</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>1117</td>\n",
-       "      <td>1.363200</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>1118</td>\n",
-       "      <td>1.523900</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>1119</td>\n",
-       "      <td>1.433600</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>1120</td>\n",
-       "      <td>1.452200</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>1121</td>\n",
-       "      <td>1.544300</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>1122</td>\n",
-       "      <td>1.465900</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>1123</td>\n",
-       "      <td>1.377600</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>1124</td>\n",
-       "      <td>1.440300</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>1125</td>\n",
-       "      <td>1.302200</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>1126</td>\n",
-       "      <td>1.468200</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>1127</td>\n",
-       "      <td>1.378600</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>1128</td>\n",
-       "      <td>1.435300</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>1129</td>\n",
-       "      <td>1.479000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>1130</td>\n",
-       "      <td>1.382800</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>1131</td>\n",
-       "      <td>1.424500</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>1132</td>\n",
-       "      <td>1.428200</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>1133</td>\n",
-       "      <td>1.469500</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>1134</td>\n",
-       "      <td>1.468200</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>1135</td>\n",
-       "      <td>1.444400</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>1136</td>\n",
-       "      <td>1.544500</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>1137</td>\n",
-       "      <td>1.431600</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>1138</td>\n",
-       "      <td>1.442000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>1139</td>\n",
-       "      <td>1.537700</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>1140</td>\n",
-       "      <td>1.396300</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>1141</td>\n",
-       "      <td>1.410400</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>1142</td>\n",
-       "      <td>1.438300</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>1143</td>\n",
-       "      <td>1.270800</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>1144</td>\n",
-       "      <td>1.449900</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>1145</td>\n",
-       "      <td>1.492000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>1146</td>\n",
-       "      <td>1.487600</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>1147</td>\n",
-       "      <td>1.369300</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>1148</td>\n",
-       "      <td>1.365100</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>1149</td>\n",
-       "      <td>1.491000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>1150</td>\n",
-       "      <td>1.413800</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>1151</td>\n",
-       "      <td>1.563000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>1152</td>\n",
-       "      <td>1.507800</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>1153</td>\n",
-       "      <td>1.301600</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>1154</td>\n",
-       "      <td>1.511200</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>1155</td>\n",
-       "      <td>1.538100</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>1156</td>\n",
-       "      <td>1.301700</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>1157</td>\n",
-       "      <td>1.379500</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>1158</td>\n",
-       "      <td>1.603100</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>1159</td>\n",
-       "      <td>1.453100</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>1160</td>\n",
-       "      <td>1.422200</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>1161</td>\n",
-       "      <td>1.597700</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>1162</td>\n",
-       "      <td>1.541900</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>1163</td>\n",
-       "      <td>1.456500</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>1164</td>\n",
-       "      <td>1.467500</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>1165</td>\n",
-       "      <td>1.303300</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>1166</td>\n",
-       "      <td>1.495300</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>1167</td>\n",
-       "      <td>1.454000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>1168</td>\n",
-       "      <td>1.562400</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>1169</td>\n",
-       "      <td>1.406800</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>1170</td>\n",
-       "      <td>1.247900</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>1171</td>\n",
-       "      <td>1.631900</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>1172</td>\n",
-       "      <td>1.394800</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>1173</td>\n",
-       "      <td>1.493100</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>1174</td>\n",
-       "      <td>1.379300</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>1175</td>\n",
-       "      <td>1.334400</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>1176</td>\n",
-       "      <td>1.499200</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>1177</td>\n",
-       "      <td>1.505100</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>1178</td>\n",
-       "      <td>1.415100</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>1179</td>\n",
-       "      <td>1.453500</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>1180</td>\n",
-       "      <td>1.368400</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>1181</td>\n",
-       "      <td>1.459900</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>1182</td>\n",
-       "      <td>1.544000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>1183</td>\n",
-       "      <td>1.549300</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>1184</td>\n",
-       "      <td>1.580900</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>1185</td>\n",
-       "      <td>1.456400</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>1186</td>\n",
-       "      <td>1.465700</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>1187</td>\n",
-       "      <td>1.457900</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>1188</td>\n",
-       "      <td>1.497100</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>1189</td>\n",
-       "      <td>1.600700</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>1190</td>\n",
-       "      <td>1.438900</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>1191</td>\n",
-       "      <td>1.406400</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>1192</td>\n",
-       "      <td>1.415300</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>1193</td>\n",
-       "      <td>1.442900</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>1194</td>\n",
-       "      <td>1.488600</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>1195</td>\n",
-       "      <td>1.457500</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>1196</td>\n",
-       "      <td>1.484800</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>1197</td>\n",
-       "      <td>1.455100</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>1198</td>\n",
-       "      <td>1.467500</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>1199</td>\n",
-       "      <td>1.568700</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>1200</td>\n",
-       "      <td>1.466500</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>1201</td>\n",
-       "      <td>1.495300</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>1202</td>\n",
-       "      <td>1.496600</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>1203</td>\n",
-       "      <td>1.500400</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>1204</td>\n",
-       "      <td>1.571200</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>1205</td>\n",
-       "      <td>1.448100</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>1206</td>\n",
-       "      <td>1.405400</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>1207</td>\n",
-       "      <td>1.510100</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>1208</td>\n",
-       "      <td>1.400100</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>1209</td>\n",
-       "      <td>1.461100</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>1210</td>\n",
-       "      <td>1.368100</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>1211</td>\n",
-       "      <td>1.474400</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>1212</td>\n",
-       "      <td>1.363600</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>1213</td>\n",
-       "      <td>1.564700</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>1214</td>\n",
-       "      <td>1.553300</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>1215</td>\n",
-       "      <td>1.326500</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>1216</td>\n",
-       "      <td>1.338000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>1217</td>\n",
-       "      <td>1.407600</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>1218</td>\n",
-       "      <td>1.584600</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>1219</td>\n",
-       "      <td>1.384300</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>1220</td>\n",
-       "      <td>1.461900</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>1221</td>\n",
-       "      <td>1.384800</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>1222</td>\n",
-       "      <td>1.406000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>1223</td>\n",
-       "      <td>1.500400</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>1224</td>\n",
-       "      <td>1.351400</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>1225</td>\n",
-       "      <td>1.399500</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>1226</td>\n",
-       "      <td>1.415000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>1227</td>\n",
-       "      <td>1.287200</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>1228</td>\n",
-       "      <td>1.417100</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>1229</td>\n",
-       "      <td>1.372600</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>1230</td>\n",
-       "      <td>1.329200</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>1231</td>\n",
-       "      <td>1.547300</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>1232</td>\n",
-       "      <td>1.395000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>1233</td>\n",
-       "      <td>1.321300</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>1234</td>\n",
-       "      <td>1.296700</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>1235</td>\n",
-       "      <td>1.414100</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>1236</td>\n",
-       "      <td>1.383600</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>1237</td>\n",
-       "      <td>1.384600</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>1238</td>\n",
-       "      <td>1.401000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>1239</td>\n",
-       "      <td>1.403600</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>1240</td>\n",
-       "      <td>1.572300</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>1241</td>\n",
-       "      <td>1.422600</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>1242</td>\n",
-       "      <td>1.386300</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>1243</td>\n",
-       "      <td>1.365200</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>1244</td>\n",
-       "      <td>1.430600</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>1245</td>\n",
-       "      <td>1.573700</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>1246</td>\n",
-       "      <td>1.518800</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>1247</td>\n",
-       "      <td>1.399000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>1248</td>\n",
-       "      <td>1.408100</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>1249</td>\n",
-       "      <td>1.542400</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>1250</td>\n",
-       "      <td>1.504800</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table><p>"
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "trainer_stats = trainer.train()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "id": "f70aca8b",
-   "metadata": {
-    "cellView": "form",
-    "execution": {
-     "iopub.execute_input": "2024-03-28T03:52:49.697610Z",
-     "iopub.status.busy": "2024-03-28T03:52:49.696564Z",
-     "iopub.status.idle": "2024-03-28T03:52:49.704999Z",
-     "shell.execute_reply": "2024-03-28T03:52:49.703738Z"
-    },
-    "id": "pCqnaKmlO1U9",
-    "outputId": "e34545d2-808b-44b3-80d5-c21ca7a2da16",
-    "papermill": {
-     "duration": 0.146166,
-     "end_time": "2024-03-28T03:52:49.707144",
-     "exception": false,
-     "start_time": "2024-03-28T03:52:49.560978",
-     "status": "completed"
-    },
-    "tags": []
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "13140.0413 seconds used for training.\n",
-      "219.0 minutes used for training.\n",
-      "Peak reserved memory = 7.268 GB.\n",
-      "Peak reserved memory for training = 2.768 GB.\n",
-      "Peak reserved memory % of max memory = 49.281 %.\n",
-      "Peak reserved memory for training % of max memory = 18.769 %.\n"
-     ]
-    }
-   ],
-   "source": [
-    "#@title Show final memory and time stats\n",
-    "used_memory = round(torch.cuda.max_memory_reserved() / 1024 / 1024 / 1024, 3)\n",
-    "used_memory_for_lora = round(used_memory - start_gpu_memory, 3)\n",
-    "used_percentage = round(used_memory         /max_memory*100, 3)\n",
-    "lora_percentage = round(used_memory_for_lora/max_memory*100, 3)\n",
-    "print(f\"{trainer_stats.metrics['train_runtime']} seconds used for training.\")\n",
-    "print(f\"{round(trainer_stats.metrics['train_runtime']/60, 2)} minutes used for training.\")\n",
-    "print(f\"Peak reserved memory = {used_memory} GB.\")\n",
-    "print(f\"Peak reserved memory for training = {used_memory_for_lora} GB.\")\n",
-    "print(f\"Peak reserved memory % of max memory = {used_percentage} %.\")\n",
-    "print(f\"Peak reserved memory for training % of max memory = {lora_percentage} %.\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "8d5dff6e",
-   "metadata": {
-    "id": "ekOmTR1hSNcr",
-    "papermill": {
-     "duration": 0.139123,
-     "end_time": "2024-03-28T03:52:49.982166",
-     "exception": false,
-     "start_time": "2024-03-28T03:52:49.843043",
-     "status": "completed"
-    },
-    "tags": []
-   },
-   "source": [
-    "<a name=\"Inference\"></a>\n",
-    "### Inference\n",
-    "Let's run the model! Since we're using `ChatML`, use `apply_chat_template` with `add_generation_prompt` set to `True` for inference."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "id": "bebbdda7",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-03-28T03:52:50.245764Z",
-     "iopub.status.busy": "2024-03-28T03:52:50.244849Z",
-     "iopub.status.idle": "2024-03-28T03:52:52.425841Z",
-     "shell.execute_reply": "2024-03-28T03:52:52.424679Z"
-    },
-    "id": "kR3gIAX-SM2q",
-    "outputId": "d1b13317-4781-4078-90bf-0de74d93f6e4",
-    "papermill": {
-     "duration": 2.314189,
-     "end_time": "2024-03-28T03:52:52.428079",
-     "exception": false,
-     "start_time": "2024-03-28T03:52:50.113890",
-     "status": "completed"
-    },
-    "tags": []
-   },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Unsloth: Will map <|im_end|> to EOS = <|im_end|>.\n",
-      "The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n",
-      "Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "['<|im_start|>user\\nContinue the fibonnaci sequence: 1, 1, 2, 3, 5, 8,<|im_end|> \\n<|im_start|>assistant\\n13<|im_end|>']"
-      ]
-     },
-     "execution_count": 12,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from unsloth.chat_templates import get_chat_template\n",
-    "\n",
-    "tokenizer = get_chat_template(\n",
-    "    tokenizer,\n",
-    "    chat_template = \"chatml\", # Supports zephyr, chatml, mistral, llama, alpaca, vicuna, vicuna_old, unsloth\n",
-    "    mapping = {\"role\" : \"from\", \"content\" : \"value\", \"user\" : \"human\", \"assistant\" : \"gpt\"}, # ShareGPT style\n",
-    "    map_eos_token = True, # Maps <|im_end|> to </s> instead\n",
-    ")\n",
-    "\n",
-    "FastLanguageModel.for_inference(model) # Enable native 2x faster inference\n",
-    "\n",
-    "messages = [\n",
-    "    {\"from\": \"human\", \"value\": \"Continue the fibonnaci sequence: 1, 1, 2, 3, 5, 8,\"},\n",
-    "]\n",
-    "inputs = tokenizer.apply_chat_template(\n",
-    "    messages,\n",
-    "    tokenize = True,\n",
-    "    add_generation_prompt = True, # Must add for generation\n",
-    "    return_tensors = \"pt\",\n",
-    ").to(\"cuda\")\n",
-    "\n",
-    "outputs = model.generate(input_ids = inputs, max_new_tokens = 64, use_cache = True)\n",
-    "tokenizer.batch_decode(outputs)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "6c7afa6f",
-   "metadata": {
-    "id": "CrSvZObor0lY",
-    "papermill": {
-     "duration": 0.138692,
-     "end_time": "2024-03-28T03:52:52.712525",
-     "exception": false,
-     "start_time": "2024-03-28T03:52:52.573833",
-     "status": "completed"
-    },
-    "tags": []
-   },
-   "source": [
-    " You can also use a `TextStreamer` for continuous inference - so you can see the generation token by token, instead of waiting the whole time!"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "id": "5cf2ad38",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-03-28T03:52:52.996612Z",
-     "iopub.status.busy": "2024-03-28T03:52:52.996171Z",
-     "iopub.status.idle": "2024-03-28T03:52:53.413691Z",
-     "shell.execute_reply": "2024-03-28T03:52:53.412641Z"
-    },
-    "id": "e2pEuRb1r2Vg",
-    "outputId": "3b7b291c-8237-4473-c3db-8bc5ebbf07f9",
-    "papermill": {
-     "duration": 0.561247,
-     "end_time": "2024-03-28T03:52:53.415994",
-     "exception": false,
-     "start_time": "2024-03-28T03:52:52.854747",
-     "status": "completed"
-    },
-    "tags": []
-   },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n",
-      "Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "<|im_start|>user\n",
-      "Continue the fibonnaci sequence: 1, 1, 2, 3, 5, 8,<|im_end|> \n",
-      "<|im_start|>assistant\n",
-      "13<|im_end|>\n"
-     ]
-    }
-   ],
-   "source": [
-    "FastLanguageModel.for_inference(model) # Enable native 2x faster inference\n",
-    "\n",
-    "messages = [\n",
-    "    {\"from\": \"human\", \"value\": \"Continue the fibonnaci sequence: 1, 1, 2, 3, 5, 8,\"},\n",
-    "]\n",
-    "inputs = tokenizer.apply_chat_template(\n",
-    "    messages,\n",
-    "    tokenize = True,\n",
-    "    add_generation_prompt = True, # Must add for generation\n",
-    "    return_tensors = \"pt\",\n",
-    ").to(\"cuda\")\n",
-    "\n",
-    "from transformers import TextStreamer\n",
-    "text_streamer = TextStreamer(tokenizer)\n",
-    "_ = model.generate(input_ids = inputs, streamer = text_streamer, max_new_tokens = 128, use_cache = True)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "92f1fa55",
-   "metadata": {
-    "id": "uMuVrWbjAzhc",
-    "papermill": {
-     "duration": 0.129027,
-     "end_time": "2024-03-28T03:52:53.687793",
-     "exception": false,
-     "start_time": "2024-03-28T03:52:53.558766",
-     "status": "completed"
-    },
-    "tags": []
-   },
-   "source": [
-    "<a name=\"Save\"></a>\n",
-    "### Saving, loading finetuned models\n",
-    "To save the final model as LoRA adapters, either use Huggingface's `push_to_hub` for an online save or `save_pretrained` for a local save.\n",
-    "\n",
-    "**[NOTE]** This ONLY saves the LoRA adapters, and not the full model. To save to 16bit or GGUF, scroll down!"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "id": "ab909818",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-03-28T03:52:53.959109Z",
-     "iopub.status.busy": "2024-03-28T03:52:53.958163Z",
-     "iopub.status.idle": "2024-03-28T03:53:02.098759Z",
-     "shell.execute_reply": "2024-03-28T03:53:02.097480Z"
-    },
-    "id": "upcOlWe7A1vc",
-    "papermill": {
-     "duration": 8.274548,
-     "end_time": "2024-03-28T03:53:02.101048",
-     "exception": false,
-     "start_time": "2024-03-28T03:52:53.826500",
-     "status": "completed"
-    },
-    "tags": []
-   },
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "50b8d4fbd7064930bbdf338796e9f09b",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "README.md:   0%|          | 0.00/579 [00:00<?, ?B/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "cd246bcf2d034bb2af58ceb7524df6c1",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "adapter_model.safetensors:   0%|          | 0.00/168M [00:00<?, ?B/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Saved model to https://huggingface.co/scoliono/groupchat_lora\n"
-     ]
-    }
-   ],
-   "source": [
-    "model.save_pretrained(\"lora_model\") # Local saving\n",
-    "#model.push_to_hub(\"scoliono/groupchat_lora\", token = \"\") # Online saving"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "a4861d1b",
-   "metadata": {
-    "id": "AEEcJ4qfC7Lp",
-    "papermill": {
-     "duration": 0.145328,
-     "end_time": "2024-03-28T03:53:02.385386",
-     "exception": false,
-     "start_time": "2024-03-28T03:53:02.240058",
-     "status": "completed"
-    },
-    "tags": []
-   },
-   "source": [
-    "Now if you want to load the LoRA adapters we just saved for inference, set `False` to `True`:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "id": "a93cbbb6",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-03-28T03:53:02.657540Z",
-     "iopub.status.busy": "2024-03-28T03:53:02.657048Z",
-     "iopub.status.idle": "2024-03-28T03:53:03.262596Z",
-     "shell.execute_reply": "2024-03-28T03:53:03.261476Z"
-    },
-    "id": "MKX_XKs_BNZR",
-    "outputId": "d8dbd499-1881-41b1-9347-d3213ab473df",
-    "papermill": {
-     "duration": 0.738494,
-     "end_time": "2024-03-28T03:53:03.264761",
-     "exception": false,
-     "start_time": "2024-03-28T03:53:02.526267",
-     "status": "completed"
-    },
-    "tags": []
-   },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n",
-      "Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "<|im_start|>user\n",
-      "What is a famous tall tower in Paris?<|im_end|> \n",
-      "<|im_start|>assistant\n",
-      "Eiffel tower<|im_end|>\n"
-     ]
-    }
-   ],
-   "source": [
-    "if False:\n",
-    "    from unsloth import FastLanguageModel\n",
-    "    model, tokenizer = FastLanguageModel.from_pretrained(\n",
-    "        model_name = \"lora_model\", # YOUR MODEL YOU USED FOR TRAINING\n",
-    "        max_seq_length = max_seq_length,\n",
-    "        dtype = dtype,\n",
-    "        load_in_4bit = load_in_4bit,\n",
-    "    )\n",
-    "    FastLanguageModel.for_inference(model) # Enable native 2x faster inference\n",
-    "\n",
-    "messages = [\n",
-    "    {\"from\": \"human\", \"value\": \"What is a famous tall tower in Paris?\"},\n",
-    "]\n",
-    "inputs = tokenizer.apply_chat_template(\n",
-    "    messages,\n",
-    "    tokenize = True,\n",
-    "    add_generation_prompt = True, # Must add for generation\n",
-    "    return_tensors = \"pt\",\n",
-    ").to(\"cuda\")\n",
-    "\n",
-    "from transformers import TextStreamer\n",
-    "text_streamer = TextStreamer(tokenizer)\n",
-    "_ = model.generate(input_ids = inputs, streamer = text_streamer, max_new_tokens = 128, use_cache = True)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "9ada1c2a",
-   "metadata": {
-    "id": "QQMjaNrjsU5_",
-    "papermill": {
-     "duration": 0.126538,
-     "end_time": "2024-03-28T03:53:03.522957",
-     "exception": false,
-     "start_time": "2024-03-28T03:53:03.396419",
-     "status": "completed"
-    },
-    "tags": []
-   },
-   "source": [
-    "You can also use Hugging Face's `AutoModelForPeftCausalLM`. Only use this if you do not have `unsloth` installed. It can be hopelessly slow, since `4bit` model downloading is not supported, and Unsloth's **inference is 2x faster**."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "id": "3c9e54cd",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-03-28T03:53:03.788897Z",
-     "iopub.status.busy": "2024-03-28T03:53:03.788477Z",
-     "iopub.status.idle": "2024-03-28T03:53:03.793816Z",
-     "shell.execute_reply": "2024-03-28T03:53:03.792849Z"
-    },
-    "id": "yFfaXG0WsQuE",
-    "papermill": {
-     "duration": 0.139822,
-     "end_time": "2024-03-28T03:53:03.795815",
-     "exception": false,
-     "start_time": "2024-03-28T03:53:03.655993",
-     "status": "completed"
-    },
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "if False:\n",
-    "    # I highly do NOT suggest - use Unsloth if possible\n",
-    "    from peft import AutoModelForPeftCausalLM\n",
-    "    from transformers import AutoTokenizer\n",
-    "    model = AutoModelForPeftCausalLM.from_pretrained(\n",
-    "        \"lora_model\", # YOUR MODEL YOU USED FOR TRAINING\n",
-    "        load_in_4bit = load_in_4bit,\n",
-    "    )\n",
-    "    tokenizer = AutoTokenizer.from_pretrained(\"lora_model\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "ae8b6865",
-   "metadata": {
-    "id": "f422JgM9sdVT",
-    "papermill": {
-     "duration": 0.133177,
-     "end_time": "2024-03-28T03:53:04.058229",
-     "exception": false,
-     "start_time": "2024-03-28T03:53:03.925052",
-     "status": "completed"
-    },
-    "tags": []
-   },
-   "source": [
-    "### Saving to float16 for VLLM\n",
-    "\n",
-    "We also support saving to `float16` directly. Select `merged_16bit` for float16 or `merged_4bit` for int4. We also allow `lora` adapters as a fallback. Use `push_to_hub_merged` to upload to your Hugging Face account! You can go to https://huggingface.co/settings/tokens for your personal tokens."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "id": "73bff174",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-03-28T03:53:04.324460Z",
-     "iopub.status.busy": "2024-03-28T03:53:04.324036Z",
-     "iopub.status.idle": "2024-03-28T03:53:04.331159Z",
-     "shell.execute_reply": "2024-03-28T03:53:04.330165Z"
-    },
-    "id": "iHjt_SMYsd3P",
-    "papermill": {
-     "duration": 0.140814,
-     "end_time": "2024-03-28T03:53:04.333322",
-     "exception": false,
-     "start_time": "2024-03-28T03:53:04.192508",
-     "status": "completed"
-    },
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "# Merge to 16bit\n",
-    "if False: model.save_pretrained_merged(\"model\", tokenizer, save_method = \"merged_16bit\",)\n",
-    "if False: model.push_to_hub_merged(\"hf/model\", tokenizer, save_method = \"merged_16bit\", token = \"\")\n",
-    "\n",
-    "# Merge to 4bit\n",
-    "if False: model.save_pretrained_merged(\"model\", tokenizer, save_method = \"merged_4bit\",)\n",
-    "if False: model.push_to_hub_merged(\"hf/model\", tokenizer, save_method = \"merged_4bit\", token = \"\")\n",
-    "\n",
-    "# Just LoRA adapters\n",
-    "if False: model.save_pretrained_merged(\"model\", tokenizer, save_method = \"lora\",)\n",
-    "if False: model.push_to_hub_merged(\"hf/model\", tokenizer, save_method = \"lora\", token = \"\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "96270533",
-   "metadata": {
-    "id": "TCv4vXHd61i7",
-    "papermill": {
-     "duration": 0.141816,
-     "end_time": "2024-03-28T03:53:04.663103",
-     "exception": false,
-     "start_time": "2024-03-28T03:53:04.521287",
-     "status": "completed"
-    },
-    "tags": []
-   },
-   "source": [
-    "### GGUF / llama.cpp Conversion\n",
-    "To save to `GGUF` / `llama.cpp`, we support it natively now! We clone `llama.cpp` and we default save it to `q8_0`. We allow all methods like `q4_k_m`. Use `save_pretrained_gguf` for local saving and `push_to_hub_gguf` for uploading to HF.\n",
-    "\n",
-    "Some supported quant methods (full list on our [Wiki page](https://github.com/unslothai/unsloth/wiki#gguf-quantization-options)):\n",
-    "* `q8_0` - Fast conversion. High resource use, but generally acceptable.\n",
-    "* `q4_k_m` - Recommended. Uses Q6_K for half of the attention.wv and feed_forward.w2 tensors, else Q4_K.\n",
-    "* `q5_k_m` - Recommended. Uses Q6_K for half of the attention.wv and feed_forward.w2 tensors, else Q5_K."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "id": "cd67a84a",
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-     "shell.execute_reply": "2024-03-28T03:53:04.947106Z"
-    },
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-     "status": "completed"
-    },
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "# Save to 8bit Q8_0\n",
-    "if False: model.save_pretrained_gguf(\"model\", tokenizer,)\n",
-    "if False: model.push_to_hub_gguf(\"hf/model\", tokenizer, token = \"\")\n",
-    "\n",
-    "# Save to 16bit GGUF\n",
-    "if False: model.save_pretrained_gguf(\"model\", tokenizer, quantization_method = \"f16\")\n",
-    "if False: model.push_to_hub_gguf(\"hf/model\", tokenizer, quantization_method = \"f16\", token = \"\")\n",
-    "\n",
-    "# Save to q4_k_m GGUF\n",
-    "if False: model.save_pretrained_gguf(\"model\", tokenizer, quantization_method = \"q4_k_m\")\n",
-    "if False: model.push_to_hub_gguf(\"hf/model\", tokenizer, quantization_method = \"q4_k_m\", token = \"\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "974bde3a",
-   "metadata": {
-    "id": "bDp0zNpwe6U_",
-    "papermill": {
-     "duration": 0.159571,
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-     "exception": false,
-     "start_time": "2024-03-28T03:53:05.103480",
-     "status": "completed"
-    },
-    "tags": []
-   },
-   "source": [
-    "Now, use the `model-unsloth.gguf` file or `model-unsloth-Q4_K_M.gguf` file in `llama.cpp` or a UI based system like `GPT4All`. You can install GPT4All by going [here](https://gpt4all.io/index.html)."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "c25b0c14",
-   "metadata": {
-    "id": "Zt9CHJqO6p30",
-    "papermill": {
-     "duration": 0.126368,
-     "end_time": "2024-03-28T03:53:05.527719",
-     "exception": false,
-     "start_time": "2024-03-28T03:53:05.401351",
-     "status": "completed"
-    },
-    "tags": []
-   },
-   "source": [
-    "And we're done! If you have any questions on Unsloth, we have a [Discord](https://discord.gg/u54VK8m8tk) channel! If you find any bugs or want to keep updated with the latest LLM stuff, or need help, join projects etc, feel free to join our Discord!\n",
-    "\n",
-    "Some other links:\n",
-    "1. Zephyr DPO 2x faster [free Colab](https://colab.research.google.com/drive/15vttTpzzVXv_tJwEk-hIcQ0S9FcEWvwP?usp=sharing)\n",
-    "2. Llama 7b 2x faster [free Colab](https://colab.research.google.com/drive/1lBzz5KeZJKXjvivbYvmGarix9Ao6Wxe5?usp=sharing)\n",
-    "3. TinyLlama 4x faster full Alpaca 52K in 1 hour [free Colab](https://colab.research.google.com/drive/1AZghoNBQaMDgWJpi4RbffGM1h6raLUj9?usp=sharing)\n",
-    "4. CodeLlama 34b 2x faster [A100 on Colab](https://colab.research.google.com/drive/1y7A0AxE3y8gdj4AVkl2aZX47Xu3P1wJT?usp=sharing)\n",
-    "5. Mistral 7b [free Kaggle version](https://www.kaggle.com/code/danielhanchen/kaggle-mistral-7b-unsloth-notebook)\n",
-    "6. We also did a [blog](https://huggingface.co/blog/unsloth-trl) with 🤗 HuggingFace, and we're in the TRL [docs](https://huggingface.co/docs/trl/main/en/sft_trainer#accelerate-fine-tuning-2x-using-unsloth)!\n",
-    "7. Text completions like novel writing [notebook](https://colab.research.google.com/drive/1ef-tab5bhkvWmBOObepl1WgJvfvSzn5Q?usp=sharing)\n",
-    "9. Gemma 6 trillion tokens is 2.5x faster! [free Colab](https://colab.research.google.com/drive/10NbwlsRChbma1v55m8LAPYG15uQv6HLo?usp=sharing)\n",
-    "\n",
-    "<div class=\"align-center\">\n",
-    "  <a href=\"https://github.com/unslothai/unsloth\"><img src=\"https://github.com/unslothai/unsloth/raw/main/images/unsloth%20new%20logo.png\" width=\"115\"></a>\n",
-    "  <a href=\"https://discord.gg/u54VK8m8tk\"><img src=\"https://github.com/unslothai/unsloth/raw/main/images/Discord.png\" width=\"145\"></a>\n",
-    "  <a href=\"https://ko-fi.com/unsloth\"><img src=\"https://github.com/unslothai/unsloth/raw/main/images/Kofi button.png\" width=\"145\"></a></a> Support our work if you can! Thanks!\n",
-    "</div>"
-   ]
-  }
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run this, press \"*Runtime*\" and press \"*Run all*\" on a **free** Tesla T4 Google Colab instance!\n<div class=\"align-center\">\n  <a href=\"https://github.com/unslothai/unsloth\"><img src=\"https://github.com/unslothai/unsloth/raw/main/images/unsloth%20new%20logo.png\" width=\"115\"></a>\n  <a href=\"https://discord.gg/u54VK8m8tk\"><img src=\"https://github.com/unslothai/unsloth/raw/main/images/Discord button.png\" width=\"145\"></a>\n  <a href=\"https://ko-fi.com/unsloth\"><img src=\"https://github.com/unslothai/unsloth/raw/main/images/Kofi button.png\" width=\"145\"></a></a> Join Discord if you need help + support us if you can!\n</div>\n\nTo install Unsloth on your own computer, follow the installation instructions on our Github page [here](https://github.com/unslothai/unsloth#installation-instructions---conda).\n\nYou will learn how to do [data prep](#Data), how to [train](#Train), how to [run the model](#Inference), & [how to save it](#Save) (eg for Llama.cpp).","metadata":{"id":"IqM-T1RTzY6C"}},{"cell_type":"markdown","source":"## Kaggle is slow - you'll have to wait **5 minutes** for it to install.\n\nI suggest you to use our free Colab notebooks instead. I linked our Mistral Colab notebook here: [notebook](https://colab.research.google.com/drive/1Dyauq4kTZoLewQ1cApceUQVNcnnNTzg_?usp=sharing)","metadata":{}},{"cell_type":"code","source":"%%capture\n!pip install -U \"xformers<0.0.26\" --index-url https://download.pytorch.org/whl/cu121\n!pip install \"unsloth[kaggle-new] @ git+https://github.com/unslothai/unsloth.git\"\n\n# Temporary fix for https://github.com/huggingface/datasets/issues/6753\n!pip install datasets==2.16.0 fsspec==2023.10.0 gcsfs==2023.10.0\n\nimport os\nos.environ[\"WANDB_DISABLED\"] = \"true\"","metadata":{"execution":{"iopub.status.busy":"2024-05-25T04:02:01.128438Z","iopub.execute_input":"2024-05-25T04:02:01.128773Z","iopub.status.idle":"2024-05-25T04:05:55.463554Z","shell.execute_reply.started":"2024-05-25T04:02:01.128749Z","shell.execute_reply":"2024-05-25T04:05:55.462209Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"* We support Llama, Mistral, CodeLlama, TinyLlama, Vicuna, Open Hermes etc\n* And Yi, Qwen ([llamafied](https://huggingface.co/models?sort=trending&search=qwen+llama)), Deepseek, all Llama, Mistral derived archs.\n* We support 16bit LoRA or 4bit QLoRA. Both 2x faster.\n* `max_seq_length` can be set to anything, since we do automatic RoPE Scaling via [kaiokendev's](https://kaiokendev.github.io/til) method.\n* [**NEW**] With [PR 26037](https://github.com/huggingface/transformers/pull/26037), we support downloading 4bit models **4x faster**! [Our repo](https://huggingface.co/unsloth) has Llama, Mistral 4bit models.","metadata":{"id":"r2v_X2fA0Df5"}},{"cell_type":"code","source":"from unsloth import FastLanguageModel\nimport torch\nmax_seq_length = 2048 # Choose any! We auto support RoPE Scaling internally!\ndtype = None # None for auto detection. Float16 for Tesla T4, V100, Bfloat16 for Ampere+\nload_in_4bit = True # Use 4bit quantization to reduce memory usage. Can be False.\n\n# 4bit pre quantized models we support for 4x faster downloading + no OOMs.\nfourbit_models = [\n    \"unsloth/mistral-7b-bnb-4bit\",\n    \"unsloth/mistral-7b-instruct-v0.2-bnb-4bit\",\n    \"unsloth/llama-2-7b-bnb-4bit\",\n    \"unsloth/llama-2-13b-bnb-4bit\",\n    \"unsloth/codellama-34b-bnb-4bit\",\n    \"unsloth/tinyllama-bnb-4bit\",\n    \"unsloth/llama-3-8b-bnb-4bit\",\n    \"unsloth/llama-3-70b-bnb-4bit\",\n] # More models at https://huggingface.co/unsloth\n\nmodel, tokenizer = FastLanguageModel.from_pretrained(\n    model_name = \"Orenguteng/Llama-3-8B-Lexi-Uncensored\", # Choose ANY! eg teknium/OpenHermes-2.5-Mistral-7B\n    max_seq_length = max_seq_length,\n    dtype = dtype,\n    load_in_4bit = load_in_4bit,\n    use_gradient_checkpointing = \"unsloth\", # We cut memory usage by a further 30% and now support fine-tuning of LLMs with 4x longer context windows!\n    # token = \"hf_...\", # use one if using gated models like meta-llama/Llama-2-7b-hf\n)","metadata":{"id":"QmUBVEnvCDJv","outputId":"5eff0d61-05b4-471c-eea2-c2e84a915109","execution":{"iopub.status.busy":"2024-05-25T04:06:55.008762Z","iopub.execute_input":"2024-05-25T04:06:55.009117Z","iopub.status.idle":"2024-05-25T04:07:35.338067Z","shell.execute_reply.started":"2024-05-25T04:06:55.009090Z","shell.execute_reply":"2024-05-25T04:07:35.337098Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"We now add LoRA adapters so we only need to update 1 to 10% of all parameters!","metadata":{"id":"SXd9bTZd1aaL"}},{"cell_type":"code","source":"model = FastLanguageModel.get_peft_model(\n    model,\n    r = 32, # Choose any number > 0 ! Suggested 8, 16, 32, 64, 128\n    target_modules = [\"q_proj\", \"k_proj\", \"v_proj\", \"o_proj\",\n                      \"gate_proj\", \"up_proj\", \"down_proj\",],\n    lora_alpha = 16,\n    lora_dropout = 0, # Supports any, but = 0 is optimized\n    bias = \"none\",    # Supports any, but = \"none\" is optimized\n    use_gradient_checkpointing = \"unsloth\", # 4x longer contexts auto supported!\n    random_state = 3407,\n    use_rslora = False,  # We support rank stabilized LoRA\n    loftq_config = None, # And LoftQ\n)","metadata":{"id":"6bZsfBuZDeCL","outputId":"b630cc80-ff95-45a2-cc0d-38666010d73b","execution":{"iopub.status.busy":"2024-05-25T04:23:33.920458Z","iopub.execute_input":"2024-05-25T04:23:33.920865Z","iopub.status.idle":"2024-05-25T04:23:34.015573Z","shell.execute_reply.started":"2024-05-25T04:23:33.920836Z","shell.execute_reply":"2024-05-25T04:23:34.014490Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"<a name=\"Data\"></a>\n### Data Prep\nWe now use the Alpaca dataset from [yahma](https://huggingface.co/datasets/yahma/alpaca-cleaned), which is a filtered version of 52K of the original [Alpaca dataset](https://crfm.stanford.edu/2023/03/13/alpaca.html). You can replace this code section with your own data prep.\n\n**[NOTE]** To train only on completions (ignoring the user's input) read TRL's docs [here](https://huggingface.co/docs/trl/sft_trainer#train-on-completions-only).\n\n**[NOTE]** Remember to add the **EOS_TOKEN** to the tokenized output!! Otherwise you'll get infinite generations!\n\nIf you want to use the `ChatML` template for ShareGPT datasets, try our conversational [notebook](https://colab.research.google.com/drive/1Aau3lgPzeZKQ-98h69CCu1UJcvIBLmy2?usp=sharing).\n\nFor text completions like novel writing, try this [notebook](https://colab.research.google.com/drive/1ef-tab5bhkvWmBOObepl1WgJvfvSzn5Q?usp=sharing).","metadata":{"id":"vITh0KVJ10qX"}},{"cell_type":"code","source":"from datasets import load_dataset\nimport json\nfrom unsloth.chat_templates import get_chat_template\n\ntokenizer = get_chat_template(\n    tokenizer,\n    chat_template = \"llama-3\", # Supports zephyr, chatml, mistral, llama, alpaca, vicuna, vicuna_old, unsloth\n    #mapping = {\"role\" : \"from\", \"content\" : \"value\", \"user\" : \"human\", \"assistant\" : \"gpt\"}, # ShareGPT style\n    map_eos_token = True, # Maps <|im_end|> to </s> instead\n)\n\ndef formatting_prompts_func(convos):\n    texts = [tokenizer.apply_chat_template(convo, tokenize = False, add_generation_prompt = False) for convo in convos]\n    return { \"text\" : texts, }\n\nwith open(\"/kaggle/input/the-group-chat/output-10k-c-dropout.json\") as chatfile:\n    convos = [json.loads(j) for j in chatfile.readlines()]\n\nwith open(\"/kaggle/input/toxicqa/toxicQAfinal.json\") as chatfile:\n    convos += [json.loads(j) for j in chatfile.readlines()]\n    \ndataset = formatting_prompts_func(convos)","metadata":{"id":"LjY75GoYUCB8","outputId":"9f40f734-788c-4793-c1af-e9d003337612","execution":{"iopub.status.busy":"2024-05-25T04:28:11.710969Z","iopub.execute_input":"2024-05-25T04:28:11.711971Z","iopub.status.idle":"2024-05-25T04:28:13.097432Z","shell.execute_reply.started":"2024-05-25T04:28:11.711936Z","shell.execute_reply":"2024-05-25T04:28:13.096601Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"from datasets import Dataset\ndataset = Dataset.from_dict(dataset)","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"<a name=\"Train\"></a>\n### Train the model\nNow let's use Huggingface TRL's `SFTTrainer`! More docs here: [TRL SFT docs](https://huggingface.co/docs/trl/sft_trainer). We do 60 steps to speed things up, but you can set `num_train_epochs=1` for a full run, and turn off `max_steps=None`. We also support TRL's `DPOTrainer`!","metadata":{"id":"idAEIeSQ3xdS"}},{"cell_type":"code","source":"from trl import SFTTrainer\nfrom transformers import TrainingArguments\n\ntrainer = SFTTrainer(\n    model = model,\n    tokenizer = tokenizer,\n    train_dataset = dataset,\n    dataset_text_field = \"text\",\n    max_seq_length = max_seq_length,\n    dataset_num_proc = 2,\n    packing = False, # Can make training 5x faster for short sequences.\n    args = TrainingArguments(\n        per_device_train_batch_size = 2,\n        gradient_accumulation_steps = 4,\n        warmup_steps = 5,\n        num_train_epochs=1,\n        learning_rate = 2e-4,\n        fp16 = not torch.cuda.is_bf16_supported(),\n        bf16 = torch.cuda.is_bf16_supported(),\n        logging_steps = 1,\n        optim = \"adamw_8bit\",\n        weight_decay = 0.01,\n        lr_scheduler_type = \"linear\",\n        seed = 3407,\n        output_dir = \"outputs\",\n        report_to = \"none\",\n    ),\n)","metadata":{"id":"95_Nn-89DhsL","outputId":"4b809e6d-271f-446f-dec8-abe0d13259f8","execution":{"iopub.status.busy":"2024-05-25T04:28:27.973142Z","iopub.execute_input":"2024-05-25T04:28:27.973856Z","iopub.status.idle":"2024-05-25T04:28:28.119131Z","shell.execute_reply.started":"2024-05-25T04:28:27.973822Z","shell.execute_reply":"2024-05-25T04:28:28.117976Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"#@title Show current memory stats\ngpu_stats = torch.cuda.get_device_properties(0)\nstart_gpu_memory = round(torch.cuda.max_memory_reserved() / 1024 / 1024 / 1024, 3)\nmax_memory = round(gpu_stats.total_memory / 1024 / 1024 / 1024, 3)\nprint(f\"GPU = {gpu_stats.name}. Max memory = {max_memory} GB.\")\nprint(f\"{start_gpu_memory} GB of memory reserved.\")","metadata":{"id":"2ejIt2xSNKKp","cellView":"form","outputId":"4815a050-0c0f-4a6a-9d93-b01c44eaea35","execution":{"iopub.status.busy":"2024-04-06T16:21:16.730485Z","iopub.execute_input":"2024-04-06T16:21:16.730782Z","iopub.status.idle":"2024-04-06T16:21:16.737279Z","shell.execute_reply.started":"2024-04-06T16:21:16.730754Z","shell.execute_reply":"2024-04-06T16:21:16.736403Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"trainer_stats = trainer.train()","metadata":{"id":"yqxqAZ7KJ4oL","outputId":"3cf26aac-6042-4458-c4a6-d8849efb6a95","execution":{"iopub.status.busy":"2024-04-06T16:21:16.738651Z","iopub.execute_input":"2024-04-06T16:21:16.739026Z","iopub.status.idle":"2024-04-06T16:30:10.783093Z","shell.execute_reply.started":"2024-04-06T16:21:16.738993Z","shell.execute_reply":"2024-04-06T16:30:10.782238Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"#@title Show final memory and time stats\nused_memory = round(torch.cuda.max_memory_reserved() / 1024 / 1024 / 1024, 3)\nused_memory_for_lora = round(used_memory - start_gpu_memory, 3)\nused_percentage = round(used_memory         /max_memory*100, 3)\nlora_percentage = round(used_memory_for_lora/max_memory*100, 3)\nprint(f\"{trainer_stats.metrics['train_runtime']} seconds used for training.\")\nprint(f\"{round(trainer_stats.metrics['train_runtime']/60, 2)} minutes used for training.\")\nprint(f\"Peak reserved memory = {used_memory} GB.\")\nprint(f\"Peak reserved memory for training = {used_memory_for_lora} GB.\")\nprint(f\"Peak reserved memory % of max memory = {used_percentage} %.\")\nprint(f\"Peak reserved memory for training % of max memory = {lora_percentage} %.\")","metadata":{"id":"pCqnaKmlO1U9","cellView":"form","outputId":"cf63d152-e152-468c-ba0d-938e0d2f71a0","execution":{"iopub.status.busy":"2024-04-06T16:30:10.784435Z","iopub.execute_input":"2024-04-06T16:30:10.7848Z","iopub.status.idle":"2024-04-06T16:30:10.791887Z","shell.execute_reply.started":"2024-04-06T16:30:10.784767Z","shell.execute_reply":"2024-04-06T16:30:10.791092Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"<a name=\"Inference\"></a>\n### Inference\nLet's run the model! You can change the instruction and input - leave the output blank!","metadata":{"id":"ekOmTR1hSNcr"}},{"cell_type":"code","source":"if False:\n    # alpaca_prompt = Copied from above\n    FastLanguageModel.for_inference(model) # Enable native 2x faster inference\n    inputs = tokenizer(\n    [\n        alpaca_prompt.format(\n            \"Continue the fibonnaci sequence.\", # instruction\n            \"1, 1, 2, 3, 5, 8\", # input\n            \"\", # output - leave this blank for generation!\n        )\n    ], return_tensors = \"pt\").to(\"cuda\")\n\n    outputs = model.generate(**inputs, max_new_tokens = 64, use_cache = True)\n    tokenizer.batch_decode(outputs)","metadata":{"id":"kR3gIAX-SM2q","outputId":"5b71f982-38c0-44c8-a4e5-58cd20b5a585","execution":{"iopub.status.busy":"2024-04-06T16:30:10.793045Z","iopub.execute_input":"2024-04-06T16:30:10.793321Z","iopub.status.idle":"2024-04-06T16:30:13.837651Z","shell.execute_reply.started":"2024-04-06T16:30:10.793298Z","shell.execute_reply":"2024-04-06T16:30:13.836679Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":" You can also use a `TextStreamer` for continuous inference - so you can see the generation token by token, instead of waiting the whole time!","metadata":{"id":"CrSvZObor0lY"}},{"cell_type":"code","source":"if False:\n    # alpaca_prompt = Copied from above\n    FastLanguageModel.for_inference(model) # Enable native 2x faster inference\n    inputs = tokenizer(\n    [\n        alpaca_prompt.format(\n            \"Continue the fibonnaci sequence.\", # instruction\n            \"1, 1, 2, 3, 5, 8\", # input\n            \"\", # output - leave this blank for generation!\n        )\n    ], return_tensors = \"pt\").to(\"cuda\")\n\n    from transformers import TextStreamer\n    text_streamer = TextStreamer(tokenizer)\n    _ = model.generate(**inputs, streamer = text_streamer, max_new_tokens = 128)","metadata":{"id":"e2pEuRb1r2Vg","outputId":"084aab62-2122-436a-c0cb-8871986640eb","execution":{"iopub.status.busy":"2024-04-06T16:30:13.840849Z","iopub.execute_input":"2024-04-06T16:30:13.841138Z","iopub.status.idle":"2024-04-06T16:30:15.541954Z","shell.execute_reply.started":"2024-04-06T16:30:13.841114Z","shell.execute_reply":"2024-04-06T16:30:15.54076Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"<a name=\"Save\"></a>\n### Saving, loading finetuned models\nTo save the final model as LoRA adapters, either use Huggingface's `push_to_hub` for an online save or `save_pretrained` for a local save.\n\n**[NOTE]** This ONLY saves the LoRA adapters, and not the full model. To save to 16bit or GGUF, scroll down!","metadata":{"id":"uMuVrWbjAzhc"}},{"cell_type":"code","source":"#model.save_pretrained(\"lora_model\") # Local saving\nmodel.push_to_hub(\"scoliono/groupchat_lora_lexi_8b\", token = \"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\")","metadata":{"id":"upcOlWe7A1vc","execution":{"iopub.status.busy":"2024-04-06T16:30:15.543701Z","iopub.execute_input":"2024-04-06T16:30:15.544355Z","iopub.status.idle":"2024-04-06T16:30:16.234142Z","shell.execute_reply.started":"2024-04-06T16:30:15.544315Z","shell.execute_reply":"2024-04-06T16:30:16.233363Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"Now if you want to load the LoRA adapters we just saved for inference, set `False` to `True`:","metadata":{"id":"AEEcJ4qfC7Lp"}},{"cell_type":"code","source":"if False:\n    from unsloth import FastLanguageModel\n    model, tokenizer = FastLanguageModel.from_pretrained(\n        model_name = \"scoliono/groupchat_lora_instruct\", # YOUR MODEL YOU USED FOR TRAINING\n        max_seq_length = max_seq_length,\n        dtype = dtype,\n        load_in_4bit = load_in_4bit,\n    )\n    FastLanguageModel.for_inference(model) # Enable native 2x faster inference\n\n    # alpaca_prompt = You MUST copy from above!\n\n    inputs = tokenizer(\n    [\n        alpaca_prompt.format(\n            \"What is a famous tall tower in Paris?\", # instruction\n            \"\", # input\n            \"\", # output - leave this blank for generation!\n        )\n    ], return_tensors = \"pt\").to(\"cuda\")\n\n    outputs = model.generate(**inputs, max_new_tokens = 64, use_cache = True)\n    tokenizer.batch_decode(outputs)","metadata":{"id":"MKX_XKs_BNZR","outputId":"05e5a193-dab0-41db-e07c-4b3afbdd7932","execution":{"iopub.status.busy":"2024-04-06T16:30:16.235412Z","iopub.execute_input":"2024-04-06T16:30:16.236127Z","iopub.status.idle":"2024-04-06T16:30:20.286318Z","shell.execute_reply.started":"2024-04-06T16:30:16.236092Z","shell.execute_reply":"2024-04-06T16:30:20.285241Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"You can also use Hugging Face's `AutoModelForPeftCausalLM`. Only use this if you do not have `unsloth` installed. It can be hopelessly slow, since `4bit` model downloading is not supported, and Unsloth's **inference is 2x faster**.","metadata":{"id":"QQMjaNrjsU5_"}},{"cell_type":"code","source":"if False:\n    # I highly do NOT suggest - use Unsloth if possible\n    from peft import AutoPeftModelForCausalLM\n    from transformers import AutoTokenizer\n    model = AutoPeftModelForCausalLM.from_pretrained(\n        \"lora_model\", # YOUR MODEL YOU USED FOR TRAINING\n        load_in_4bit = load_in_4bit,\n    )\n    tokenizer = AutoTokenizer.from_pretrained(\"lora_model\")","metadata":{"id":"yFfaXG0WsQuE","execution":{"iopub.status.busy":"2024-04-06T16:30:20.289045Z","iopub.execute_input":"2024-04-06T16:30:20.289914Z","iopub.status.idle":"2024-04-06T16:30:20.294953Z","shell.execute_reply.started":"2024-04-06T16:30:20.289877Z","shell.execute_reply":"2024-04-06T16:30:20.293978Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"### Saving to float16 for VLLM\n\nWe also support saving to `float16` directly. Select `merged_16bit` for float16 or `merged_4bit` for int4. We also allow `lora` adapters as a fallback. Use `push_to_hub_merged` to upload to your Hugging Face account! You can go to https://huggingface.co/settings/tokens for your personal tokens.","metadata":{"id":"f422JgM9sdVT"}},{"cell_type":"code","source":"# Merge to 16bit\nif False: model.save_pretrained_merged(\"model\", tokenizer, save_method = \"merged_16bit\",)\nif False: model.push_to_hub_merged(\"hf/model\", tokenizer, save_method = \"merged_16bit\", token = \"\")\n\n# Merge to 4bit\nif False: model.save_pretrained_merged(\"model\", tokenizer, save_method = \"merged_4bit\",)\nif False: model.push_to_hub_merged(\"hf/model\", tokenizer, save_method = \"merged_4bit\", token = \"\")\n\n# Just LoRA adapters\nif False: model.save_pretrained_merged(\"model\", tokenizer, save_method = \"lora\",)\nif False: model.push_to_hub_merged(\"hf/model\", tokenizer, save_method = \"lora\", token = \"\")","metadata":{"id":"iHjt_SMYsd3P","execution":{"iopub.status.busy":"2024-04-06T16:30:20.295979Z","iopub.execute_input":"2024-04-06T16:30:20.296285Z","iopub.status.idle":"2024-04-06T16:30:20.308979Z","shell.execute_reply.started":"2024-04-06T16:30:20.29626Z","shell.execute_reply":"2024-04-06T16:30:20.308167Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"### GGUF / llama.cpp Conversion\nTo save to `GGUF` / `llama.cpp`, we support it natively now! We clone `llama.cpp` and we default save it to `q8_0`. We allow all methods like `q4_k_m`. Use `save_pretrained_gguf` for local saving and `push_to_hub_gguf` for uploading to HF.\n\nSome supported quant methods (full list on our [Wiki page](https://github.com/unslothai/unsloth/wiki#gguf-quantization-options)):\n* `q8_0` - Fast conversion. High resource use, but generally acceptable.\n* `q4_k_m` - Recommended. Uses Q6_K for half of the attention.wv and feed_forward.w2 tensors, else Q4_K.\n* `q5_k_m` - Recommended. Uses Q6_K for half of the attention.wv and feed_forward.w2 tensors, else Q5_K.","metadata":{"id":"TCv4vXHd61i7"}},{"cell_type":"code","source":"# Save to 8bit Q8_0\nif False: model.save_pretrained_gguf(\"model\", tokenizer,)\nif False: model.push_to_hub_gguf(\"hf/model\", tokenizer, token = \"\")\n\n# Save to 16bit GGUF\nif False: model.save_pretrained_gguf(\"model\", tokenizer, quantization_method = \"f16\")\nif False: model.push_to_hub_gguf(\"hf/model\", tokenizer, quantization_method = \"f16\", token = \"\")\n\n# Save to q4_k_m GGUF\nif False: model.save_pretrained_gguf(\"model\", tokenizer, quantization_method = \"q4_k_m\")\nif False: model.push_to_hub_gguf(\"hf/model\", tokenizer, quantization_method = \"q4_k_m\", token = \"\")","metadata":{"id":"FqfebeAdT073","execution":{"iopub.status.busy":"2024-04-06T16:30:20.310103Z","iopub.execute_input":"2024-04-06T16:30:20.310443Z","iopub.status.idle":"2024-04-06T16:30:20.324421Z","shell.execute_reply.started":"2024-04-06T16:30:20.310419Z","shell.execute_reply":"2024-04-06T16:30:20.323668Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"Now, use the `model-unsloth.gguf` file or `model-unsloth-Q4_K_M.gguf` file in `llama.cpp` or a UI based system like `GPT4All`. You can install GPT4All by going [here](https://gpt4all.io/index.html).","metadata":{"id":"bDp0zNpwe6U_"}},{"cell_type":"markdown","source":"And we're done! If you have any questions on Unsloth, we have a [Discord](https://discord.gg/u54VK8m8tk) channel! If you find any bugs or want to keep updated with the latest LLM stuff, or need help, join projects etc, feel free to join our Discord!\n\nSome other links:\n1. Zephyr DPO 2x faster [free Colab](https://colab.research.google.com/drive/15vttTpzzVXv_tJwEk-hIcQ0S9FcEWvwP?usp=sharing)\n2. Llama 7b 2x faster [free Colab](https://colab.research.google.com/drive/1lBzz5KeZJKXjvivbYvmGarix9Ao6Wxe5?usp=sharing)\n3. TinyLlama 4x faster full Alpaca 52K in 1 hour [free Colab](https://colab.research.google.com/drive/1AZghoNBQaMDgWJpi4RbffGM1h6raLUj9?usp=sharing)\n4. CodeLlama 34b 2x faster [A100 on Colab](https://colab.research.google.com/drive/1y7A0AxE3y8gdj4AVkl2aZX47Xu3P1wJT?usp=sharing)\n5. Mistral 7b [free Kaggle version](https://www.kaggle.com/code/danielhanchen/kaggle-mistral-7b-unsloth-notebook)\n6. We also did a [blog](https://huggingface.co/blog/unsloth-trl) with 🤗 HuggingFace, and we're in the TRL [docs](https://huggingface.co/docs/trl/main/en/sft_trainer#accelerate-fine-tuning-2x-using-unsloth)!\n7. `ChatML` for ShareGPT datasets, [conversational notebook](https://colab.research.google.com/drive/1Aau3lgPzeZKQ-98h69CCu1UJcvIBLmy2?usp=sharing)\n8. Text completions like novel writing [notebook](https://colab.research.google.com/drive/1ef-tab5bhkvWmBOObepl1WgJvfvSzn5Q?usp=sharing)\n\n<div class=\"align-center\">\n  <a href=\"https://github.com/unslothai/unsloth\"><img src=\"https://github.com/unslothai/unsloth/raw/main/images/unsloth%20new%20logo.png\" width=\"115\"></a>\n  <a href=\"https://discord.gg/u54VK8m8tk\"><img src=\"https://github.com/unslothai/unsloth/raw/main/images/Discord.png\" width=\"145\"></a>\n  <a href=\"https://ko-fi.com/unsloth\"><img src=\"https://github.com/unslothai/unsloth/raw/main/images/Kofi button.png\" width=\"145\"></a></a> Support our work if you can! Thanks!\n</div>","metadata":{"id":"Zt9CHJqO6p30"}}]}
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