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{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "Y7Wdw0O6o2xa"
},
"source": [
"# コンペ用Fine-Tuningテンプレート\n",
"\n",
"こちらは、コンペにてFineーTuningを行いたい方に向けたテンプレートとなるFine-tuningコードです。\n",
"こちらを実行いただくだけでコンペの基準に達することができると思います。上手く活用してコンペ上位を目指しましょう!!\n",
"\n",
"本コードはOmnicampusで提供される演習環境での実行を想定しています。 \n",
"それ以外の環境で実行される場合は適宜、修正して下さい。 "
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"id": "6-t3HhxN8joX",
"outputId": "a87462b9-9d12-4446-e95a-fa3d406f864f"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Looking in indexes: https://pypi.org/simple, https://pypi.ngc.nvidia.com\n",
"Requirement already satisfied: pip in /usr/local/lib/python3.10/dist-packages (24.3.1)\n",
"\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager, possibly rendering your system unusable.It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv. Use the --root-user-action option if you know what you are doing and want to suppress this warning.\u001b[0m\u001b[33m\n",
"\u001b[0mLooking in indexes: https://pypi.org/simple, https://pypi.ngc.nvidia.com\n",
"Requirement already satisfied: transformers in /usr/local/lib/python3.10/dist-packages (4.46.3)\n",
"Collecting transformers\n",
" Downloading transformers-4.47.0-py3-none-any.whl.metadata (43 kB)\n",
"Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from transformers) (3.12.2)\n",
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"Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.10/dist-packages (from transformers) (1.26.4)\n",
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"Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.10/dist-packages (from transformers) (6.0.2)\n",
"Requirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.10/dist-packages (from transformers) (2023.6.3)\n",
"Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from transformers) (2.32.3)\n",
"Collecting tokenizers<0.22,>=0.21 (from transformers)\n",
" Downloading tokenizers-0.21.0-cp39-abi3-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (6.7 kB)\n",
"Requirement already satisfied: safetensors>=0.4.1 in /usr/local/lib/python3.10/dist-packages (from transformers) (0.4.5)\n",
"Requirement already satisfied: tqdm>=4.27 in /usr/local/lib/python3.10/dist-packages (from transformers) (4.67.1)\n",
"Requirement already satisfied: fsspec>=2023.5.0 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub<1.0,>=0.24.0->transformers) (2023.6.0)\n",
"Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub<1.0,>=0.24.0->transformers) (4.12.2)\n",
"Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (3.1.0)\n",
"Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (3.4)\n",
"Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (1.26.16)\n",
"Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (2023.5.7)\n",
"Downloading transformers-4.47.0-py3-none-any.whl (10.1 MB)\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m10.1/10.1 MB\u001b[0m \u001b[31m355.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[?25hDownloading tokenizers-0.21.0-cp39-abi3-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (3.0 MB)\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m3.0/3.0 MB\u001b[0m \u001b[31m601.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[?25hInstalling collected packages: tokenizers, transformers\n",
" Attempting uninstall: tokenizers\n",
" Found existing installation: tokenizers 0.20.3\n",
" Uninstalling tokenizers-0.20.3:\n",
" Successfully uninstalled tokenizers-0.20.3\n",
" Attempting uninstall: transformers\n",
" Found existing installation: transformers 4.46.3\n",
" Uninstalling transformers-4.46.3:\n",
" Successfully uninstalled transformers-4.46.3\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.\n",
"trl 0.12.2 requires transformers<4.47.0, but you have transformers 4.47.0 which is incompatible.\u001b[0m\u001b[31m\n",
"\u001b[0mSuccessfully installed tokenizers-0.21.0 transformers-4.47.0\n",
"\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager, possibly rendering your system unusable.It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv. Use the --root-user-action option if you know what you are doing and want to suppress this warning.\u001b[0m\u001b[33m\n",
"\u001b[0mLooking in indexes: https://pypi.org/simple, https://pypi.ngc.nvidia.com\n",
"Requirement already satisfied: bitsandbytes in /usr/local/lib/python3.10/dist-packages (0.45.0)\n",
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"Requirement already satisfied: networkx in /usr/local/lib/python3.10/dist-packages (from torch->bitsandbytes) (3.4.2)\n",
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"Requirement already satisfied: fsspec in /usr/local/lib/python3.10/dist-packages (from torch->bitsandbytes) (2023.6.0)\n",
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"Requirement already satisfied: nvidia-cuda-runtime-cu12==12.4.127 in /usr/local/lib/python3.10/dist-packages (from torch->bitsandbytes) (12.4.127)\n",
"Requirement already satisfied: nvidia-cuda-cupti-cu12==12.4.127 in /usr/local/lib/python3.10/dist-packages (from torch->bitsandbytes) (12.4.127)\n",
"Requirement already satisfied: nvidia-cudnn-cu12==9.1.0.70 in /usr/local/lib/python3.10/dist-packages (from torch->bitsandbytes) (9.1.0.70)\n",
"Requirement already satisfied: nvidia-cublas-cu12==12.4.5.8 in /usr/local/lib/python3.10/dist-packages (from torch->bitsandbytes) (12.4.5.8)\n",
"Requirement already satisfied: nvidia-cufft-cu12==11.2.1.3 in /usr/local/lib/python3.10/dist-packages (from torch->bitsandbytes) (11.2.1.3)\n",
"Requirement already satisfied: nvidia-curand-cu12==10.3.5.147 in /usr/local/lib/python3.10/dist-packages (from torch->bitsandbytes) (10.3.5.147)\n",
"Requirement already satisfied: nvidia-cusolver-cu12==11.6.1.9 in /usr/local/lib/python3.10/dist-packages (from torch->bitsandbytes) (11.6.1.9)\n",
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"Requirement already satisfied: nvidia-nccl-cu12==2.21.5 in /usr/local/lib/python3.10/dist-packages (from torch->bitsandbytes) (2.21.5)\n",
"Requirement already satisfied: nvidia-nvtx-cu12==12.4.127 in /usr/local/lib/python3.10/dist-packages (from torch->bitsandbytes) (12.4.127)\n",
"Requirement already satisfied: nvidia-nvjitlink-cu12==12.4.127 in /usr/local/lib/python3.10/dist-packages (from torch->bitsandbytes) (12.4.127)\n",
"Requirement already satisfied: triton==3.1.0 in /usr/local/lib/python3.10/dist-packages (from torch->bitsandbytes) (3.1.0)\n",
"Requirement already satisfied: sympy==1.13.1 in /usr/local/lib/python3.10/dist-packages (from torch->bitsandbytes) (1.13.1)\n",
"Requirement already satisfied: mpmath<1.4,>=1.1.0 in /usr/local/lib/python3.10/dist-packages (from sympy==1.13.1->torch->bitsandbytes) (1.3.0)\n",
"Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from jinja2->torch->bitsandbytes) (2.1.3)\n",
"\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager, possibly rendering your system unusable.It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv. Use the --root-user-action option if you know what you are doing and want to suppress this warning.\u001b[0m\u001b[33m\n",
"\u001b[0mLooking in indexes: https://pypi.org/simple, https://pypi.ngc.nvidia.com\n",
"Requirement already satisfied: accelerate in /usr/local/lib/python3.10/dist-packages (1.2.1)\n",
"Requirement already satisfied: numpy<3.0.0,>=1.17 in /usr/local/lib/python3.10/dist-packages (from accelerate) (1.26.4)\n",
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"Requirement already satisfied: huggingface-hub>=0.21.0 in /usr/local/lib/python3.10/dist-packages (from accelerate) (0.26.2)\n",
"Requirement already satisfied: safetensors>=0.4.3 in /usr/local/lib/python3.10/dist-packages (from accelerate) (0.4.5)\n",
"Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from huggingface-hub>=0.21.0->accelerate) (3.12.2)\n",
"Requirement already satisfied: fsspec>=2023.5.0 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub>=0.21.0->accelerate) (2023.6.0)\n",
"Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from huggingface-hub>=0.21.0->accelerate) (2.32.3)\n",
"Requirement already satisfied: tqdm>=4.42.1 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub>=0.21.0->accelerate) (4.67.1)\n",
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"Requirement already satisfied: nvidia-cuda-runtime-cu12==12.4.127 in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate) (12.4.127)\n",
"Requirement already satisfied: nvidia-cuda-cupti-cu12==12.4.127 in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate) (12.4.127)\n",
"Requirement already satisfied: nvidia-cudnn-cu12==9.1.0.70 in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate) (9.1.0.70)\n",
"Requirement already satisfied: nvidia-cublas-cu12==12.4.5.8 in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate) (12.4.5.8)\n",
"Requirement already satisfied: nvidia-cufft-cu12==11.2.1.3 in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate) (11.2.1.3)\n",
"Requirement already satisfied: nvidia-curand-cu12==10.3.5.147 in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate) (10.3.5.147)\n",
"Requirement already satisfied: nvidia-cusolver-cu12==11.6.1.9 in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate) (11.6.1.9)\n",
"Requirement already satisfied: nvidia-cusparse-cu12==12.3.1.170 in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate) (12.3.1.170)\n",
"Requirement already satisfied: nvidia-nccl-cu12==2.21.5 in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate) (2.21.5)\n",
"Requirement already satisfied: nvidia-nvtx-cu12==12.4.127 in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate) (12.4.127)\n",
"Requirement already satisfied: nvidia-nvjitlink-cu12==12.4.127 in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate) (12.4.127)\n",
"Requirement already satisfied: triton==3.1.0 in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate) (3.1.0)\n",
"Requirement already satisfied: sympy==1.13.1 in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate) (1.13.1)\n",
"Requirement already satisfied: mpmath<1.4,>=1.1.0 in /usr/local/lib/python3.10/dist-packages (from sympy==1.13.1->torch>=1.10.0->accelerate) (1.3.0)\n",
"Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from jinja2->torch>=1.10.0->accelerate) (2.1.3)\n",
"Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface-hub>=0.21.0->accelerate) (3.1.0)\n",
"Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface-hub>=0.21.0->accelerate) (3.4)\n",
"Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface-hub>=0.21.0->accelerate) (1.26.16)\n",
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"\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager, possibly rendering your system unusable.It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv. Use the --root-user-action option if you know what you are doing and want to suppress this warning.\u001b[0m\u001b[33m\n",
"\u001b[0mLooking in indexes: https://pypi.org/simple, https://pypi.ngc.nvidia.com\n",
"Requirement already satisfied: datasets in /usr/local/lib/python3.10/dist-packages (3.2.0)\n",
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"Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.10/dist-packages (from datasets) (1.26.4)\n",
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"Requirement already satisfied: dill<0.3.9,>=0.3.0 in /usr/local/lib/python3.10/dist-packages (from datasets) (0.3.8)\n",
"Requirement already satisfied: pandas in /usr/local/lib/python3.10/dist-packages (from datasets) (1.5.2)\n",
"Requirement already satisfied: requests>=2.32.2 in /usr/local/lib/python3.10/dist-packages (from datasets) (2.32.3)\n",
"Requirement already satisfied: tqdm>=4.66.3 in /usr/local/lib/python3.10/dist-packages (from datasets) (4.67.1)\n",
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"Requirement already satisfied: async-timeout<6.0,>=4.0 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets) (4.0.2)\n",
"Requirement already satisfied: attrs>=17.3.0 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets) (24.2.0)\n",
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"Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests>=2.32.2->datasets) (2023.5.7)\n",
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"\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager, possibly rendering your system unusable.It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv. Use the --root-user-action option if you know what you are doing and want to suppress this warning.\u001b[0m\u001b[33m\n",
"\u001b[0mLooking in indexes: https://pypi.org/simple, https://pypi.ngc.nvidia.com\n",
"Requirement already satisfied: peft in /usr/local/lib/python3.10/dist-packages (0.14.0)\n",
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"Requirement already satisfied: jinja2 in /usr/local/lib/python3.10/dist-packages (from torch>=1.13.0->peft) (3.1.2)\n",
"Requirement already satisfied: nvidia-cuda-nvrtc-cu12==12.4.127 in /usr/local/lib/python3.10/dist-packages (from torch>=1.13.0->peft) (12.4.127)\n",
"Requirement already satisfied: nvidia-cuda-runtime-cu12==12.4.127 in /usr/local/lib/python3.10/dist-packages (from torch>=1.13.0->peft) (12.4.127)\n",
"Requirement already satisfied: nvidia-cuda-cupti-cu12==12.4.127 in /usr/local/lib/python3.10/dist-packages (from torch>=1.13.0->peft) (12.4.127)\n",
"Requirement already satisfied: nvidia-cudnn-cu12==9.1.0.70 in /usr/local/lib/python3.10/dist-packages (from torch>=1.13.0->peft) (9.1.0.70)\n",
"Requirement already satisfied: nvidia-cublas-cu12==12.4.5.8 in /usr/local/lib/python3.10/dist-packages (from torch>=1.13.0->peft) (12.4.5.8)\n",
"Requirement already satisfied: nvidia-cufft-cu12==11.2.1.3 in /usr/local/lib/python3.10/dist-packages (from torch>=1.13.0->peft) (11.2.1.3)\n",
"Requirement already satisfied: nvidia-curand-cu12==10.3.5.147 in /usr/local/lib/python3.10/dist-packages (from torch>=1.13.0->peft) (10.3.5.147)\n",
"Requirement already satisfied: nvidia-cusolver-cu12==11.6.1.9 in /usr/local/lib/python3.10/dist-packages (from torch>=1.13.0->peft) (11.6.1.9)\n",
"Requirement already satisfied: nvidia-cusparse-cu12==12.3.1.170 in /usr/local/lib/python3.10/dist-packages (from torch>=1.13.0->peft) (12.3.1.170)\n",
"Requirement already satisfied: nvidia-nccl-cu12==2.21.5 in /usr/local/lib/python3.10/dist-packages (from torch>=1.13.0->peft) (2.21.5)\n",
"Requirement already satisfied: nvidia-nvtx-cu12==12.4.127 in /usr/local/lib/python3.10/dist-packages (from torch>=1.13.0->peft) (12.4.127)\n",
"Requirement already satisfied: nvidia-nvjitlink-cu12==12.4.127 in /usr/local/lib/python3.10/dist-packages (from torch>=1.13.0->peft) (12.4.127)\n",
"Requirement already satisfied: triton==3.1.0 in /usr/local/lib/python3.10/dist-packages (from torch>=1.13.0->peft) (3.1.0)\n",
"Requirement already satisfied: sympy==1.13.1 in /usr/local/lib/python3.10/dist-packages (from torch>=1.13.0->peft) (1.13.1)\n",
"Requirement already satisfied: mpmath<1.4,>=1.1.0 in /usr/local/lib/python3.10/dist-packages (from sympy==1.13.1->torch>=1.13.0->peft) (1.3.0)\n",
"Requirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.10/dist-packages (from transformers->peft) (2023.6.3)\n",
"Requirement already satisfied: tokenizers<0.22,>=0.21 in /usr/local/lib/python3.10/dist-packages (from transformers->peft) (0.21.0)\n",
"Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from jinja2->torch>=1.13.0->peft) (2.1.3)\n",
"Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface-hub>=0.25.0->peft) (3.1.0)\n",
"Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface-hub>=0.25.0->peft) (3.4)\n",
"Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface-hub>=0.25.0->peft) (1.26.16)\n",
"Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface-hub>=0.25.0->peft) (2023.5.7)\n",
"\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager, possibly rendering your system unusable.It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv. Use the --root-user-action option if you know what you are doing and want to suppress this warning.\u001b[0m\u001b[33m\n",
"\u001b[0mLooking in indexes: https://pypi.org/simple, https://pypi.ngc.nvidia.com\n",
"Requirement already satisfied: trl in /usr/local/lib/python3.10/dist-packages (0.12.2)\n",
"Requirement already satisfied: accelerate>=0.34.0 in /usr/local/lib/python3.10/dist-packages (from trl) (1.2.1)\n",
"Requirement already satisfied: datasets>=2.21.0 in /usr/local/lib/python3.10/dist-packages (from trl) (3.2.0)\n",
"Requirement already satisfied: rich in /usr/local/lib/python3.10/dist-packages (from trl) (13.9.4)\n",
"Collecting transformers<4.47.0 (from trl)\n",
" Downloading transformers-4.46.3-py3-none-any.whl.metadata (44 kB)\n",
"Requirement already satisfied: numpy<3.0.0,>=1.17 in /usr/local/lib/python3.10/dist-packages (from accelerate>=0.34.0->trl) (1.26.4)\n",
"Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from accelerate>=0.34.0->trl) (24.2)\n",
"Requirement already satisfied: psutil in /usr/local/lib/python3.10/dist-packages (from accelerate>=0.34.0->trl) (5.9.4)\n",
"Requirement already satisfied: pyyaml in /usr/local/lib/python3.10/dist-packages (from accelerate>=0.34.0->trl) (6.0.2)\n",
"Requirement already satisfied: torch>=1.10.0 in /usr/local/lib/python3.10/dist-packages (from accelerate>=0.34.0->trl) (2.5.1)\n",
"Requirement already satisfied: huggingface-hub>=0.21.0 in /usr/local/lib/python3.10/dist-packages (from accelerate>=0.34.0->trl) (0.26.2)\n",
"Requirement already satisfied: safetensors>=0.4.3 in /usr/local/lib/python3.10/dist-packages (from accelerate>=0.34.0->trl) (0.4.5)\n",
"Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from datasets>=2.21.0->trl) (3.12.2)\n",
"Requirement already satisfied: pyarrow>=15.0.0 in /usr/local/lib/python3.10/dist-packages (from datasets>=2.21.0->trl) (18.0.0)\n",
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"Requirement already satisfied: pandas in /usr/local/lib/python3.10/dist-packages (from datasets>=2.21.0->trl) (1.5.2)\n",
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"Requirement already satisfied: tqdm>=4.66.3 in /usr/local/lib/python3.10/dist-packages (from datasets>=2.21.0->trl) (4.67.1)\n",
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"Collecting tokenizers<0.21,>=0.20 (from transformers<4.47.0->trl)\n",
" Downloading tokenizers-0.20.3-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (6.7 kB)\n",
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"Requirement already satisfied: nvidia-cudnn-cu12==9.1.0.70 in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate>=0.34.0->trl) (9.1.0.70)\n",
"Requirement already satisfied: nvidia-cublas-cu12==12.4.5.8 in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate>=0.34.0->trl) (12.4.5.8)\n",
"Requirement already satisfied: nvidia-cufft-cu12==11.2.1.3 in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate>=0.34.0->trl) (11.2.1.3)\n",
"Requirement already satisfied: nvidia-curand-cu12==10.3.5.147 in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate>=0.34.0->trl) (10.3.5.147)\n",
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"\u001b[?25hInstalling collected packages: tokenizers, transformers\n",
" Attempting uninstall: tokenizers\n",
" Found existing installation: tokenizers 0.21.0\n",
" Uninstalling tokenizers-0.21.0:\n",
" Successfully uninstalled tokenizers-0.21.0\n",
" Attempting uninstall: transformers\n",
" Found existing installation: transformers 4.47.0\n",
" Uninstalling transformers-4.47.0:\n",
" Successfully uninstalled transformers-4.47.0\n",
"Successfully installed tokenizers-0.20.3 transformers-4.46.3\n",
"\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager, possibly rendering your system unusable.It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv. Use the --root-user-action option if you know what you are doing and want to suppress this warning.\u001b[0m\u001b[33m\n",
"\u001b[0mLooking in indexes: https://pypi.org/simple, https://pypi.ngc.nvidia.com\n",
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"\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager, possibly rendering your system unusable.It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv. Use the --root-user-action option if you know what you are doing and want to suppress this warning.\u001b[0m\u001b[33m\n",
"\u001b[0mLooking in indexes: https://pypi.org/simple, https://pypi.ngc.nvidia.com\n",
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"\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager, possibly rendering your system unusable.It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv. Use the --root-user-action option if you know what you are doing and want to suppress this warning.\u001b[0m\u001b[33m\n",
"\u001b[0m"
]
}
],
"source": [
"# python 3.10.12\n",
"!pip install -U pip\n",
"!pip install -U transformers\n",
"!pip install -U bitsandbytes\n",
"!pip install -U accelerate\n",
"!pip install -U datasets\n",
"!pip install -U peft\n",
"!pip install -U trl\n",
"!pip install -U wandb\n",
"!pip install ipywidgets --upgrade"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"id": "LbmtYWUH8p_J"
},
"outputs": [],
"source": [
"from transformers import (\n",
" AutoModelForCausalLM,\n",
" AutoTokenizer,\n",
" BitsAndBytesConfig,\n",
" TrainingArguments,\n",
" logging,\n",
")\n",
"from peft import (\n",
" LoraConfig,\n",
" PeftModel,\n",
" get_peft_model,\n",
")\n",
"import os, torch, gc\n",
"from datasets import load_dataset\n",
"import bitsandbytes as bnb\n",
"from trl import SFTTrainer"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"id": "WAaS0RKXgG72"
},
"outputs": [],
"source": [
"# Hugging Face Token\n",
"HF_TOKEN = \"write権限のあるトークン\""
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"id": "Oh-kvG8LQ2EZ"
},
"outputs": [],
"source": [
"# モデルを読み込み。\n",
"# llm-jp-3 1.8B, 3.7B, 13Bのsnapshotをダウンロード済みでmodelsディレクトリに格納してあります。\n",
"# base_model_idの値はomnicampusの環境におけるモデルのパスを表しており、それ以外の環境で実行する場合は変更の必要があります。\n",
"# その他のモデルは取得に承諾が必要なため、各自でダウンロードお願いします。\n",
"base_model_id = \"models/models--llm-jp--llm-jp-3-13b/snapshots/cd3823f4c1fcbb0ad2e2af46036ab1b0ca13192a\" #Fine-Tuningするベースモデル\n",
"# omnicampus以外の環境をご利用の方は以下をご利用ください。\n",
"# base_model_id = \"llm-jp/llm-jp-3-13b\"\n",
"new_model_id = \"llm-jp-3-13b-finetune\" #Fine-Tuningしたモデルにつけたい名前"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"id": "HXXd9RiiQqZP"
},
"outputs": [],
"source": [
"\"\"\"\n",
"bnb_config: 量子化の設定\n",
"\n",
" - load_in_4bit:\n",
" - 4bit量子化形式でモデルをロード\n",
"\n",
" - bnb_4bit_quant_type:\n",
" - 量子化の形式を指定\n",
"\n",
" - bnb_4bit_compute_dtype:\n",
" - 量子化された重みを用いて計算する際のデータ型\n",
"\n",
"\"\"\"\n",
"\n",
"bnb_config = BitsAndBytesConfig(\n",
" load_in_4bit=True,\n",
" bnb_4bit_quant_type=\"nf4\", # nf4は通常のINT4より精度が高く、ニューラルネットワークの分布に最適です\n",
" bnb_4bit_compute_dtype=torch.bfloat16,\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"colab": {
"referenced_widgets": [
"f4974ca5329a4a1e8fe1e1225f200c10"
]
},
"id": "St-tJNuJQviq",
"outputId": "1a4237b3-d41d-4034-d0a1-6fa46e1cf2ec"
},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "c92dfc83d7024beebea3c486fc311027",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Loading checkpoint shards: 0%| | 0/6 [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"\"\"\"\n",
"model: モデル\n",
"\n",
" - base_model:\n",
" - 読み込むベースモデル (事前に定義したもの)\n",
"\n",
" - quantization_config:\n",
" - bnb_configで設定した量子化設定\n",
"\n",
" - device_map:\n",
" - モデルを割り当てるデバイス (CPU/GPU) \"auto\"で自動に割り当てられます。\n",
"\n",
"tokenizer: トークナイザー\n",
"\n",
" - base_model:\n",
" - 読み込むベースモデル (事前に定義したもの)\n",
"\n",
" - trust_remote_code:\n",
" - リモートコードの実行を許可 (カスタムモデルなど)\n",
"\"\"\"\n",
"model = AutoModelForCausalLM.from_pretrained(\n",
" base_model_id,\n",
" quantization_config=bnb_config,\n",
" device_map=\"auto\"\n",
")\n",
"\n",
"tokenizer = AutoTokenizer.from_pretrained(base_model_id, trust_remote_code=True)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"id": "kHpncyFpRBq0"
},
"outputs": [],
"source": [
"\"\"\"\n",
"find_all_linear_names: モデル内の4bit量子化線形層を探します。\n",
"\"\"\"\n",
"\n",
"def find_all_linear_names(model):\n",
" cls = bnb.nn.Linear4bit # 4bit量子化線形層クラスを指定\n",
" lora_module_names = set() # ここに取得した線形層を保持します。\n",
"\n",
" # モデル内の全てのモジュールを探索します\n",
" for name, module in model.named_modules():\n",
" if isinstance(module, cls): # モジュールが4bit量子化線形層の場合\n",
" names = name.split('.') # モジュールの名前を分割 (ネストされてる際などに対処)\n",
" lora_module_names.add(names[0] if len(names) == 1 else names[-1]) # 最下層の名前をlora_module_namesに追加\n",
"\n",
" # 'lm_head' は16ビット演算の際に除外する必要があるため、lora_module_namesから削除\n",
" if 'lm_head' in lora_module_names:\n",
" lora_module_names.remove('lm_head')\n",
"\n",
" return list(lora_module_names) # lora_module_namesをリストに変換して返します。\n",
"\n",
"modules = find_all_linear_names(model)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"id": "YNJvNjnERuW8"
},
"outputs": [],
"source": [
"\"\"\"\n",
"peft_config: PEFTの構成設定\n",
"\n",
" - r\n",
" - LoRA のランク (4, 8, 16 ,32...)\n",
" - 増やすほど学習が捗るが, 過学習のリスクも高まるので注意\n",
"\n",
" - lora_alpha\n",
" - LoRAのスケーリング係数\n",
"\n",
" - lora_dropout\n",
" - ドロップアウト率（過学習を防ぐための割合）\n",
"\n",
" - bias\n",
" - バイアス項の扱い (\"none\"の場合、LoRAはバイアスを学習しない)\n",
"\n",
" - task_type\n",
" - タスクタイプ\n",
"\n",
" - target_modules\n",
" - LoRAを適用するターゲットモジュール (前のコードで特定した層)\n",
"\"\"\"\n",
"\n",
"peft_config = LoraConfig(\n",
" r=16, # 増やすことを検討\n",
" lora_alpha=32, # 増やすことを後ほど検討\n",
" lora_dropout=0.05, #0.1に調整することを検討\n",
" bias=\"none\",\n",
" task_type=\"CAUSAL_LM\",\n",
" target_modules=modules,\n",
")\n",
"\n",
"model = get_peft_model(model, peft_config)"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"colab": {
"referenced_widgets": [
"98bc15c6c1fa4a24b8ee2ccefeceedd1"
]
},
"id": "RT0wnFkYjNpO",
"outputId": "a8489c1d-2b33-48b2-e465-f701925e7962"
},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "7bbe20125672460baafc522f82791a63",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Generating train split: 0 examples [00:00, ? examples/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"DatasetDict({\n",
" train: Dataset({\n",
" features: ['ID', 'text', 'output'],\n",
" num_rows: 1729\n",
" })\n",
"})"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"\"\"\"\n",
"学習に用いるデータセットの指定\n",
"今回はLLM-jp の公開している Ichikara Instruction を使います。データにアクセスするためには申請が必要ですので、使いたい方のみ申請をしてください。\n",
"Ichikara Instruciton を Hugging Face Hub にて公開することはお控えください。\n",
"また、CC-BY-NC-SAですのでモデルはライセンスを継承する前提でお使いください。\n",
"\n",
"下記のリンクから申請を終えた先に Google Drive があり、Distribution20241221_all というフォルダごとダウンロードしてください。\n",
"今回は「ichikara-instruction-003-001-1.json」を使います。必要であれば展開（!unzip など）し、データセットのパスを適切に指定してください。\n",
"omnicampusの開発環境では取得したデータを左側にドラッグアンドドロップしてお使いください。\n",
"\n",
"https://liat-aip.sakura.ne.jp/wp/llmのための日本語インストラクションデータ作成/llmのための日本語インストラクションデータ-公開/\n",
"関根聡, 安藤まや, 後藤美知子, 鈴木久美, 河原大輔, 井之上直也, 乾健太郎. ichikara-instruction: LLMのための日本語インストラクションデータの構築. 言語処理学会第30回年次大会(2024)\n",
"\n",
"\"\"\"\n",
"\n",
"dataset = load_dataset(\"json\", data_files=\"./Distribution20241221_all/ichikara-instruction-003-001-1.json\")\n",
"dataset"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"colab": {
"referenced_widgets": [
"4d8f0cd8795c4ecc962d3b241c49ff2c"
]
},
"id": "BANlYJFSKf-K",
"outputId": "3d0aba22-438e-46b0-e89d-cd85ba6e8724"
},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "52a66e292f5d4128a494b5b252cb9e23",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Map (num_proc=4): 0%| | 0/1729 [00:00<?, ? examples/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"DatasetDict({\n",
" train: Dataset({\n",
" features: ['ID', 'text', 'output', 'formatted_text'],\n",
" num_rows: 1729\n",
" })\n",
"})"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# 学習時のプロンプトフォーマットの定義\n",
"prompt = \"\"\"### 指示\n",
"{}\n",
"### 回答\n",
"{}\"\"\"\n",
"\n",
"\n",
"\"\"\"\n",
"formatting_prompts_func: 各データをプロンプトに合わせた形式に合わせる\n",
"\"\"\"\n",
"EOS_TOKEN = tokenizer.eos_token # トークナイザーのEOSトークン（文末トークン）\n",
"def formatting_prompts_func(examples):\n",
" input = examples[\"text\"] # 入力データ\n",
" output = examples[\"output\"] # 出力データ\n",
" text = prompt.format(input, output) + EOS_TOKEN # プロンプトの作成\n",
" return { \"formatted_text\" : text, } # 新しいフィールド \"formatted_text\" を返す\n",
"pass\n",
"\n",
"# # 各データにフォーマットを適用\n",
"dataset = dataset.map(\n",
" formatting_prompts_func,\n",
" num_proc= 4, # 並列処理数を指定\n",
")\n",
"\n",
"dataset"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"id": "8NhF0GLaTkUZ",
"outputId": "3aec5f30-05ed-492e-c8b0-d6c2495d900e"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"### 指示\n",
"言葉の単位には、小さいものから順に「単語」→「文節」→「文」→「段落」→「文章」があります。\n",
"この5つの単位の違いを説明しなさい。\n",
"### 回答\n",
"日本語において、「言葉の単位」は5つあります。\n",
"1つずつ見ていくと、\n",
"・文章・・・1冊の本、1編の論文、1件のレポートなどはそれ全体で1つの文章ということになります。「言葉の単位」で最大の単位です。\n",
"\n",
"・段落・・・「文章」を意味内容で区切ったひとまとまりのことを指します。日本語では、1字下げ（段落の先頭を1文字空ける）のルールがあります。\n",
"\n",
"・文・・・句点「。」によって区切られるひと続きの言葉のことを言い、必ず文の終わりには「。」を付けます。\n",
"\n",
"・文節・・・文を「意味の通じる最小の単位」まで区切ったものをいいます。\n",
"文：自分で薪を割れ、二重に温まる。\n",
"文節分け：自分で / 薪を / 割れ、 / 二重に / 温まる。/\n",
"\n",
"・単語・・・文節をさらに区切って、もうこれ以上分けることができない「言葉の単位として最小の単位」です。\n",
"単語分け：自分 / で / 薪 / を / 割れ、/ 二重 / に / 温まる。/\n",
"\n",
"となります。</s>\n"
]
}
],
"source": [
"# データを確認\n",
"print(dataset[\"train\"][\"formatted_text\"][3])"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"id": "tp9vHUYtTvly"
},
"outputs": [
{
"data": {
"text/plain": [
"DatasetDict({\n",
" train: Dataset({\n",
" features: ['ID', 'text', 'output', 'formatted_text'],\n",
" num_rows: 1556\n",
" })\n",
" test: Dataset({\n",
" features: ['ID', 'text', 'output', 'formatted_text'],\n",
" num_rows: 173\n",
" })\n",
"})"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# データをtrainデータとtestデータに分割 (test_sizeの比率に)\n",
"dataset = dataset[\"train\"].train_test_split(test_size=0.1) #元コメントアウト\n",
"dataset"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"id": "6gJAYhfCacf7"
},
"outputs": [],
"source": [
"\"\"\"\n",
"training_arguments: 学習の設定\n",
"\n",
" - output_dir:\n",
" -トレーニング後のモデルを保存するディレクトリ\n",
"\n",
" - per_device_train_batch_size:\n",
" - デバイスごとのトレーニングバッチサイズ\n",
"\n",
" - per_device_\n",
" _batch_size:\n",
" - デバイスごとの評価バッチサイズ\n",
"\n",
" - gradient_accumulation_steps:\n",
" - 勾配を更新する前にステップを積み重ねる回数\n",
"\n",
" - optim:\n",
" - オプティマイザの設定\n",
"\n",
" - num_train_epochs:\n",
" - エポック数\n",
"\n",
" - eval_strategy:\n",
" - 評価の戦略 (\"no\"/\"steps\"/\"epoch\")\n",
"\n",
" - eval_steps:\n",
" - eval_strategyが\"steps\"のとき、評価を行うstep間隔\n",
"\n",
" - logging_strategy:\n",
" - ログ記録の戦略\n",
"\n",
" - logging_steps:\n",
" - ログを出力するステップ間隔\n",
"\n",
" - warmup_steps:\n",
" - 学習率のウォームアップステップ数\n",
"\n",
" - save_steps:\n",
" - モデルを保存するステップ間隔\n",
"\n",
" - save_total_limit:\n",
" - 保存しておくcheckpointの数\n",
"\n",
" - max_steps:\n",
" - トレーニングの最大ステップ数\n",
"\n",
" - learning_rate:\n",
" - 学習率\n",
"\n",
" - fp16:\n",
" - 16bit浮動小数点の使用設定（第8回演習を参考にすると良いです）\n",
"\n",
" - bf16:\n",
" - BFloat16の使用設定\n",
"\n",
" - group_by_length:\n",
" - 入力シーケンスの長さによりバッチをグループ化 (トレーニングの効率化)\n",
"\n",
" - report_to:\n",
" - ログの送信先 (\"wandb\"/\"tensorboard\"など)\n",
"\"\"\"\n",
"\n",
"training_arguments = TrainingArguments(\n",
" output_dir=new_model_id,\n",
" per_device_train_batch_size=1,\n",
" gradient_accumulation_steps=2,\n",
" optim=\"paged_adamw_32bit\",\n",
" num_train_epochs=3, # 1から3に増やす\n",
" logging_strategy=\"steps\",\n",
" logging_steps=10,\n",
" warmup_steps=10,\n",
" save_steps=100,\n",
" save_total_limit = 2,\n",
" max_steps = -1,\n",
" learning_rate=1e-5, #5e-5から変更\n",
" fp16=False,\n",
" bf16=False,\n",
" seed = 3407,\n",
" group_by_length=True,\n",
" report_to=\"none\"\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"colab": {
"referenced_widgets": [
"f59354b9d9aa4e17b376a508d7e168e6"
]
},
"id": "f3U8FUkwTx_K",
"outputId": "03f6db07-5637-4308-a471-d7341706ad39"
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_deprecation.py:100: FutureWarning: Deprecated argument(s) used in '__init__': max_seq_length, dataset_text_field. Will not be supported from version '0.13.0'.\n",
"\n",
"Deprecated positional argument(s) used in SFTTrainer, please use the SFTConfig to set these arguments instead.\n",
" warnings.warn(message, FutureWarning)\n",
"/usr/local/lib/python3.10/dist-packages/trl/trainer/sft_trainer.py:300: UserWarning: You passed a `max_seq_length` argument to the SFTTrainer, the value you passed will override the one in the `SFTConfig`.\n",
" warnings.warn(\n",
"/usr/local/lib/python3.10/dist-packages/trl/trainer/sft_trainer.py:328: UserWarning: You passed a `dataset_text_field` argument to the SFTTrainer, the value you passed will override the one in the `SFTConfig`.\n",
" warnings.warn(\n"
]
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "415cff5064a6458d9a6d842551eac707",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Map: 0%| | 0/1556 [00:00<?, ? examples/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"[2024-12-16 09:04:35,043] [INFO] [real_accelerator.py:219:get_accelerator] Setting ds_accelerator to cuda (auto detect)\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"df: /root/.triton/autotune: No such file or directory\n"
]
},
{
"data": {
"text/html": [
"\n",
" <div>\n",
" \n",
" <progress value='2334' max='2334' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
" [2334/2334 1:37:48, Epoch 3/3]\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>10</td>\n",
" <td>2.096900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>20</td>\n",
" <td>2.231800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>30</td>\n",
" <td>2.154300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>40</td>\n",
" <td>2.341300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>50</td>\n",
" <td>2.253400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>60</td>\n",
" <td>2.252700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>70</td>\n",
" <td>1.944400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>80</td>\n",
" <td>2.016700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>90</td>\n",
" <td>2.016200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>100</td>\n",
" <td>1.915200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>110</td>\n",
" <td>2.082700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>120</td>\n",
" <td>1.838300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>130</td>\n",
" <td>1.999300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>140</td>\n",
" <td>1.745800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>150</td>\n",
" <td>1.881100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>160</td>\n",
" <td>1.980600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>170</td>\n",
" <td>1.866600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>180</td>\n",
" <td>1.816600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>190</td>\n",
" <td>1.709700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>200</td>\n",
" <td>1.776300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>210</td>\n",
" <td>1.829500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>220</td>\n",
" <td>2.097600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>230</td>\n",
" <td>1.890900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>240</td>\n",
" <td>1.952500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>250</td>\n",
" <td>1.879400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>260</td>\n",
" <td>2.055700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>270</td>\n",
" <td>1.966700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>280</td>\n",
" <td>1.998000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>290</td>\n",
" <td>1.870500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>300</td>\n",
" <td>1.738800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>310</td>\n",
" <td>1.896700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>320</td>\n",
" <td>1.918800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>330</td>\n",
" <td>1.879300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>340</td>\n",
" <td>1.916400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>350</td>\n",
" <td>1.788100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>360</td>\n",
" <td>1.982500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>370</td>\n",
" <td>1.828600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>380</td>\n",
" <td>1.813200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>390</td>\n",
" <td>1.983600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>400</td>\n",
" <td>1.693000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>410</td>\n",
" <td>1.869900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>420</td>\n",
" <td>2.013100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>430</td>\n",
" <td>1.800600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>440</td>\n",
" <td>1.691500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>450</td>\n",
" <td>1.819500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>460</td>\n",
" <td>2.004800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>470</td>\n",
" <td>1.835400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>480</td>\n",
" <td>1.947900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>490</td>\n",
" <td>1.684900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>500</td>\n",
" <td>1.800400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>510</td>\n",
" <td>2.124100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>520</td>\n",
" <td>2.039200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>530</td>\n",
" <td>1.751800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>540</td>\n",
" <td>1.896800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>550</td>\n",
" <td>1.767800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>560</td>\n",
" <td>2.010100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>570</td>\n",
" <td>1.867400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>580</td>\n",
" <td>1.842500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>590</td>\n",
" <td>1.834400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>600</td>\n",
" <td>1.630700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>610</td>\n",
" <td>1.977500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>620</td>\n",
" <td>1.760200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>630</td>\n",
" <td>1.787500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>640</td>\n",
" <td>1.849500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>650</td>\n",
" <td>1.699100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>660</td>\n",
" <td>2.017200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>670</td>\n",
" <td>1.906200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>680</td>\n",
" <td>1.773000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>690</td>\n",
" <td>1.745600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>700</td>\n",
" <td>1.616300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>710</td>\n",
" <td>2.075700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>720</td>\n",
" <td>1.891000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>730</td>\n",
" <td>1.950200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>740</td>\n",
" <td>1.782900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>750</td>\n",
" <td>1.648000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>760</td>\n",
" <td>1.910500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>770</td>\n",
" <td>1.801000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>780</td>\n",
" <td>1.946800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>790</td>\n",
" <td>1.906900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>800</td>\n",
" <td>1.811200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>810</td>\n",
" <td>1.692600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>820</td>\n",
" <td>1.712400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>830</td>\n",
" <td>1.715800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>840</td>\n",
" <td>1.949900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>850</td>\n",
" <td>1.807600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>860</td>\n",
" <td>1.625700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>870</td>\n",
" <td>1.733700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>880</td>\n",
" <td>1.605800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>890</td>\n",
" <td>1.969900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>900</td>\n",
" <td>1.812800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>910</td>\n",
" <td>1.794900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>920</td>\n",
" <td>1.733200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>930</td>\n",
" <td>1.638400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>940</td>\n",
" <td>2.083000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>950</td>\n",
" <td>1.831100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>960</td>\n",
" <td>1.774300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>970</td>\n",
" <td>1.712800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>980</td>\n",
" <td>1.780100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>990</td>\n",
" <td>1.871500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1000</td>\n",
" <td>1.787100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1010</td>\n",
" <td>1.905300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1020</td>\n",
" <td>1.766200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1030</td>\n",
" <td>1.825000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1040</td>\n",
" <td>2.016800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1050</td>\n",
" <td>1.919000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1060</td>\n",
" <td>1.956000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1070</td>\n",
" <td>1.691500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1080</td>\n",
" <td>1.588900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1090</td>\n",
" <td>1.860000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1100</td>\n",
" <td>1.787900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1110</td>\n",
" <td>1.755900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1120</td>\n",
" <td>1.863400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1130</td>\n",
" <td>1.576300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1140</td>\n",
" <td>1.821800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1150</td>\n",
" <td>1.794300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1160</td>\n",
" <td>1.677100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1170</td>\n",
" <td>1.714300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1180</td>\n",
" <td>1.708300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1190</td>\n",
" <td>1.930100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1200</td>\n",
" <td>1.791700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1210</td>\n",
" <td>1.740000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1220</td>\n",
" <td>1.679000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1230</td>\n",
" <td>1.743500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1240</td>\n",
" <td>1.955000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1250</td>\n",
" <td>1.797900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1260</td>\n",
" <td>1.841000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1270</td>\n",
" <td>1.759900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1280</td>\n",
" <td>1.876400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1290</td>\n",
" <td>2.042600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1300</td>\n",
" <td>1.900500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1310</td>\n",
" <td>1.915300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1320</td>\n",
" <td>1.852100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1330</td>\n",
" <td>1.795900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1340</td>\n",
" <td>1.800200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1350</td>\n",
" <td>1.822300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1360</td>\n",
" <td>1.765400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1370</td>\n",
" <td>1.713800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1380</td>\n",
" <td>1.774400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1390</td>\n",
" <td>2.039700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1400</td>\n",
" <td>1.756600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1410</td>\n",
" <td>1.940300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1420</td>\n",
" <td>1.670100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1430</td>\n",
" <td>1.795300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1440</td>\n",
" <td>1.811100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1450</td>\n",
" <td>1.871900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1460</td>\n",
" <td>1.766400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1470</td>\n",
" <td>1.841000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1480</td>\n",
" <td>1.733600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1490</td>\n",
" <td>1.930300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1500</td>\n",
" <td>1.957400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1510</td>\n",
" <td>1.771100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1520</td>\n",
" <td>1.797000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1530</td>\n",
" <td>1.678000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1540</td>\n",
" <td>1.862700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1550</td>\n",
" <td>1.727100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1560</td>\n",
" <td>1.771500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1570</td>\n",
" <td>1.785200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1580</td>\n",
" <td>1.839400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1590</td>\n",
" <td>1.848800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1600</td>\n",
" <td>1.638000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1610</td>\n",
" <td>1.813800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1620</td>\n",
" <td>1.985700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1630</td>\n",
" <td>1.815600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1640</td>\n",
" <td>1.709500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1650</td>\n",
" <td>1.641000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1660</td>\n",
" <td>1.804000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1670</td>\n",
" <td>1.901300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1680</td>\n",
" <td>1.768000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1690</td>\n",
" <td>1.774800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1700</td>\n",
" <td>1.903000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1710</td>\n",
" <td>1.891900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1720</td>\n",
" <td>1.815200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1730</td>\n",
" <td>1.749200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1740</td>\n",
" <td>1.745600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1750</td>\n",
" <td>1.620700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1760</td>\n",
" <td>1.698900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1770</td>\n",
" <td>1.885000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1780</td>\n",
" <td>1.754800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1790</td>\n",
" <td>1.557600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1800</td>\n",
" <td>1.774500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1810</td>\n",
" <td>1.747700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1820</td>\n",
" <td>1.750500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1830</td>\n",
" <td>1.713000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1840</td>\n",
" <td>1.805900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1850</td>\n",
" <td>1.673200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1860</td>\n",
" <td>1.614400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1870</td>\n",
" <td>1.929300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1880</td>\n",
" <td>1.753100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1890</td>\n",
" <td>1.794700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1900</td>\n",
" <td>1.655000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1910</td>\n",
" <td>1.628000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1920</td>\n",
" <td>2.032200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1930</td>\n",
" <td>1.610700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1940</td>\n",
" <td>1.725400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1950</td>\n",
" <td>1.702800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1960</td>\n",
" <td>1.612100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1970</td>\n",
" <td>1.908500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1980</td>\n",
" <td>1.884100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1990</td>\n",
" <td>1.858000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2000</td>\n",
" <td>1.619800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2010</td>\n",
" <td>1.630200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2020</td>\n",
" <td>1.807300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2030</td>\n",
" <td>1.826500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2040</td>\n",
" <td>1.614800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2050</td>\n",
" <td>1.784200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2060</td>\n",
" <td>1.683500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2070</td>\n",
" <td>1.953000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2080</td>\n",
" <td>1.891300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2090</td>\n",
" <td>1.828600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2100</td>\n",
" <td>1.594500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2110</td>\n",
" <td>1.731000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2120</td>\n",
" <td>1.858400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2130</td>\n",
" <td>1.849700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2140</td>\n",
" <td>1.884600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2150</td>\n",
" <td>1.632100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2160</td>\n",
" <td>1.909900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2170</td>\n",
" <td>1.876000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2180</td>\n",
" <td>1.632800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2190</td>\n",
" <td>1.775200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2200</td>\n",
" <td>1.600000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2210</td>\n",
" <td>1.821300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2220</td>\n",
" <td>1.855500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2230</td>\n",
" <td>1.728500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2240</td>\n",
" <td>1.827200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2250</td>\n",
" <td>1.640800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2260</td>\n",
" <td>1.867500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2270</td>\n",
" <td>1.856600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2280</td>\n",
" <td>1.746500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2290</td>\n",
" <td>1.815800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2300</td>\n",
" <td>1.716500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2310</td>\n",
" <td>1.777700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2320</td>\n",
" <td>1.754400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2330</td>\n",
" <td>1.715200</td>\n",
" </tr>\n",
" </tbody>\n",
"</table><p>"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"TrainOutput(global_step=2334, training_loss=1.8247228751554383, metrics={'train_runtime': 5873.6387, 'train_samples_per_second': 0.795, 'train_steps_per_second': 0.397, 'total_flos': 7.552964234723328e+16, 'train_loss': 1.8247228751554383, 'epoch': 3.0})"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"\"\"\"\n",
"SFTTrainer: Supervised Fine-Tuningに関する設定\n",
"\n",
" - model:\n",
" - 読み込んだベースのモデル\n",
"\n",
" - train_dataset:\n",
" - トレーニングに使用するデータセット\n",
"\n",
" - eval_dataset:\n",
" - 評価に使用するデータセット\n",
"\n",
" - peft_config:\n",
" - PEFT（Parameter-Efficient Fine-Tuning）の設定（LoRAを利用する場合に指定）\n",
"\n",
" - max_seq_length:\n",
" - モデルに入力されるシーケンスの最大トークン長\n",
"\n",
" - dataset_text_field:\n",
" - データセット内の学習に使うテキストを含むフィールド名\n",
"\n",
" - tokenizer:\n",
" - モデルに対応するトークナイザー\n",
"\n",
" - args:\n",
" - トレーニングに使用するハイパーパラメータ（TrainingArgumentsの設定を指定）\n",
"\n",
" - packing:\n",
" - 入力シーケンスのパッキングを行うかどうかの設定 (False に設定することで、各入力を独立して扱う)\n",
"\"\"\"\n",
"trainer = SFTTrainer(\n",
" model=model,\n",
" train_dataset=dataset[\"train\"],\n",
" peft_config=peft_config,\n",
" max_seq_length= 512,\n",
" dataset_text_field=\"formatted_text\",\n",
" tokenizer=tokenizer,\n",
" args=training_arguments,\n",
" packing= False,\n",
")\n",
"\n",
"model.config.use_cache = False # キャッシュ機能を無効化\n",
"trainer.train() # トレーニングを実行"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"id": "tLOyoqCHYujQ"
},
"outputs": [],
"source": [
"# タスクとなるデータの読み込み。\n",
"# omnicampusの開発環境では、左にタスクのjsonlをドラッグアンドドロップしてから実行。\n",
"import json\n",
"datasets = []\n",
"with open(\"./elyza-tasks-100-TV_0.jsonl\", \"r\") as f:\n",
" item = \"\"\n",
" for line in f:\n",
" line = line.strip()\n",
" item += line\n",
" if item.endswith(\"}\"):\n",
" datasets.append(json.loads(item))\n",
" item = \"\""
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"id": "g8XTWwTeYujQ",
"outputId": "550984f7-985f-4b6d-e01c-1d5770272294"
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
" 0%| | 0/100 [00:00<?, ?it/s]/usr/local/lib/python3.10/dist-packages/transformers/generation/configuration_utils.py:590: UserWarning: `do_sample` is set to `False`. However, `temperature` is set to `0.2` -- this flag is only used in sample-based generation modes. You should set `do_sample=True` or unset `temperature`.\n",
" warnings.warn(\n",
"100%|██████████| 100/100 [13:07<00:00, 7.88s/it]\n"
]
}
],
"source": [
"# モデルによるタスクの推論。\n",
"from tqdm import tqdm\n",
"\n",
"results = []\n",
"for data in tqdm(datasets):\n",
"\n",
" input = data[\"input\"]\n",
"\n",
" prompt = f\"\"\"### 指示\n",
" {input}\n",
" ### 回答\n",
" \"\"\"\n",
"\n",
" tokenized_input = tokenizer.encode(prompt, add_special_tokens=False, return_tensors=\"pt\").to(model.device)\n",
" attention_mask = torch.ones_like(tokenized_input)\n",
"\n",
" with torch.no_grad():\n",
" outputs = model.generate(\n",
" tokenized_input,\n",
" attention_mask=attention_mask,\n",
" max_new_tokens=100,\n",
" do_sample=False,\n",
" temperature=0.2, # サンプリングの多様性を制御\n",
" repetition_penalty=1.2,\n",
" pad_token_id=tokenizer.eos_token_id\n",
" )[0]\n",
" output = tokenizer.decode(outputs[tokenized_input.size(1):], skip_special_tokens=True)\n",
"\n",
" results.append({\"task_id\": data[\"task_id\"], \"input\": input, \"output\": output})"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"id": "DfE_YGMAYujQ"
},
"outputs": [],
"source": [
"# こちらで生成されたjsolを提出してください。\n",
"# 本コードではinputとeval_aspectも含んでいますが、なくても問題ありません。\n",
"# 必須なのはtask_idとoutputとなります。\n",
"import re\n",
"jsonl_id = re.sub(\".*/\", \"\", new_model_id)\n",
"with open(f\"./{jsonl_id}-outputs.jsonl\", 'w', encoding='utf-8') as f:\n",
" for result in results:\n",
" json.dump(result, f, ensure_ascii=False, indent=None) # ensure_ascii=Falseを確実に指定\n",
" f.write('\\n')"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"id": "zq4Ko1FWakX9"
},
"outputs": [
{
"ename": "UnicodeEncodeError",
"evalue": "'latin-1' codec can't encode characters in position 12-20: ordinal not in range(256)",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mUnicodeEncodeError\u001b[0m Traceback (most recent call last)",
"Cell \u001b[0;32mIn[18], line 2\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;66;03m# モデルとトークナイザーをHugging Faceにアップロード\u001b[39;00m\n\u001b[0;32m----> 2\u001b[0m \u001b[43mmodel\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpush_to_hub\u001b[49m\u001b[43m(\u001b[49m\u001b[43mnew_model_id\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtoken\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mHF_TOKEN\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mprivate\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m)\u001b[49m \u001b[38;5;66;03m# Online saving\u001b[39;00m\n\u001b[1;32m 3\u001b[0m tokenizer\u001b[38;5;241m.\u001b[39mpush_to_hub(new_model_id, token\u001b[38;5;241m=\u001b[39mHF_TOKEN, private\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m) \u001b[38;5;66;03m# Online saving\u001b[39;00m\n",
"File \u001b[0;32m/usr/local/lib/python3.10/dist-packages/transformers/utils/hub.py:933\u001b[0m, in \u001b[0;36mPushToHubMixin.push_to_hub\u001b[0;34m(self, repo_id, use_temp_dir, commit_message, private, token, max_shard_size, create_pr, safe_serialization, revision, commit_description, tags, **deprecated_kwargs)\u001b[0m\n\u001b[1;32m 930\u001b[0m repo_url \u001b[38;5;241m=\u001b[39m deprecated_kwargs\u001b[38;5;241m.\u001b[39mpop(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mrepo_url\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m)\n\u001b[1;32m 931\u001b[0m organization \u001b[38;5;241m=\u001b[39m deprecated_kwargs\u001b[38;5;241m.\u001b[39mpop(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124morganization\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m)\n\u001b[0;32m--> 933\u001b[0m repo_id \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_create_repo\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 934\u001b[0m \u001b[43m \u001b[49m\u001b[43mrepo_id\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mprivate\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mprivate\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtoken\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtoken\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrepo_url\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mrepo_url\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43morganization\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43morganization\u001b[49m\n\u001b[1;32m 935\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 937\u001b[0m \u001b[38;5;66;03m# Create a new empty model card and eventually tag it\u001b[39;00m\n\u001b[1;32m 938\u001b[0m model_card \u001b[38;5;241m=\u001b[39m create_and_tag_model_card(\n\u001b[1;32m 939\u001b[0m repo_id, tags, token\u001b[38;5;241m=\u001b[39mtoken, ignore_metadata_errors\u001b[38;5;241m=\u001b[39mignore_metadata_errors\n\u001b[1;32m 940\u001b[0m )\n",
"File \u001b[0;32m/usr/local/lib/python3.10/dist-packages/transformers/utils/hub.py:740\u001b[0m, in \u001b[0;36mPushToHubMixin._create_repo\u001b[0;34m(self, repo_id, private, token, repo_url, organization)\u001b[0m\n\u001b[1;32m 737\u001b[0m repo_id \u001b[38;5;241m=\u001b[39m repo_id\u001b[38;5;241m.\u001b[39msplit(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m/\u001b[39m\u001b[38;5;124m\"\u001b[39m)[\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m]\n\u001b[1;32m 738\u001b[0m repo_id \u001b[38;5;241m=\u001b[39m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00morganization\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m/\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mrepo_id\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m--> 740\u001b[0m url \u001b[38;5;241m=\u001b[39m \u001b[43mcreate_repo\u001b[49m\u001b[43m(\u001b[49m\u001b[43mrepo_id\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mrepo_id\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtoken\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtoken\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mprivate\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mprivate\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mexist_ok\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m)\u001b[49m\n\u001b[1;32m 741\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m url\u001b[38;5;241m.\u001b[39mrepo_id\n",
"File \u001b[0;32m/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_validators.py:114\u001b[0m, in \u001b[0;36mvalidate_hf_hub_args.<locals>._inner_fn\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 111\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m check_use_auth_token:\n\u001b[1;32m 112\u001b[0m kwargs \u001b[38;5;241m=\u001b[39m smoothly_deprecate_use_auth_token(fn_name\u001b[38;5;241m=\u001b[39mfn\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__name__\u001b[39m, has_token\u001b[38;5;241m=\u001b[39mhas_token, kwargs\u001b[38;5;241m=\u001b[39mkwargs)\n\u001b[0;32m--> 114\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfn\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
"File \u001b[0;32m/usr/local/lib/python3.10/dist-packages/huggingface_hub/hf_api.py:3518\u001b[0m, in \u001b[0;36mHfApi.create_repo\u001b[0;34m(self, repo_id, token, private, repo_type, exist_ok, resource_group_id, space_sdk, space_hardware, space_storage, space_sleep_time, space_secrets, space_variables)\u001b[0m\n\u001b[1;32m 3516\u001b[0m headers \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_build_hf_headers(token\u001b[38;5;241m=\u001b[39mtoken)\n\u001b[1;32m 3517\u001b[0m \u001b[38;5;28;01mwhile\u001b[39;00m \u001b[38;5;28;01mTrue\u001b[39;00m:\n\u001b[0;32m-> 3518\u001b[0m r \u001b[38;5;241m=\u001b[39m \u001b[43mget_session\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpost\u001b[49m\u001b[43m(\u001b[49m\u001b[43mpath\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mheaders\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mheaders\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mjson\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mjson\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 3519\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m r\u001b[38;5;241m.\u001b[39mstatus_code \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m409\u001b[39m \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mCannot create repo: another conflicting operation is in progress\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;129;01min\u001b[39;00m r\u001b[38;5;241m.\u001b[39mtext:\n\u001b[1;32m 3520\u001b[0m \u001b[38;5;66;03m# Since https://github.com/huggingface/moon-landing/pull/7272 (private repo), it is not possible to\u001b[39;00m\n\u001b[1;32m 3521\u001b[0m \u001b[38;5;66;03m# concurrently create repos on the Hub for a same user. This is rarely an issue, except when running\u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 3524\u001b[0m \u001b[38;5;66;03m# dependent libraries.\u001b[39;00m\n\u001b[1;32m 3525\u001b[0m \u001b[38;5;66;03m# NOTE: If a fix is implemented server-side, we should be able to remove this retry mechanism.\u001b[39;00m\n\u001b[1;32m 3526\u001b[0m logger\u001b[38;5;241m.\u001b[39mdebug(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mCreate repo failed due to a concurrency issue. Retrying...\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
"File \u001b[0;32m/usr/local/lib/python3.10/dist-packages/requests/sessions.py:637\u001b[0m, in \u001b[0;36mSession.post\u001b[0;34m(self, url, data, json, **kwargs)\u001b[0m\n\u001b[1;32m 626\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mpost\u001b[39m(\u001b[38;5;28mself\u001b[39m, url, data\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, json\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[1;32m 627\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124mr\u001b[39m\u001b[38;5;124;03m\"\"\"Sends a POST request. Returns :class:`Response` object.\u001b[39;00m\n\u001b[1;32m 628\u001b[0m \n\u001b[1;32m 629\u001b[0m \u001b[38;5;124;03m :param url: URL for the new :class:`Request` object.\u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 634\u001b[0m \u001b[38;5;124;03m :rtype: requests.Response\u001b[39;00m\n\u001b[1;32m 635\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[0;32m--> 637\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrequest\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mPOST\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43murl\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdata\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdata\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mjson\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mjson\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
"File \u001b[0;32m/usr/local/lib/python3.10/dist-packages/requests/sessions.py:589\u001b[0m, in \u001b[0;36mSession.request\u001b[0;34m(self, method, url, params, data, headers, cookies, files, auth, timeout, allow_redirects, proxies, hooks, stream, verify, cert, json)\u001b[0m\n\u001b[1;32m 584\u001b[0m send_kwargs \u001b[38;5;241m=\u001b[39m {\n\u001b[1;32m 585\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtimeout\u001b[39m\u001b[38;5;124m\"\u001b[39m: timeout,\n\u001b[1;32m 586\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mallow_redirects\u001b[39m\u001b[38;5;124m\"\u001b[39m: allow_redirects,\n\u001b[1;32m 587\u001b[0m }\n\u001b[1;32m 588\u001b[0m send_kwargs\u001b[38;5;241m.\u001b[39mupdate(settings)\n\u001b[0;32m--> 589\u001b[0m resp \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msend\u001b[49m\u001b[43m(\u001b[49m\u001b[43mprep\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43msend_kwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 591\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m resp\n",
"File \u001b[0;32m/usr/local/lib/python3.10/dist-packages/requests/sessions.py:703\u001b[0m, in \u001b[0;36mSession.send\u001b[0;34m(self, request, **kwargs)\u001b[0m\n\u001b[1;32m 700\u001b[0m start \u001b[38;5;241m=\u001b[39m preferred_clock()\n\u001b[1;32m 702\u001b[0m \u001b[38;5;66;03m# Send the request\u001b[39;00m\n\u001b[0;32m--> 703\u001b[0m r \u001b[38;5;241m=\u001b[39m \u001b[43madapter\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msend\u001b[49m\u001b[43m(\u001b[49m\u001b[43mrequest\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 705\u001b[0m \u001b[38;5;66;03m# Total elapsed time of the request (approximately)\u001b[39;00m\n\u001b[1;32m 706\u001b[0m elapsed \u001b[38;5;241m=\u001b[39m preferred_clock() \u001b[38;5;241m-\u001b[39m start\n",
"File \u001b[0;32m/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_http.py:93\u001b[0m, in \u001b[0;36mUniqueRequestIdAdapter.send\u001b[0;34m(self, request, *args, **kwargs)\u001b[0m\n\u001b[1;32m 91\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"Catch any RequestException to append request id to the error message for debugging.\"\"\"\u001b[39;00m\n\u001b[1;32m 92\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m---> 93\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43msuper\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msend\u001b[49m\u001b[43m(\u001b[49m\u001b[43mrequest\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 94\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m requests\u001b[38;5;241m.\u001b[39mRequestException \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[1;32m 95\u001b[0m request_id \u001b[38;5;241m=\u001b[39m request\u001b[38;5;241m.\u001b[39mheaders\u001b[38;5;241m.\u001b[39mget(X_AMZN_TRACE_ID)\n",
"File \u001b[0;32m/usr/local/lib/python3.10/dist-packages/requests/adapters.py:667\u001b[0m, in \u001b[0;36mHTTPAdapter.send\u001b[0;34m(self, request, stream, timeout, verify, cert, proxies)\u001b[0m\n\u001b[1;32m 664\u001b[0m timeout \u001b[38;5;241m=\u001b[39m TimeoutSauce(connect\u001b[38;5;241m=\u001b[39mtimeout, read\u001b[38;5;241m=\u001b[39mtimeout)\n\u001b[1;32m 666\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 667\u001b[0m resp \u001b[38;5;241m=\u001b[39m \u001b[43mconn\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43murlopen\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 668\u001b[0m \u001b[43m \u001b[49m\u001b[43mmethod\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mrequest\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmethod\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 669\u001b[0m \u001b[43m \u001b[49m\u001b[43murl\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43murl\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 670\u001b[0m \u001b[43m \u001b[49m\u001b[43mbody\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mrequest\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbody\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 671\u001b[0m \u001b[43m \u001b[49m\u001b[43mheaders\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mrequest\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mheaders\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 672\u001b[0m \u001b[43m \u001b[49m\u001b[43mredirect\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[1;32m 673\u001b[0m \u001b[43m \u001b[49m\u001b[43massert_same_host\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[1;32m 674\u001b[0m \u001b[43m \u001b[49m\u001b[43mpreload_content\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[1;32m 675\u001b[0m \u001b[43m \u001b[49m\u001b[43mdecode_content\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[1;32m 676\u001b[0m \u001b[43m \u001b[49m\u001b[43mretries\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmax_retries\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 677\u001b[0m \u001b[43m \u001b[49m\u001b[43mtimeout\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtimeout\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 678\u001b[0m \u001b[43m \u001b[49m\u001b[43mchunked\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mchunked\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 679\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 681\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m (ProtocolError, \u001b[38;5;167;01mOSError\u001b[39;00m) \u001b[38;5;28;01mas\u001b[39;00m err:\n\u001b[1;32m 682\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mConnectionError\u001b[39;00m(err, request\u001b[38;5;241m=\u001b[39mrequest)\n",
"File \u001b[0;32m/usr/local/lib/python3.10/dist-packages/urllib3/connectionpool.py:714\u001b[0m, in \u001b[0;36mHTTPConnectionPool.urlopen\u001b[0;34m(self, method, url, body, headers, retries, redirect, assert_same_host, timeout, pool_timeout, release_conn, chunked, body_pos, **response_kw)\u001b[0m\n\u001b[1;32m 711\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_prepare_proxy(conn)\n\u001b[1;32m 713\u001b[0m \u001b[38;5;66;03m# Make the request on the httplib connection object.\u001b[39;00m\n\u001b[0;32m--> 714\u001b[0m httplib_response \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_make_request\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 715\u001b[0m \u001b[43m \u001b[49m\u001b[43mconn\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 716\u001b[0m \u001b[43m \u001b[49m\u001b[43mmethod\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 717\u001b[0m \u001b[43m \u001b[49m\u001b[43murl\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 718\u001b[0m \u001b[43m \u001b[49m\u001b[43mtimeout\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtimeout_obj\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 719\u001b[0m \u001b[43m \u001b[49m\u001b[43mbody\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mbody\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 720\u001b[0m \u001b[43m \u001b[49m\u001b[43mheaders\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mheaders\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 721\u001b[0m \u001b[43m \u001b[49m\u001b[43mchunked\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mchunked\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 722\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 724\u001b[0m \u001b[38;5;66;03m# If we're going to release the connection in ``finally:``, then\u001b[39;00m\n\u001b[1;32m 725\u001b[0m \u001b[38;5;66;03m# the response doesn't need to know about the connection. Otherwise\u001b[39;00m\n\u001b[1;32m 726\u001b[0m \u001b[38;5;66;03m# it will also try to release it and we'll have a double-release\u001b[39;00m\n\u001b[1;32m 727\u001b[0m \u001b[38;5;66;03m# mess.\u001b[39;00m\n\u001b[1;32m 728\u001b[0m response_conn \u001b[38;5;241m=\u001b[39m conn \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m release_conn \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n",
"File \u001b[0;32m/usr/local/lib/python3.10/dist-packages/urllib3/connectionpool.py:415\u001b[0m, in \u001b[0;36mHTTPConnectionPool._make_request\u001b[0;34m(self, conn, method, url, timeout, chunked, **httplib_request_kw)\u001b[0m\n\u001b[1;32m 413\u001b[0m conn\u001b[38;5;241m.\u001b[39mrequest_chunked(method, url, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mhttplib_request_kw)\n\u001b[1;32m 414\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 415\u001b[0m \u001b[43mconn\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrequest\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmethod\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43murl\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mhttplib_request_kw\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 417\u001b[0m \u001b[38;5;66;03m# We are swallowing BrokenPipeError (errno.EPIPE) since the server is\u001b[39;00m\n\u001b[1;32m 418\u001b[0m \u001b[38;5;66;03m# legitimately able to close the connection after sending a valid response.\u001b[39;00m\n\u001b[1;32m 419\u001b[0m \u001b[38;5;66;03m# With this behaviour, the received response is still readable.\u001b[39;00m\n\u001b[1;32m 420\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mBrokenPipeError\u001b[39;00m:\n\u001b[1;32m 421\u001b[0m \u001b[38;5;66;03m# Python 3\u001b[39;00m\n",
"File \u001b[0;32m/usr/local/lib/python3.10/dist-packages/urllib3/connection.py:244\u001b[0m, in \u001b[0;36mHTTPConnection.request\u001b[0;34m(self, method, url, body, headers)\u001b[0m\n\u001b[1;32m 242\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124muser-agent\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m (six\u001b[38;5;241m.\u001b[39mensure_str(k\u001b[38;5;241m.\u001b[39mlower()) \u001b[38;5;28;01mfor\u001b[39;00m k \u001b[38;5;129;01min\u001b[39;00m headers):\n\u001b[1;32m 243\u001b[0m headers[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mUser-Agent\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m _get_default_user_agent()\n\u001b[0;32m--> 244\u001b[0m \u001b[38;5;28;43msuper\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mHTTPConnection\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrequest\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmethod\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43murl\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mbody\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mbody\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mheaders\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mheaders\u001b[49m\u001b[43m)\u001b[49m\n",
"File \u001b[0;32m/usr/lib/python3.10/http/client.py:1282\u001b[0m, in \u001b[0;36mHTTPConnection.request\u001b[0;34m(self, method, url, body, headers, encode_chunked)\u001b[0m\n\u001b[1;32m 1279\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mrequest\u001b[39m(\u001b[38;5;28mself\u001b[39m, method, url, body\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, headers\u001b[38;5;241m=\u001b[39m{}, \u001b[38;5;241m*\u001b[39m,\n\u001b[1;32m 1280\u001b[0m encode_chunked\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m):\n\u001b[1;32m 1281\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"Send a complete request to the server.\"\"\"\u001b[39;00m\n\u001b[0;32m-> 1282\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_send_request\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmethod\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43murl\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mbody\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mheaders\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mencode_chunked\u001b[49m\u001b[43m)\u001b[49m\n",
"File \u001b[0;32m/usr/lib/python3.10/http/client.py:1323\u001b[0m, in \u001b[0;36mHTTPConnection._send_request\u001b[0;34m(self, method, url, body, headers, encode_chunked)\u001b[0m\n\u001b[1;32m 1320\u001b[0m encode_chunked \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mFalse\u001b[39;00m\n\u001b[1;32m 1322\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m hdr, value \u001b[38;5;129;01min\u001b[39;00m headers\u001b[38;5;241m.\u001b[39mitems():\n\u001b[0;32m-> 1323\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mputheader\u001b[49m\u001b[43m(\u001b[49m\u001b[43mhdr\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mvalue\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1324\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(body, \u001b[38;5;28mstr\u001b[39m):\n\u001b[1;32m 1325\u001b[0m \u001b[38;5;66;03m# RFC 2616 Section 3.7.1 says that text default has a\u001b[39;00m\n\u001b[1;32m 1326\u001b[0m \u001b[38;5;66;03m# default charset of iso-8859-1.\u001b[39;00m\n\u001b[1;32m 1327\u001b[0m body \u001b[38;5;241m=\u001b[39m _encode(body, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mbody\u001b[39m\u001b[38;5;124m'\u001b[39m)\n",
"File \u001b[0;32m/usr/local/lib/python3.10/dist-packages/urllib3/connection.py:224\u001b[0m, in \u001b[0;36mHTTPConnection.putheader\u001b[0;34m(self, header, *values)\u001b[0m\n\u001b[1;32m 222\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\" \"\"\"\u001b[39;00m\n\u001b[1;32m 223\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28many\u001b[39m(\u001b[38;5;28misinstance\u001b[39m(v, \u001b[38;5;28mstr\u001b[39m) \u001b[38;5;129;01mand\u001b[39;00m v \u001b[38;5;241m==\u001b[39m SKIP_HEADER \u001b[38;5;28;01mfor\u001b[39;00m v \u001b[38;5;129;01min\u001b[39;00m values):\n\u001b[0;32m--> 224\u001b[0m \u001b[43m_HTTPConnection\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mputheader\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mheader\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mvalues\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 225\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m six\u001b[38;5;241m.\u001b[39mensure_str(header\u001b[38;5;241m.\u001b[39mlower()) \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m SKIPPABLE_HEADERS:\n\u001b[1;32m 226\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[1;32m 227\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124murllib3.util.SKIP_HEADER only supports \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 228\u001b[0m \u001b[38;5;241m%\u001b[39m (\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m, \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;241m.\u001b[39mjoin(\u001b[38;5;28mmap\u001b[39m(\u001b[38;5;28mstr\u001b[39m\u001b[38;5;241m.\u001b[39mtitle, \u001b[38;5;28msorted\u001b[39m(SKIPPABLE_HEADERS))),)\n\u001b[1;32m 229\u001b[0m )\n",
"File \u001b[0;32m/usr/lib/python3.10/http/client.py:1255\u001b[0m, in \u001b[0;36mHTTPConnection.putheader\u001b[0;34m(self, header, *values)\u001b[0m\n\u001b[1;32m 1253\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m i, one_value \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28menumerate\u001b[39m(values):\n\u001b[1;32m 1254\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mhasattr\u001b[39m(one_value, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mencode\u001b[39m\u001b[38;5;124m'\u001b[39m):\n\u001b[0;32m-> 1255\u001b[0m values[i] \u001b[38;5;241m=\u001b[39m \u001b[43mone_value\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mencode\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mlatin-1\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1256\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(one_value, \u001b[38;5;28mint\u001b[39m):\n\u001b[1;32m 1257\u001b[0m values[i] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mstr\u001b[39m(one_value)\u001b[38;5;241m.\u001b[39mencode(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mascii\u001b[39m\u001b[38;5;124m'\u001b[39m)\n",
"\u001b[0;31mUnicodeEncodeError\u001b[0m: 'latin-1' codec can't encode characters in position 12-20: ordinal not in range(256)"
]
}
],
"source": [
"# モデルとトークナイザーをHugging Faceにアップロード\n",
"model.push_to_hub(new_model_id, token=HF_TOKEN, private=True) # Online saving\n",
"tokenizer.push_to_hub(new_model_id, token=HF_TOKEN, private=True) # Online saving"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "99LlHgFgc-RA"
},
"source": [
"https://liat-aip.sakura.ne.jp/wp/llmのための日本語インストラクションデータ作成/llmのための日本語インストラクションデータ-公開/\n",
"\n",
"関根聡, 安藤まや, 後藤美知子, 鈴木久美, 河原大輔, 井之上直也, 乾健太郎. ichikara-instruction: LLMのための日本語インストラクションデータの構築. 言語処理学会第30回年次大会(2024)"
]
}
],
"metadata": {
"accelerator": "GPU",
"colab": {
"gpuType": "T4",
"machine_shape": "hm",
"provenance": []
},
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.10.6"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
|