From 9c94ff095f94118ba6df400b84ce7e4ca924a2f0 Mon Sep 17 00:00:00 2001
From: James S <james@femboyfinancial.jp>
Date: Tue, 28 May 2024 18:32:03 +0000
Subject: [PATCH] New training notebook
---
train_unsloth.ipynb | 10070 +-----------------------------------------
1 file changed, 1 insertion(+), 10069 deletions(-)
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",
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- "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",
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- "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",
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- " Downloading https://download.pytorch.org/whl/cu121/nvidia_cuda_runtime_cu12-12.1.105-py3-none-manylinux1_x86_64.whl (823 kB)\r\n",
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- " 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",
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- " 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",
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- " 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",
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- " 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",
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- " 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",
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- "\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",
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- " 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",
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- " Downloading https://download.pytorch.org/whl/cu121/nvidia_nvtx_cu12-12.1.105-py3-none-manylinux1_x86_64.whl (99 kB)\r\n",
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- "\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",
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- "\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",
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- "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",
- "\u001b[?25h Installing backend dependencies ... \u001b[?25l-\b \b\\\b \b|\b \b/\b \bdone\r\n",
- "\u001b[?25h Preparing metadata (pyproject.toml) ... \u001b[?25l-\b \b\\\b \bdone\r\n",
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- "Collecting bitsandbytes (from unsloth@ git+https://github.com/unslothai/unsloth.git->unsloth[kaggle-new]@ git+https://github.com/unslothai/unsloth.git)\r\n",
- " Downloading bitsandbytes-0.43.0-py3-none-manylinux_2_24_x86_64.whl.metadata (1.8 kB)\r\n",
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- "Requirement already satisfied: async-timeout<5.0,>=4.0 in /opt/conda/lib/python3.10/site-packages (from aiohttp->datasets==2.17.1) (4.0.3)\r\n",
- "Requirement already satisfied: typing-extensions>=3.7.4.3 in /opt/conda/lib/python3.10/site-packages (from huggingface-hub>=0.19.4->datasets==2.17.1) (4.9.0)\r\n",
- "Requirement already satisfied: pyparsing!=3.0.5,>=2.0.2 in /opt/conda/lib/python3.10/site-packages (from packaging->datasets==2.17.1) (3.1.1)\r\n",
- "Requirement already satisfied: charset-normalizer<4,>=2 in /opt/conda/lib/python3.10/site-packages (from requests>=2.19.0->datasets==2.17.1) (3.3.2)\r\n",
- "Requirement already satisfied: idna<4,>=2.5 in /opt/conda/lib/python3.10/site-packages (from requests>=2.19.0->datasets==2.17.1) (3.6)\r\n",
- "Requirement already satisfied: urllib3<3,>=1.21.1 in /opt/conda/lib/python3.10/site-packages (from requests>=2.19.0->datasets==2.17.1) (1.26.18)\r\n",
- "Requirement already satisfied: certifi>=2017.4.17 in /opt/conda/lib/python3.10/site-packages (from requests>=2.19.0->datasets==2.17.1) (2024.2.2)\r\n",
- "Requirement already satisfied: torch in /opt/conda/lib/python3.10/site-packages (from bitsandbytes->unsloth@ git+https://github.com/unslothai/unsloth.git->unsloth[kaggle-new]@ git+https://github.com/unslothai/unsloth.git) (2.2.1+cu121)\r\n",
- "Requirement already satisfied: python-dateutil>=2.8.2 in /opt/conda/lib/python3.10/site-packages (from pandas->datasets==2.17.1) (2.9.0.post0)\r\n",
- "Requirement already satisfied: pytz>=2020.1 in /opt/conda/lib/python3.10/site-packages (from pandas->datasets==2.17.1) (2023.3.post1)\r\n",
- "Requirement already satisfied: tzdata>=2022.1 in /opt/conda/lib/python3.10/site-packages (from pandas->datasets==2.17.1) (2023.4)\r\n",
- "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",
- "Requirement already satisfied: transformers>=4.38.2 in /opt/conda/lib/python3.10/site-packages (from unsloth@ git+https://github.com/unslothai/unsloth.git->unsloth[kaggle-new]@ git+https://github.com/unslothai/unsloth.git) (4.38.2)\r\n",
- "Requirement already satisfied: sentencepiece in /opt/conda/lib/python3.10/site-packages (from unsloth@ git+https://github.com/unslothai/unsloth.git->unsloth[kaggle-new]@ git+https://github.com/unslothai/unsloth.git) (0.2.0)\r\n",
- "Requirement already satisfied: psutil in /opt/conda/lib/python3.10/site-packages (from unsloth@ git+https://github.com/unslothai/unsloth.git->unsloth[kaggle-new]@ git+https://github.com/unslothai/unsloth.git) (5.9.3)\r\n",
- "Requirement already satisfied: wheel>=0.42.0 in /opt/conda/lib/python3.10/site-packages (from unsloth@ git+https://github.com/unslothai/unsloth.git->unsloth[kaggle-new]@ git+https://github.com/unslothai/unsloth.git) (0.42.0)\r\n",
- "Requirement already satisfied: accelerate>=0.26.1 in /opt/conda/lib/python3.10/site-packages (from unsloth@ git+https://github.com/unslothai/unsloth.git->unsloth[kaggle-new]@ git+https://github.com/unslothai/unsloth.git) (0.28.0)\r\n",
- "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",
- "Requirement already satisfied: safetensors>=0.3.1 in /opt/conda/lib/python3.10/site-packages (from accelerate>=0.26.1->unsloth@ git+https://github.com/unslothai/unsloth.git->unsloth[kaggle-new]@ git+https://github.com/unslothai/unsloth.git) (0.4.2)\r\n",
- "Requirement already satisfied: six>=1.5 in /opt/conda/lib/python3.10/site-packages (from python-dateutil>=2.8.2->pandas->datasets==2.17.1) (1.16.0)\r\n",
- "Requirement already satisfied: sympy 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) (1.12)\r\n",
- "Requirement already satisfied: networkx 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) (3.2.1)\r\n",
- "Requirement already satisfied: jinja2 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) (3.1.2)\r\n",
- "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",
- "Requirement already satisfied: nvidia-cuda-runtime-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",
- "Requirement already satisfied: nvidia-cuda-cupti-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",
- "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",
- "Requirement already satisfied: nvidia-cublas-cu12==12.1.3.1 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) (12.1.3.1)\r\n",
- "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",
- "Requirement already satisfied: nvidia-curand-cu12==10.3.2.106 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) (10.3.2.106)\r\n",
- "Requirement already satisfied: nvidia-cusolver-cu12==11.4.5.107 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) (11.4.5.107)\r\n",
- "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",
- "Requirement already satisfied: nvidia-nccl-cu12==2.19.3 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) (2.19.3)\r\n",
- "Requirement already satisfied: nvidia-nvtx-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",
- "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",
- "Requirement already satisfied: regex!=2019.12.17 in /opt/conda/lib/python3.10/site-packages (from transformers>=4.38.2->unsloth@ git+https://github.com/unslothai/unsloth.git->unsloth[kaggle-new]@ git+https://github.com/unslothai/unsloth.git) (2023.12.25)\r\n",
- "Requirement already satisfied: tokenizers<0.19,>=0.14 in /opt/conda/lib/python3.10/site-packages (from transformers>=4.38.2->unsloth@ git+https://github.com/unslothai/unsloth.git->unsloth[kaggle-new]@ git+https://github.com/unslothai/unsloth.git) (0.15.2)\r\n",
- "Requirement already satisfied: docstring-parser>=0.14.1 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) (0.15)\r\n",
- "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",
- "Requirement already satisfied: markdown-it-py>=2.2.0 in /opt/conda/lib/python3.10/site-packages (from rich>=11.1.0->tyro->unsloth@ git+https://github.com/unslothai/unsloth.git->unsloth[kaggle-new]@ git+https://github.com/unslothai/unsloth.git) (3.0.0)\r\n",
- "Requirement already satisfied: pygments<3.0.0,>=2.13.0 in /opt/conda/lib/python3.10/site-packages (from rich>=11.1.0->tyro->unsloth@ git+https://github.com/unslothai/unsloth.git->unsloth[kaggle-new]@ git+https://github.com/unslothai/unsloth.git) (2.17.2)\r\n",
- "Requirement already satisfied: MarkupSafe>=2.0 in /opt/conda/lib/python3.10/site-packages (from jinja2->torch->bitsandbytes->unsloth@ git+https://github.com/unslothai/unsloth.git->unsloth[kaggle-new]@ git+https://github.com/unslothai/unsloth.git) (2.1.3)\r\n",
- "Requirement already satisfied: mpmath>=0.19 in /opt/conda/lib/python3.10/site-packages (from sympy->torch->bitsandbytes->unsloth@ git+https://github.com/unslothai/unsloth.git->unsloth[kaggle-new]@ git+https://github.com/unslothai/unsloth.git) (1.3.0)\r\n",
- "Requirement already satisfied: mdurl~=0.1 in /opt/conda/lib/python3.10/site-packages (from markdown-it-py>=2.2.0->rich>=11.1.0->tyro->unsloth@ git+https://github.com/unslothai/unsloth.git->unsloth[kaggle-new]@ git+https://github.com/unslothai/unsloth.git) (0.1.2)\r\n",
- "Downloading datasets-2.17.1-py3-none-any.whl (536 kB)\r\n",
- "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m536.7/536.7 kB\u001b[0m \u001b[31m4.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\r\n",
- "\u001b[?25hDownloading fsspec-2023.10.0-py3-none-any.whl (166 kB)\r\n",
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- "\u001b[?25hDownloading pyarrow-15.0.2-cp310-cp310-manylinux_2_28_x86_64.whl (38.3 MB)\r\n",
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- "\u001b[?25hDownloading bitsandbytes-0.43.0-py3-none-manylinux_2_24_x86_64.whl (102.2 MB)\r\n",
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- "\u001b[?25hDownloading pyarrow_hotfix-0.6-py3-none-any.whl (7.9 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",
- " Found existing installation: pyarrow 11.0.0\r\n",
- " 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",
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- "iopub.status.idle": "2024-03-28T00:13:38.483179Z",
- "shell.execute_reply": "2024-03-28T00:13:38.482338Z"
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- "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,
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- "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": {
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- "model.safetensors: 0%| | 0.00/4.13G [00:00<?, ?B/s]"
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- "generation_config.json: 0%| | 0.00/116 [00:00<?, ?B/s]"
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- "tokenizer_config.json: 0%| | 0.00/971 [00:00<?, ?B/s]"
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- "special_tokens_map.json: 0%| | 0.00/438 [00:00<?, ?B/s]"
- ]
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- {
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- },
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- "tokenizer.json: 0%| | 0.00/1.80M [00:00<?, ?B/s]"
- ]
- },
- "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",
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- "shell.execute_reply": "2024-03-28T00:13:48.323307Z"
- },
- "id": "95_Nn-89DhsL",
- "outputId": "c13d3e90-5342-4535-9541-98f9120dfe2b",
- "papermill": {
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- "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": {
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- "version_major": 2,
- "version_minor": 0
- },
- "text/plain": [
- "Map (num_proc=2): 0%| | 0/10000 [00:00<?, ? examples/s]"
- ]
- },
- "metadata": {},
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- }
- ],
- "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",
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- "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",
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- "exception": false,
- "start_time": "2024-03-28T03:52:53.826500",
- "status": "completed"
- },
- "tags": []
- },
- "outputs": [
- {
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- "README.md: 0%| | 0.00/579 [00:00<?, ?B/s]"
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- "model_id": "cd246bcf2d034bb2af58ceb7524df6c1",
- "version_major": 2,
- "version_minor": 0
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- "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",
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- "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",
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- "id": "MKX_XKs_BNZR",
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- "papermill": {
- "duration": 0.738494,
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- "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",
- "metadata": {
- "execution": {
- "iopub.execute_input": "2024-03-28T03:53:04.940718Z",
- "iopub.status.busy": "2024-03-28T03:53:04.939798Z",
- "iopub.status.idle": "2024-03-28T03:53:04.948324Z",
- "shell.execute_reply": "2024-03-28T03:53:04.947106Z"
- },
- "id": "FqfebeAdT073",
- "papermill": {
- "duration": 0.147412,
- "end_time": "2024-03-28T03:53:04.951208",
- "exception": false,
- "start_time": "2024-03-28T03:53:04.803796",
- "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,
- "end_time": "2024-03-28T03:53:05.263051",
- "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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