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chottokun/ollama_elyza-jp8b.ipynb

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ollama_elyza-jp8b.ipynb
{
"nbformat": 4,
"nbformat_minor": 0,
"metadata": {
"colab": {
"provenance": [],
"gpuType": "L4",
"collapsed_sections": [
"k8yY0-fnIRpP",
"h8aJAR8xHM1h"
],
"machine_shape": "hm",
"name": "ollama_mmnga_Llama3-ArrowSE-8B-v0.3-gguf.ipynb",
"authorship_tag": "ABX9TyOHwB8hmNeL47BP0A8s/qt1",
"include_colab_link": true
},
"kernelspec": {
"name": "python3",
"display_name": "Python 3"
},
"language_info": {
"name": "python"
},
"accelerator": "GPU"
},
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "view-in-github",
"colab_type": "text"
},
"source": [
"<a href=\"https://colab.research.google.com/gist/chottokun/c1b18889c7dde24fe74d172c0488b501/ollama_elyza-jp8b.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
]
},
{
"cell_type": "code",
"source": [
"# 感謝とリファレンス\n",
"# https://github.com/HawkClaws/oyama\n",
"# https://huggingface.co/mmnga/Llama3-ArrowSE-8B-v0.3-gguf/resolve/main/Llama3-ArrowSE-8B-v0.3-Q5_K_M.gguf"
],
"metadata": {
"id": "V0xWAUncPLyo"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"source": [
"# ollama+oyamaで起動"
],
"metadata": {
"id": "Jr5QqUjdH2EG"
}
},
{
"cell_type": "code",
"source": [
"#@title ollama、huggingfaceのggufモデル選択\n",
"model_path = \"https://huggingface.co/mmnga/Llama3-ArrowSE-8B-v0.3-gguf/resolve/main/Llama3-ArrowSE-8B-v0.3-Q5_K_M.gguf\" # @param {type:\"string\"}\n"
],
"metadata": {
"cellView": "form",
"id": "8qonLAr4IpHb"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"source": [
"## apt & pip\n",
"セッション再起動がchat起動前に必要です。\n"
],
"metadata": {
"id": "k8yY0-fnIRpP"
}
},
{
"cell_type": "code",
"source": [
"# aptとか読み込みとか"
],
"metadata": {
"id": "Z_DS4Oc4H8Iv"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"!apt-get -y install pciutils"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "gjQWeyAYO86q",
"outputId": "11342eb1-3f90-436e-bf71-566a842ff26b"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Reading package lists... Done\n",
"Building dependency tree... Done\n",
"Reading state information... Done\n",
"The following additional packages will be installed:\n",
" libpci3 pci.ids\n",
"The following NEW packages will be installed:\n",
" libpci3 pci.ids pciutils\n",
"0 upgraded, 3 newly installed, 0 to remove and 45 not upgraded.\n",
"Need to get 0 B/343 kB of archives.\n",
"After this operation, 1,581 kB of additional disk space will be used.\n",
"Selecting previously unselected package pci.ids.\n",
"(Reading database ... 121925 files and directories currently installed.)\n",
"Preparing to unpack .../pci.ids_0.0~2022.01.22-1_all.deb ...\n",
"Unpacking pci.ids (0.0~2022.01.22-1) ...\n",
"Selecting previously unselected package libpci3:amd64.\n",
"Preparing to unpack .../libpci3_1%3a3.7.0-6_amd64.deb ...\n",
"Unpacking libpci3:amd64 (1:3.7.0-6) ...\n",
"Selecting previously unselected package pciutils.\n",
"Preparing to unpack .../pciutils_1%3a3.7.0-6_amd64.deb ...\n",
"Unpacking pciutils (1:3.7.0-6) ...\n",
"Setting up pci.ids (0.0~2022.01.22-1) ...\n",
"Setting up libpci3:amd64 (1:3.7.0-6) ...\n",
"Setting up pciutils (1:3.7.0-6) ...\n",
"Processing triggers for man-db (2.10.2-1) ...\n",
"Processing triggers for libc-bin (2.35-0ubuntu3.4) ...\n",
"/sbin/ldconfig.real: /usr/local/lib/libtbbbind_2_5.so.3 is not a symbolic link\n",
"\n",
"/sbin/ldconfig.real: /usr/local/lib/libtbbbind_2_0.so.3 is not a symbolic link\n",
"\n",
"/sbin/ldconfig.real: /usr/local/lib/libtbbmalloc_proxy.so.2 is not a symbolic link\n",
"\n",
"/sbin/ldconfig.real: /usr/local/lib/libtbbbind.so.3 is not a symbolic link\n",
"\n",
"/sbin/ldconfig.real: /usr/local/lib/libtbbmalloc.so.2 is not a symbolic link\n",
"\n",
"/sbin/ldconfig.real: /usr/local/lib/libtbb.so.12 is not a symbolic link\n",
"\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"!pip install -q git+https://github.com/HawkClaws/oyama.git ollama"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "9b6iQFXBMTIN",
"outputId": "b29475fc-4008-4be1-f36f-65872de2bc35"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
" Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"!pip install -q langchain-community tiktoken"
],
"metadata": {
"id": "zJSjnk17Mirg"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"!pip install mesop"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "AqZ9wwyVEkVb",
"outputId": "1726d10a-a306-485b-fa2b-af27ec6f14e9"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Requirement already satisfied: mesop in /usr/local/lib/python3.10/dist-packages (0.9.3)\n",
"Requirement already satisfied: absl-py in /usr/local/lib/python3.10/dist-packages (from mesop) (1.4.0)\n",
"Requirement already satisfied: deepdiff==6.* in /usr/local/lib/python3.10/dist-packages (from mesop) (6.7.1)\n",
"Requirement already satisfied: flask in /usr/local/lib/python3.10/dist-packages (from mesop) (2.2.5)\n",
"Requirement already satisfied: markdown in /usr/local/lib/python3.10/dist-packages (from mesop) (3.6)\n",
"Requirement already satisfied: protobuf in /usr/local/lib/python3.10/dist-packages (from mesop) (3.20.3)\n",
"Requirement already satisfied: pydantic in /usr/local/lib/python3.10/dist-packages (from mesop) (2.7.4)\n",
"Requirement already satisfied: pygments in /usr/local/lib/python3.10/dist-packages (from mesop) (2.16.1)\n",
"Requirement already satisfied: python-dotenv in /usr/local/lib/python3.10/dist-packages (from mesop) (1.0.1)\n",
"Requirement already satisfied: watchdog in /usr/local/lib/python3.10/dist-packages (from mesop) (4.0.1)\n",
"Requirement already satisfied: ordered-set<4.2.0,>=4.0.2 in /usr/local/lib/python3.10/dist-packages (from deepdiff==6.*->mesop) (4.1.0)\n",
"Requirement already satisfied: Werkzeug>=2.2.2 in /usr/local/lib/python3.10/dist-packages (from flask->mesop) (3.0.3)\n",
"Requirement already satisfied: Jinja2>=3.0 in /usr/local/lib/python3.10/dist-packages (from flask->mesop) (3.1.4)\n",
"Requirement already satisfied: itsdangerous>=2.0 in /usr/local/lib/python3.10/dist-packages (from flask->mesop) (2.2.0)\n",
"Requirement already satisfied: click>=8.0 in /usr/local/lib/python3.10/dist-packages (from flask->mesop) (8.1.7)\n",
"Requirement already satisfied: annotated-types>=0.4.0 in /usr/local/lib/python3.10/dist-packages (from pydantic->mesop) (0.7.0)\n",
"Requirement already satisfied: pydantic-core==2.18.4 in /usr/local/lib/python3.10/dist-packages (from pydantic->mesop) (2.18.4)\n",
"Requirement already satisfied: typing-extensions>=4.6.1 in /usr/local/lib/python3.10/dist-packages (from pydantic->mesop) (4.12.2)\n",
"Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from Jinja2>=3.0->flask->mesop) (2.1.5)\n"
]
}
]
},
{
"cell_type": "markdown",
"source": [
"## モデルの読み込みと動作確認"
],
"metadata": {
"id": "y4ODQIzlIKRf"
}
},
{
"cell_type": "code",
"source": [
"modelfile = \"\"\"\n",
"TEMPLATE \"{{ if .System }}<|start_header_id|>system<|end_header_id|>{{ .System }}<|eot_id|>{{ end }}{{ if .Prompt }}<|start_header_id|>user<|end_header_id|>{{ .Prompt }}<|eot_id|>{{ end }}<|start_header_id|>assistant<|end_header_id|>{{ .Response }}<|eot_id|>\"\n",
"PARAMETER stop \"<|start_header_id|>\"\n",
"PARAMETER stop \"<|end_header_id|>\"\n",
"PARAMETER stop \"<|eot_id|>\"\n",
"PARAMETER stop \"<|reserved_special_token\"\n",
"\"\"\""
],
"metadata": {
"id": "F7VmfJh-XG1N"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"from oyama import oyama\n",
"import ollama\n",
"\n",
"# Model Path\n",
"model_name = oyama.run(model_path, modelfile)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "FPJYTdEpMaS9",
"outputId": "c7a84686-8ab2-4153-b45e-2dc85ac76446"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"command:=ollama --version\n",
"Output: /bin/sh: 1: ollama: not found\n",
"/bin/sh: 1: ollama: not found\n",
"command:=curl -fsSL https://ollama.com/install.sh | sh\n",
"Output: >>> Downloading ollama...\n",
"############################################################################################# 100.0%\n",
">>> Installing ollama to /usr/local/bin...\n",
">>> Creating ollama user...\n",
">>> Adding ollama user to video group...\n",
">>> Adding current user to ollama group...\n",
">>> Creating ollama systemd service...\n",
">>> NVIDIA GPU installed.\n",
">>> The Ollama API is now available at 127.0.0.1:11434.\n",
">>> Install complete. Run \"ollama\" from the command line.\n",
">>> Downloading ollama...\n",
"############################################################################################# 100.0%\n",
">>> Installing ollama to /usr/local/bin...\n",
">>> Creating ollama user...\n",
">>> Adding ollama user to video group...\n",
">>> Adding current user to ollama group...\n",
">>> Creating ollama systemd service...\n",
">>> NVIDIA GPU installed.\n",
">>> The Ollama API is now available at 127.0.0.1:11434.\n",
">>> Install complete. Run \"ollama\" from the command line.\n",
"command:=ollama serve\n",
"Server is not ready yet. Retrying...\n",
"Server is ready.\n",
"command:=ollama pull https://huggingface.co/elyza/Llama-3-ELYZA-JP-8B-GGUF/resolve/main/Llama-3-ELYZA-JP-8B-q4_k_m.gguf\n",
"Output: \u001b[?25l\u001b[?25h\n",
"Error: invalid model name\n",
"\u001b[?25l\u001b[?25h\n",
"Error: invalid model name\n"
]
},
{
"output_type": "stream",
"name": "stderr",
"text": [
"Downloading Llama-3-ELYZA-JP-8B-q4_k_m.gguf: 100%|██████████| 4.58G/4.58G [00:58<00:00, 84.7MB/s]\n"
]
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"Modelfile:\n",
"FROM ./model/Llama-3-ELYZA-JP-8B-q4_k_m.gguf\n",
"\n",
"creating model layer transferring model data...\n",
"command:=ollama create Llama-3-ELYZA-JP-8B-q4_k_m -f Llama-3-ELYZA-JP-8B-q4_k_m_Modelfile\n",
"Output: \u001b[?25ltransferring model data ⠙ \u001b[?25h\u001b[?25l\u001b[2K\u001b[1Gtransferring model data ⠹ \u001b[?25h\u001b[?25l\u001b[2K\u001b[1Gtransferring model data ⠸ \u001b[?25h\u001b[?25l\u001b[2K\u001b[1Gtransferring model data ⠼ \u001b[?25h\u001b[?25l\u001b[2K\u001b[1Gtransferring model data ⠼ \u001b[?25h\u001b[?25l\u001b[2K\u001b[1Gtransferring model data ⠦ \u001b[?25h\u001b[?25l\u001b[2K\u001b[1Gtransferring model data ⠧ \u001b[?25h\u001b[?25l\u001b[2K\u001b[1Gtransferring model data ⠇ \u001b[?25h\u001b[?25l\u001b[2K\u001b[1Gtransferring model data ⠏ \u001b[?25h\u001b[?25l\u001b[2K\u001b[1Gtransferring model data ⠋ \u001b[?25h\u001b[?25l\u001b[2K\u001b[1Gtransferring model data ⠙ \u001b[?25h\u001b[?25l\u001b[2K\u001b[1Gtransferring model data ⠹ \u001b[?25h\u001b[?25l\u001b[2K\u001b[1Gtransferring model data ⠸ \u001b[?25h\u001b[?25l\u001b[2K\u001b[1Gtransferring model data ⠼ \u001b[?25h\u001b[?25l\u001b[2K\u001b[1Gtransferring model data ⠴ 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\u001b[?25h\u001b[?25l\u001b[2K\u001b[1Gtransferring model data ⠦ \u001b[?25h\u001b[?25l\u001b[2K\u001b[1Gtransferring model data ⠧ \u001b[?25h\u001b[?25l\u001b[2K\u001b[1Gtransferring model data ⠧ \u001b[?25h\u001b[?25l\u001b[2K\u001b[1Gtransferring model data ⠏ \u001b[?25h\u001b[?25l\u001b[2K\u001b[1Gtransferring model data ⠋ \u001b[?25h\u001b[?25l\u001b[2K\u001b[1Gtransferring model data ⠙ \u001b[?25h\u001b[?25l\u001b[2K\u001b[1Gtransferring model data ⠹ \u001b[?25h\u001b[?25l\u001b[2K\u001b[1Gtransferring model data ⠸ \u001b[?25h\u001b[?25l\u001b[2K\u001b[1Gtransferring model data ⠼ \u001b[?25h\u001b[?25l\u001b[2K\u001b[1Gtransferring model data ⠴ \u001b[?25h\u001b[?25l\u001b[2K\u001b[1Gtransferring model data ⠦ \u001b[?25h\u001b[?25l\u001b[2K\u001b[1Gtransferring model data \n",
"using existing layer sha256:91553c45080b11d95be21bb67961c9a5d2ed7556275423efaaad6df54ba9beae \n",
"using autodetected template llama3-instruct \n",
"creating new layer sha256:699d01a15aa31c073a5039bf5ea4297e9919aa06c5782356ee5e27c2bbda76a7 \n",
"writing manifest \n",
"success \u001b[?25h\n",
"\u001b[?25ltransferring model data ⠙ \u001b[?25h\u001b[?25l\u001b[2K\u001b[1Gtransferring model data ⠹ \u001b[?25h\u001b[?25l\u001b[2K\u001b[1Gtransferring model data ⠸ \u001b[?25h\u001b[?25l\u001b[2K\u001b[1Gtransferring model data ⠼ \u001b[?25h\u001b[?25l\u001b[2K\u001b[1Gtransferring model data ⠼ \u001b[?25h\u001b[?25l\u001b[2K\u001b[1Gtransferring model data ⠦ \u001b[?25h\u001b[?25l\u001b[2K\u001b[1Gtransferring model data ⠧ \u001b[?25h\u001b[?25l\u001b[2K\u001b[1Gtransferring model data ⠇ \u001b[?25h\u001b[?25l\u001b[2K\u001b[1Gtransferring model data ⠏ \u001b[?25h\u001b[?25l\u001b[2K\u001b[1Gtransferring model data ⠋ \u001b[?25h\u001b[?25l\u001b[2K\u001b[1Gtransferring model data ⠙ \u001b[?25h\u001b[?25l\u001b[2K\u001b[1Gtransferring model data ⠹ \u001b[?25h\u001b[?25l\u001b[2K\u001b[1Gtransferring model data ⠸ \u001b[?25h\u001b[?25l\u001b[2K\u001b[1Gtransferring model data ⠼ \u001b[?25h\u001b[?25l\u001b[2K\u001b[1Gtransferring model data ⠴ 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\u001b[?25h\u001b[?25l\u001b[2K\u001b[1Gtransferring model data ⠦ \u001b[?25h\u001b[?25l\u001b[2K\u001b[1Gtransferring model data ⠧ \u001b[?25h\u001b[?25l\u001b[2K\u001b[1Gtransferring model data ⠧ \u001b[?25h\u001b[?25l\u001b[2K\u001b[1Gtransferring model data ⠏ \u001b[?25h\u001b[?25l\u001b[2K\u001b[1Gtransferring model data ⠋ \u001b[?25h\u001b[?25l\u001b[2K\u001b[1Gtransferring model data ⠙ \u001b[?25h\u001b[?25l\u001b[2K\u001b[1Gtransferring model data ⠹ \u001b[?25h\u001b[?25l\u001b[2K\u001b[1Gtransferring model data ⠸ \u001b[?25h\u001b[?25l\u001b[2K\u001b[1Gtransferring model data ⠼ \u001b[?25h\u001b[?25l\u001b[2K\u001b[1Gtransferring model data ⠴ \u001b[?25h\u001b[?25l\u001b[2K\u001b[1Gtransferring model data ⠦ \u001b[?25h\u001b[?25l\u001b[2K\u001b[1Gtransferring model data \n",
"using existing layer sha256:91553c45080b11d95be21bb67961c9a5d2ed7556275423efaaad6df54ba9beae \n",
"using autodetected template llama3-instruct \n",
"creating new layer sha256:699d01a15aa31c073a5039bf5ea4297e9919aa06c5782356ee5e27c2bbda76a7 \n",
"writing manifest \n",
"success \u001b[?25h\n",
"command:=ollama run Llama-3-ELYZA-JP-8B-q4_k_m\n",
"Output: \u001b[?25l⠙ \u001b[?25h\u001b[?25l\u001b[2K\u001b[1G⠹ \u001b[?25h\u001b[?25l\u001b[2K\u001b[1G⠸ \u001b[?25h\u001b[?25l\u001b[2K\u001b[1G⠼ \u001b[?25h\u001b[?25l\u001b[2K\u001b[1G⠴ \u001b[?25h\u001b[?25l\u001b[2K\u001b[1G⠦ \u001b[?25h\u001b[?25l\u001b[2K\u001b[1G⠧ \u001b[?25h\u001b[?25l\u001b[2K\u001b[1G⠇ \u001b[?25h\u001b[?25l\u001b[2K\u001b[1G⠏ \u001b[?25h\u001b[?25l\u001b[2K\u001b[1G⠋ \u001b[?25h\u001b[?25l\u001b[2K\u001b[1G⠙ \u001b[?25h\u001b[?25l\u001b[2K\u001b[1G⠹ \u001b[?25h\u001b[?25l\u001b[2K\u001b[1G⠸ \u001b[?25h\u001b[?25l\u001b[2K\u001b[1G⠼ \u001b[?25h\u001b[?25l\u001b[2K\u001b[1G⠴ \u001b[?25h\u001b[?25l\u001b[2K\u001b[1G⠦ \u001b[?25h\u001b[?25l\u001b[2K\u001b[1G⠧ \u001b[?25h\u001b[?25l\u001b[2K\u001b[1G⠇ \u001b[?25h\u001b[?25l\u001b[2K\u001b[1G⠏ \u001b[?25h\u001b[?25l\u001b[2K\u001b[1G⠋ \u001b[?25h\u001b[?25l\u001b[2K\u001b[1G⠙ \u001b[?25h\u001b[?25l\u001b[2K\u001b[1G⠹ \u001b[?25h\u001b[?25l\u001b[2K\u001b[1G⠸ \u001b[?25h\u001b[?25l\u001b[2K\u001b[1G⠼ \u001b[?25h\u001b[?25l\u001b[2K\u001b[1G⠴ \u001b[?25h\u001b[?25l\u001b[2K\u001b[1G⠦ \u001b[?25h\u001b[?25l\u001b[2K\u001b[1G⠧ \u001b[?25h\u001b[?25l\u001b[2K\u001b[1G⠇ \u001b[?25h\u001b[?25l\u001b[2K\u001b[1G⠏ \u001b[?25h\u001b[?25l\u001b[2K\u001b[1G⠋ \u001b[?25h\u001b[?25l\u001b[2K\u001b[1G⠙ \u001b[?25h\u001b[?25l\u001b[2K\u001b[1G⠹ \u001b[?25h\u001b[?25l\u001b[2K\u001b[1G⠸ \u001b[?25h\u001b[?25l\u001b[2K\u001b[1G⠼ \u001b[?25h\u001b[?25l\u001b[2K\u001b[1G⠴ \u001b[?25h\u001b[?25l\u001b[2K\u001b[1G⠦ \u001b[?25h\u001b[?25l\u001b[2K\u001b[1G⠧ \u001b[?25h\u001b[?25l\u001b[2K\u001b[1G⠇ \u001b[?25h\u001b[?25l\u001b[?25l\u001b[2K\u001b[1G\u001b[?25h\u001b[2K\u001b[1G\u001b[?25h\u001b[?25l\u001b[?25h\n",
"\u001b[?25l⠙ \u001b[?25h\u001b[?25l\u001b[2K\u001b[1G⠹ \u001b[?25h\u001b[?25l\u001b[2K\u001b[1G⠸ \u001b[?25h\u001b[?25l\u001b[2K\u001b[1G⠼ \u001b[?25h\u001b[?25l\u001b[2K\u001b[1G⠴ \u001b[?25h\u001b[?25l\u001b[2K\u001b[1G⠦ \u001b[?25h\u001b[?25l\u001b[2K\u001b[1G⠧ \u001b[?25h\u001b[?25l\u001b[2K\u001b[1G⠇ \u001b[?25h\u001b[?25l\u001b[2K\u001b[1G⠏ \u001b[?25h\u001b[?25l\u001b[2K\u001b[1G⠋ \u001b[?25h\u001b[?25l\u001b[2K\u001b[1G⠙ \u001b[?25h\u001b[?25l\u001b[2K\u001b[1G⠹ \u001b[?25h\u001b[?25l\u001b[2K\u001b[1G⠸ \u001b[?25h\u001b[?25l\u001b[2K\u001b[1G⠼ \u001b[?25h\u001b[?25l\u001b[2K\u001b[1G⠴ \u001b[?25h\u001b[?25l\u001b[2K\u001b[1G⠦ \u001b[?25h\u001b[?25l\u001b[2K\u001b[1G⠧ \u001b[?25h\u001b[?25l\u001b[2K\u001b[1G⠇ \u001b[?25h\u001b[?25l\u001b[2K\u001b[1G⠏ \u001b[?25h\u001b[?25l\u001b[2K\u001b[1G⠋ \u001b[?25h\u001b[?25l\u001b[2K\u001b[1G⠙ \u001b[?25h\u001b[?25l\u001b[2K\u001b[1G⠹ \u001b[?25h\u001b[?25l\u001b[2K\u001b[1G⠸ \u001b[?25h\u001b[?25l\u001b[2K\u001b[1G⠼ \u001b[?25h\u001b[?25l\u001b[2K\u001b[1G⠴ \u001b[?25h\u001b[?25l\u001b[2K\u001b[1G⠦ \u001b[?25h\u001b[?25l\u001b[2K\u001b[1G⠧ \u001b[?25h\u001b[?25l\u001b[2K\u001b[1G⠇ \u001b[?25h\u001b[?25l\u001b[2K\u001b[1G⠏ \u001b[?25h\u001b[?25l\u001b[2K\u001b[1G⠋ \u001b[?25h\u001b[?25l\u001b[2K\u001b[1G⠙ \u001b[?25h\u001b[?25l\u001b[2K\u001b[1G⠹ \u001b[?25h\u001b[?25l\u001b[2K\u001b[1G⠸ \u001b[?25h\u001b[?25l\u001b[2K\u001b[1G⠼ \u001b[?25h\u001b[?25l\u001b[2K\u001b[1G⠴ \u001b[?25h\u001b[?25l\u001b[2K\u001b[1G⠦ \u001b[?25h\u001b[?25l\u001b[2K\u001b[1G⠧ \u001b[?25h\u001b[?25l\u001b[2K\u001b[1G⠇ \u001b[?25h\u001b[?25l\u001b[?25l\u001b[2K\u001b[1G\u001b[?25h\u001b[2K\u001b[1G\u001b[?25h\u001b[?25l\u001b[?25h\n",
"Enable Model:Llama-3-ELYZA-JP-8B-q4_k_m\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"response = ollama.chat(model=model_name, messages=[\n",
" {\n",
" 'role': 'user',\n",
" 'content': '日本でお薦めの観光地を5つあげてください。',\n",
" },\n",
"])\n",
"print(response['message']['content'])"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "h4MP20MNDhfj",
"outputId": "f56dd19d-500a-4952-86d0-b0454767b343"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"日本には多くの観光地がありますが、以下は特に人気のあるお勧めの観光地です。\n",
"\n",
"1. 成田山新勝寺 (千葉県) : 成田山新勝寺は、真言宗豊山派の大本山で、別名「不動尊」と呼ばれています。境内には、巨大な不動明王像や多くの寺宝が安置されており、多くの参拝者が訪れます。\n",
"2. 宮島 (広島県) : 宮島は、瀬戸内海に浮かぶ小さな島で、厳島神社とその周辺の景色を楽しむことができます。厳島神社は、世界遺産に登録されていて、特に潮が引いているときには、社殿の後ろに広がる大鳥居や美しい砂浜を散策することができます。\n",
"3. 箱根 (神奈川県) : 箱根は、東京から比較的近く、自然豊かで観光客が多く訪れるエリアです。芦ノ湖、箱根峠、強羅公園など美しい景色を堪能することができます。\n",
"4. 古都京都の文化財 (京都府) : 京都は世界遺産に登録されており、寺院や神社、庭園などの多くの文化財が残っています。特に金閣寺、清水寺、嵐山などは有名で、散策することができます。\n",
"5. 奄美大島 (鹿児島県) : 奄美大島は、南西諸島のなかでも最も北に位置し、亜熱帯雨林や白い砂浜が特徴的です。美しい海や山、川など自然を堪能することができます。\n",
"\n",
"これらの観光地は、日本でお薦めの場所の一部であり、他にも多くの素晴らしい観光地があります。\n"
]
}
]
},
{
"cell_type": "markdown",
"source": [
"# 質問に答えてもらいます。"
],
"metadata": {
"id": "bw253pGUDiiB"
}
},
{
"cell_type": "code",
"source": [
"%%time\n",
"response = ollama.chat(model=model_name, messages=[\n",
" {\n",
" 'role': 'user',\n",
" 'content': '日本でお薦めの観光地を5つあげてください。',\n",
" },\n",
"])\n",
"print(response['message']['content'])"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "L8F01aS8MiWh",
"outputId": "5b3636d7-c6be-4c48-d456-7723769802d2"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"日本には、多くの魅力的な観光地がありますが、以下は特に人気が高く、おすすめの観光地です。\n",
"\n",
"1. 成田山新勝寺 (千葉県) : 成田山新勝寺は、日本仏教の総本山で、年間800万人の参拝者を迎える人気の寺院です。境内には、多くの重要文化財や国宝が保存されていて、特に三重塔と大本堂は必見です。\n",
"\n",
"2. 広島平和記念公園 (広島県) : 広島平和記念公園は、原爆ドームや原爆死没者慰霊碑などを含む平和祈念資料館のある公園です。原爆投下の悲惨さと平和の尊さを学ぶことができます。\n",
"\n",
"3. 宮古島 (沖縄県) : 宮古島は、美しいビーチやサンゴ礁で有名な南国リゾートです。透明度の高い海でのシュノーケリングやスキューバダイビングも人気です。\n",
"\n",
"4. 横浜中華街 (神奈川県) : 横浜中華街は、横浜市の山下公園周辺に広がるアジア最大級のチャイナタウンです。美味しい中国料理を食べたり、色彩豊かな飾りやパフォーマンスを楽しむことができます。\n",
"\n",
"5. 白川郷合掌造り集落 (岐阜県) : 白川郷は、ユネスコ世界遺産にも登録された合掌造りの集落です。古き良き日本の景色が残っており、生活用水を確保するための独特な建築様式が有名です。\n",
"\n",
"これらは、日本でお薦めの観光地ですが、多くの魅力的なスポットがあるので、是非、行く予定や興味に応じて探してみてください。\n",
"CPU times: user 37.1 ms, sys: 4.83 ms, total: 41.9 ms\n",
"Wall time: 11.4 s\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"%%time\n",
"response = ollama.chat(model=model_name, messages=[\n",
" {\n",
" 'role': 'user',\n",
" 'content': 'まどか☆マギカで一番かわいいのは？',\n",
" },\n",
"])\n",
"print(response['message']['content'])"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "htFWenBFOTlT",
"outputId": "1a13c73b-16e2-4057-c3ab-d153c3afbceb"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"まどか☆マギカは人気のある作品ですし、可愛いキャラクターが多いですから、一番かわいいと感じるのは人によって違うと思います。\n",
"CPU times: user 5.87 ms, sys: 165 µs, total: 6.03 ms\n",
"Wall time: 1.15 s\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"%%time\n",
"query = \"\"\"日本で二番目に高い山を検討して答えてください。\n",
"\"\"\"\n",
"response = ollama.chat(model=model_name, messages=[\n",
" {\n",
" 'role': 'user',\n",
" 'content': query,\n",
" },\n",
"])\n",
"print(response['message']['content'])"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "K5Bi0gbBcvNs",
"outputId": "be577fd9-329b-4516-ce47-0d9a18f6dc4e"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"日本で二番目に高い山は、北海道の「旭岳」で標高2,291mです。\n",
"CPU times: user 4.36 ms, sys: 264 µs, total: 4.63 ms\n",
"Wall time: 827 ms\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"%%time\n",
"query = \"\"\"\n",
"以下を簡単にまとめてください。\n",
"\n",
"ウィキペディア（英: Wikipedia）は、世界中のボランティアの共同作業によって執筆及び作成されるフリーの多言語[6]インターネット百科事典[7]。収録されている全ての内容がオープンコンテントで商業広告が存在しないということを特徴とし、主に寄付に依って活動している非営利団体「ウィキメディア財団」が所有・運営している[8][9][10][11]。「ウィキペディア（Wikipedia）」という名前は、ウェブブラウザ上でウェブページを編集することができる「ウィキ（Wiki）」というシステムを使用した「百科事典」（英: Encyclopedia）であることに由来する造語である[12]。設立者の1人であるラリー・サンガーにより命名された[13][14]。\n",
"\"\"\"\n",
"response = ollama.chat(model=model_name, messages=[\n",
" {\n",
" 'role': 'user',\n",
" 'content': query,\n",
" },\n",
"])\n",
"print(response['message']['content'])"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "yuoewzZkdS8f",
"outputId": "ccff8810-e5b5-4c00-e897-8964c0d9fc11"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"以下は、文章の簡単なまとめです。\n",
"\n",
"ウィキペディアは、ボランティアが共同で執筆・作成するフリーの多言語インターネット百科事典です。収録内容はオープンコンテントで商業広告はなく、非営利団体「ウィキメディア財団」が所有・運営しています。名前の由来は、「ウェブブラウザ上で編集可能なWikiシステム」を使用した百科事典であることから造語されたものです。\n",
"CPU times: user 15.5 ms, sys: 3.27 ms, total: 18.8 ms\n",
"Wall time: 3.28 s\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"%%time\n",
"query = \"\"\"\n",
"1+1*2+3/2+2^10を計算してください。計算が終わったら検算をしてください。最後に最終的な計算経過と結果を答えてください。\n",
"\"\"\"\n",
"response = ollama.chat(model=model_name, messages=[\n",
" {\n",
" 'role': 'user',\n",
" 'content': query,\n",
" },\n",
"])\n",
"print(response['message']['content'])"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "HWZrbaPH-VAt",
"outputId": "aa8e0f74-f0be-4fec-d59e-a1747b1cf9c4"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"計算を行います。\n",
"\n",
"1. 1+1*2=1+2=3\n",
"2. 3/2=1.5\n",
"3. 2^10=1024\n",
"\n",
"合計すると、以下の式で表されます。\n",
"\n",
"3+1.5+1024\n",
"\n",
"計算経過:\n",
"3+(1.5+1024)=3+1025=1028\n",
"CPU times: user 10.1 ms, sys: 354 µs, total: 10.4 ms\n",
"Wall time: 2.17 s\n"
]
}
]
},
{
"cell_type": "markdown",
"source": [
"# langchainで使ってみます。"
],
"metadata": {
"id": "3_edPDrVDsBb"
}
},
{
"cell_type": "markdown",
"source": [
"## 動作確認"
],
"metadata": {
"id": "zRPu7fqHKKyg"
}
},
{
"cell_type": "code",
"source": [
"from langchain_community.llms import Ollama\n",
"\n",
"llm = Ollama(model=\"Llama3-ArrowSE-8B-v0\",temperature=0,)\n",
"\n",
"# llm = Ollama(\n",
"# model=model_path,\n",
"# temperature=0,\n",
"# )\n",
"\n",
"print(llm.invoke(\"面白いジョークを言ってください。\"))"
],
"metadata": {
"id": "irIGA8DuQb7c",
"outputId": "308769d7-f807-4c51-aa82-5b416cad5d64",
"colab": {
"base_uri": "https://localhost:8080/"
}
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"挑戦します！ここに、面白いジョークがあります：\n",
"\n",
"なぜ、宇宙飛行士は卵を食べないのか？\n",
"\n",
"答え：それは、卵が「crack\"-ingに危険だからです！\n",
"\n",
"（訳：宇宙で卵が割れると大変なことになるから）\n",
"\n",
"いかがでしょうか？\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"from langchain.chains.summarize import load_summarize_chain\n",
"from langchain_community.document_loaders import WebBaseLoader\n",
"\n",
"loader = WebBaseLoader(\"https://www.aozora.gr.jp/cards/000879/files/127_15260.html\")\n",
"docs = loader.load()\n",
"chain = load_summarize_chain(llm, chain_type=\"stuff\")\n",
"result = chain.invoke(docs)\n",
"print(result[\"output_text\"])"
],
"metadata": {
"id": "JYAl9aIfQVi4",
"outputId": "c564a000-e26c-4eae-a340-1660727d284a",
"colab": {
"base_uri": "https://localhost:8080/"
}
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stderr",
"text": [
"WARNING:langchain_community.utils.user_agent:USER_AGENT environment variable not set, consider setting it to identify your requests.\n"
]
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"この小説は芥川龍之介の作品で、「検非違使」という役人に扮した下人が老婆を捕らえ、殺害するという内容です。下人は老婆が死骸から髪を抜く行為を目撃し、憎悪を抱きますが、老婆の話を聞くうちに、饑死や盗人になるかもしれないという現実を知り、勇気を得て老婆を殺害します。\n"
]
}
]
},
{
"cell_type": "markdown",
"source": [
"## 要約させてみます。"
],
"metadata": {
"id": "PrCoHVTDKTD8"
}
},
{
"cell_type": "code",
"source": [
"# WebLoader\n",
"loader = WebBaseLoader(\"https://ja.wikipedia.org/wiki/%E5%8C%97%E5%B2%B3\")"
],
"metadata": {
"id": "LpP1Z36FQX3W"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"source": [],
"metadata": {
"id": "wRWbCob3IA91"
}
},
{
"cell_type": "code",
"source": [
"%%time\n",
"docs = loader.load()\n",
"chain = load_summarize_chain(llm, chain_type=\"stuff\")\n",
"result = chain.invoke(docs)\n",
"print(result[\"output_text\"])"
],
"metadata": {
"id": "AU2CsdpdQlHu",
"outputId": "8bb50464-c795-45bb-8f84-48b5b4eec838",
"colab": {
"base_uri": "https://localhost:8080/"
}
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"北岳は山梨県南アルプス市に位置する3000メートル峰の独立峰です。日本百名山、南アルプス市の地理、山梨県の山などに分類されます。\n",
"CPU times: user 788 ms, sys: 7.23 ms, total: 796 ms\n",
"Wall time: 3.78 s\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"%%time\n",
"docs = loader.load()\n",
"chain = load_summarize_chain(llm, chain_type=\"refine\")\n",
"result = chain.invoke(docs)\n",
"print(result[\"output_text\"])"
],
"metadata": {
"id": "5y4lrTyLQnW8",
"outputId": "02bbae19-719a-42eb-bab2-15757383a865",
"colab": {
"base_uri": "https://localhost:8080/"
}
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"北岳は山梨県南アルプス市に位置する3000メートル峰の独立峰です。日本百名山、南アルプス市の地理、山梨県の山などに分類されます。\n",
"CPU times: user 718 ms, sys: 5.64 ms, total: 724 ms\n",
"Wall time: 2.29 s\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"%%time\n",
"from langchain.chains.combine_documents.stuff import StuffDocumentsChain\n",
"from langchain_core.prompts import PromptTemplate\n",
"\n",
"prompt_template = \"\"\"以下のテキストの簡潔な要約をします。:\n",
"\"{text}\"\n",
"要約:\"\"\"\n",
"prompt = PromptTemplate.from_template(prompt_template)\n",
"\n",
"# LLMChain\n",
"# llm_chain = LLMChain(llm=llm, prompt=prompt)\n",
"\n",
"# StuffDocumentsChain\n",
"stuff_chain = prompt | llm\n",
"\n",
"# loader\n",
"docs = loader.load()\n",
"# generate\n",
"print(stuff_chain.invoke({\"text\": docs}))"
],
"metadata": {
"id": "pJC9AA-PVTUq",
"outputId": "9aaaa665-10bf-4782-d2ab-374424b019e2",
"colab": {
"base_uri": "https://localhost:8080/"
}
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"この文章は、ウィキペディアの「日本百名山」に関するページの内容を抜粋したものです。日本百名山とは、日本の山々を選定し、百座の山を選び出したもので、深田久弥が著書「日本百名山」で紹介しました。\n",
"\n",
"文章中には、山梨県の山や南アルプスの山など、多くの山の名前が挙げられています。具体的には、間ノ岳、塩見岳、悪沢岳、赤石岳、聖岳、光岳などの山々が名を連ねています。また、西日本の山や深田久弥に関する情報も含まれています。\n",
"\n",
"文章は、ウィキペディアの形式で書かれていて、典拠管理データベースやVIAF、国立図書館など、信頼性の高い情報源に基づいています。\n",
"CPU times: user 830 ms, sys: 18.2 ms, total: 848 ms\n",
"Wall time: 6.75 s\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"from langchain_core.prompts import ChatPromptTemplate\n",
"from langchain_core.pydantic_v1 import BaseModel, Field\n",
"\n",
"tagging_prompt = ChatPromptTemplate.from_template(\n",
" \"\"\"\n",
"Extract the desired information from the following passage.\n",
"\n",
"Only extract the properties mentioned in the 'Classification' function.\n",
"\n",
"Passage:\n",
"{input}\n",
"\"\"\"\n",
")\n",
"\n",
"\n",
"class Classification(BaseModel):\n",
" sentiment: str = Field(description=\"The sentiment of the text\")\n",
" aggressiveness: int = Field(\n",
" description=\"How aggressive the text is on a scale from 1 to 10\"\n",
" )\n",
" language: str = Field(description=\"The language the text is written in\")\n",
"\n",
"\n",
"tagging_chain = tagging_prompt | llm"
],
"metadata": {
"id": "aZGKbDlESB0t"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"inp = \"Estoy increiblemente contento de haberte conocido! Creo que seremos muy buenos amigos!\"\n",
"tagging_chain.invoke({\"input\": inp})"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 35
},
"id": "h2oigNAbSNgf",
"outputId": "a6aae290-1b34-47a2-acf7-cea78788c064"
},
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"'There is no \"Classification\" function or properties mentioned in the given passage.'"
],
"application/vnd.google.colaboratory.intrinsic+json": {
"type": "string"
}
},
"metadata": {},
"execution_count": 32
}
]
},
{
"cell_type": "code",
"source": [
"inp = \"Estoy muy enojado con vos! Te voy a dar tu merecido!\"\n",
"res = tagging_chain.invoke({\"input\": inp})"
],
"metadata": {
"id": "UbwklWDzSPW9"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"source": [
"# チャット形式遊ぶ"
],
"metadata": {
"id": "klIe7eoRDxNf"
}
},
{
"cell_type": "code",
"source": [],
"metadata": {
"id": "R5Gg8glIHWgH"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"source": [
"## mesop"
],
"metadata": {
"id": "h8aJAR8xHM1h"
}
},
{
"cell_type": "code",
"source": [],
"metadata": {
"id": "8r-jfnqE_fZI"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"import mesop as me\n",
"import mesop.labs as mel\n",
"\n",
"me.colab_run()"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "2gI8-eKo_VP7",
"outputId": "c329bbf9-6b1b-45ea-8b7b-7128697979c2"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"\n",
"\u001b[32mRunning server on: http://localhost:32123\u001b[0m\n",
" * Serving Flask app 'mesop.server.server'\n",
" * Debug mode: off\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"@me.page(\n",
" path=\"/chat\",\n",
" title=\"Chat demo\",\n",
")\n",
"def page():\n",
" mel.chat(transform, title=\"Demo Chat\", bot_user=model_path)\n",
"\n",
"def transform(input: str, history: list[mel.ChatMessage]) -> str:\n",
"\n",
" messages = []\n",
" for his in history:\n",
" messages.extend([{'role': his.role, 'content': his.content}])\n",
"\n",
" last_message = [\n",
" {\n",
" 'role': 'user',\n",
" 'content': input,\n",
" },\n",
" ]\n",
"\n",
" messages.extend(last_message)\n",
"\n",
" res = ollama.chat(model=model_name,\n",
" messages=messages)\n",
"\n",
" res = res['message']['content']\n",
" return res"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "h9BjdHfu_Xis",
"outputId": "7275ea8a-347f-42d6-b152-8758894cfdc1"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stderr",
"text": [
"INFO:werkzeug:\u001b[31m\u001b[1mWARNING: This is a development server. Do not use it in a production deployment. Use a production WSGI server instead.\u001b[0m\n",
" * Running on all addresses (::)\n",
" * Running on http://[::1]:32123\n",
" * Running on http://[::1]:32123\n"
]
}
]
},
{
"cell_type": "markdown",
"source": [
"## チャット"
],
"metadata": {
"id": "SNdeF8V2HP3Y"
}
},
{
"cell_type": "code",
"source": [
"me.colab_show(path=\"/chat\", height=600)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 639
},
"id": "1pYXJQkB_blP",
"outputId": "8f4ab857-c328-4495-d339-8815143ed702"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stderr",
"text": [
"INFO:werkzeug:\u001b[33mPress CTRL+C to quit\u001b[0m\n"
]
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"<IPython.core.display.Javascript object>"
],
"application/javascript": [
"(async (port, path, width, height, cache, element) => {\n",
" if (!google.colab.kernel.accessAllowed && !cache) {\n",
" return;\n",
" }\n",
" element.appendChild(document.createTextNode(''));\n",
" const url = await google.colab.kernel.proxyPort(port, {cache});\n",
" const iframe = document.createElement('iframe');\n",
" iframe.src = new URL(path, url).toString();\n",
" iframe.height = height;\n",
" iframe.width = width;\n",
" iframe.style.border = 0;\n",
" iframe.allow = [\n",
" 'accelerometer',\n",
" 'autoplay',\n",
" 'camera',\n",
" 'clipboard-read',\n",
" 'clipboard-write',\n",
" 'gyroscope',\n",
" 'magnetometer',\n",
" 'microphone',\n",
" 'serial',\n",
" 'usb',\n",
" 'xr-spatial-tracking',\n",
" ].join('; ');\n",
" element.appendChild(iframe);\n",
" })(32123, \"/chat\", \"100%\", 600, false, window.element)"
]
},
"metadata": {}
}
]
}
]
}
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