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ollama_ninja-v2-7b-q8_0.ipynb
{
"nbformat": 4,
"nbformat_minor": 0,
"metadata": {
"colab": {
"provenance": [],
"gpuType": "L4",
"collapsed_sections": [
"k8yY0-fnIRpP",
"h8aJAR8xHM1h"
],
"machine_shape": "hm",
"authorship_tag": "ABX9TyNhrEq+DKU/CIlaECh9f1JG",
"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/ad2bf08fa27b6cde4268b89ae5e4b1f0/ollama_ninja-v2-7b-q8_0.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/Local-Novel-LLM-project/Ninja-V2-7B/resolve/main/Ninja-V2-7B-Q8_0.gguf"
],
"metadata": {
"id": "V0xWAUncPLyo"
},
"execution_count": 4,
"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/Local-Novel-LLM-project/Ninja-V2-7B/resolve/main/Ninja-V2-7B-Q8_0.gguf\" # @param {type:\"string\"}\n"
],
"metadata": {
"cellView": "form",
"id": "8qonLAr4IpHb"
},
"execution_count": 5,
"outputs": []
},
{
"cell_type": "markdown",
"source": [
"## apt & pip\n",
"セッション再起動が必要です。\n"
],
"metadata": {
"id": "k8yY0-fnIRpP"
}
},
{
"cell_type": "code",
"source": [
"# aptとか読み込みとか"
],
"metadata": {
"id": "Z_DS4Oc4H8Iv"
},
"execution_count": 6,
"outputs": []
},
{
"cell_type": "code",
"source": [
"!apt-get -y install pciutils"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "gjQWeyAYO86q",
"outputId": "ddb1a877-2042-4161-cda1-87a96933e443"
},
"execution_count": 7,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Reading package lists... Done\n",
"Building dependency tree... Done\n",
"Reading state information... Done\n",
"pciutils is already the newest version (1:3.7.0-6).\n",
"0 upgraded, 0 newly installed, 0 to remove and 45 not upgraded.\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": "ac1aca7c-9b17-4884-9941-3a3d9662c63e"
},
"execution_count": 8,
"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": 9,
"outputs": []
},
{
"cell_type": "code",
"source": [
"!pip install mesop"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "AqZ9wwyVEkVb",
"outputId": "69e886a1-41d5-4a8a-b94d-3932f292c461"
},
"execution_count": 10,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Requirement already satisfied: mesop in /usr/local/lib/python3.10/dist-packages (0.8.0)\n",
"Requirement already satisfied: absl-py in /usr/local/lib/python3.10/dist-packages (from mesop) (1.4.0)\n",
"Requirement already satisfied: deepdiff==6.7.1 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: libcst==1.1.0 in /usr/local/lib/python3.10/dist-packages (from mesop) (1.1.0)\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==1.10.13 in /usr/local/lib/python3.10/dist-packages (from mesop) (1.10.13)\n",
"Requirement already satisfied: pygments in /usr/local/lib/python3.10/dist-packages (from mesop) (2.16.1)\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==6.7.1->mesop) (4.1.0)\n",
"Requirement already satisfied: typing-extensions>=3.7.4.2 in /usr/local/lib/python3.10/dist-packages (from libcst==1.1.0->mesop) (4.12.2)\n",
"Requirement already satisfied: typing-inspect>=0.4.0 in /usr/local/lib/python3.10/dist-packages (from libcst==1.1.0->mesop) (0.9.0)\n",
"Requirement already satisfied: pyyaml>=5.2 in /usr/local/lib/python3.10/dist-packages (from libcst==1.1.0->mesop) (6.0.1)\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: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from Jinja2>=3.0->flask->mesop) (2.1.5)\n",
"Requirement already satisfied: mypy-extensions>=0.3.0 in /usr/local/lib/python3.10/dist-packages (from typing-inspect>=0.4.0->libcst==1.1.0->mesop) (1.0.0)\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": [
"from oyama import oyama\n",
"import ollama\n",
"\n",
"# Model Path\n",
"model_name = oyama.run(model_path)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "FPJYTdEpMaS9",
"outputId": "bfc20a74-4762-4b86-c57f-dc94656ba184"
},
"execution_count": 11,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"command:=ollama --version\n",
"Output: Warning: could not connect to a running Ollama instance\n",
"Warning: client version is 0.1.44\n",
"Warning: could not connect to a running Ollama instance\n",
"Warning: client version is 0.1.44\n",
"command:=ollama serve\n",
"Server is not ready yet. Retrying...\n",
"Server is ready.\n",
"command:=ollama pull https://huggingface.co/Local-Novel-LLM-project/Ninja-V2-7B/resolve/main/Ninja-V2-7B-Q8_0.gguf\n",
"Output: \u001b[?25l\u001b[?25h\n",
"Error: invalid model name\n",
"\u001b[?25l\u001b[?25h\n",
"Error: invalid model name\n",
"model/Ninja-V2-7B-Q8_0.gguf already exists. Skipping download.\n",
"Modelfile:\n",
"FROM ./model/Ninja-V2-7B-Q8_0.gguf\n",
"\n",
"creating model layer transferring model data...\n",
"command:=ollama create Ninja-V2-7B-Q8_0 -f Ninja-V2-7B-Q8_0_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 ⠦ \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 sha256:1db9a0850048c685cc18c0775107ab5d8dd959a043ed9c9c710c47fa5d08b8bc \n",
"using existing layer sha256:82390a00361ecca3ca4eed22885bc58f818d7d119379cbec69a21a4634b05fe1 \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 \n",
"using existing layer sha256:1db9a0850048c685cc18c0775107ab5d8dd959a043ed9c9c710c47fa5d08b8bc \n",
"using existing layer sha256:82390a00361ecca3ca4eed22885bc58f818d7d119379cbec69a21a4634b05fe1 \n",
"writing manifest \n",
"success \u001b[?25h\n",
"command:=ollama run Ninja-V2-7B-Q8_0\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[?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[?25l\u001b[2K\u001b[1G\u001b[?25h\u001b[2K\u001b[1G\u001b[?25h\u001b[?25l\u001b[?25h\n",
"Enable Model:Ninja-V2-7B-Q8_0\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": "80f1c166-8a84-4621-8820-1b6f03b7d02d"
},
"execution_count": 12,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"\n",
"1.京都府 - 金閣寺（きんかくじ）：世界文化遺産に登録された美しい金色の建物で、日本文化の代表的な場所です。春は桜が咲き誇り、秋は紅葉が素晴らしく、四季折々の風景を楽しめます。\n",
"2.北海道 - 富良野美瑛（ふらのびえい）：広大な丘陵地帯や花畑が広がる自然豊かな場所で、映画「北の零度」やCM撮影で有名になりました。美しい風景を求めて世界中から観光客が訪れます。\n",
"3.沖縄県 - 沖縄美ら海水族館（おきなわちゅらうみすいぞくかん）：世界最大級のジンベエザメやマンタ、カラフルな熱帯魚が展示されています。沖縄の美しい海を再現した大水槽は必見です。\n",
"4.東京都 - 浅草寺（せんそうじ）：江戸時代から続く古刹で、雷門や仲見世通りが有名です。伝統的な日本文化を感じることができ、外国人観光客にも大人気のスポットです。\n",
"5.岡山県 - 岡山城（おかやまじょう）：別名「烏城」と呼ばれる美しい白鷺城で、歴史的な建造物が残る場所です。周辺には後楽園もあり、日本三大名勝の一つとして知られています。季節ごとに美しい景色を見せてくれます。\n",
"これらの場所はそれぞれ異なる魅力があり、日本の多彩な文化や自然美を楽しめるお薦めの観光地です。\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": "be124652-8d22-4a19-9441-21bd8efb7fe8"
},
"execution_count": 13,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"\n",
"1. 京都府 - 金閣寺（鹿苑寺）：黄金色に輝く美しい寺院で、日本文化の象徴的存在とも言われます。秋には紅葉が見事な景色を作り出します。\n",
"2. 沖縄県 - 沖縄美ら海水族館：世界最大級の水槽やジンベエザメの泳ぐ姿が見られる、大人気の水族館です。南国の雰囲気も味わえます。\n",
"3. 北海道 - 富良野・美瑠津スキー場：雪景色と広大な自然が楽しめる、ウィンタースポーツや観光の名所です。夏にはラベンダー畑も有名です。\n",
"4. 奈良県 - 東大寺・大仏殿：世界最大級のブロンズ像、大仏様があります。歴史的な建造物とともに、日本の伝統文化を感じることができます。\n",
"5. 長崎県 - 軍艦島（端島）：明治時代から昭和初期にかけて栄えた炭鉱島で、廃墟となった建造物が独特の雰囲気を醸し出しています。近年は世界遺産登録もされ、注目を集めています。\n",
"これらは日本を代表する観光地であり、それぞれに独自の魅力があります。旅行先として検討される際には、自分の好みや目的に合わせて選んでください。\n",
"CPU times: user 64.6 ms, sys: 11.2 ms, total: 75.8 ms\n",
"Wall time: 17.7 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": "4fbeb534-80e5-49af-92b0-2c9dff1a1118"
},
"execution_count": 14,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"と聞かれたら、私は答えることが出来ない。それぞれ魅力的すぎて選べないんだもの。\n",
"でも、まどか☆マギカに登場するキャラクターの中で、特に印象深く心を打たれるのが「鹿目まどか」というキャラクターだ。彼女は作品のヒロイン的存在であり、魔法少女として戦い続ける美しい少女。\n",
"そんな鹿目まどかが、自分の願いを叶えた後に選んだ運命とは？　彼女が最終的にたどり着いた場所とは？\n",
"これはネタバレになるので書けないけれど、まどか☆マギカは非常に感動的なストーリー展開を持っており、多くのファンが心を打たれた作品だ。\n",
"私自身もまどか☆マギカを見て以来、彼女たちの物語に魅了され続けている。そして、こうしてブログで感想を書くことが出来るのは、私にとっても幸せな時間だ。\n",
"最後に、まどか☆マギカを初めて見た人にはぜひ全話視聴することをお勧めしたい。きっとあなたも魅了されるはずだから。\n",
"CPU times: user 50.9 ms, sys: 10.3 ms, total: 61.3 ms\n",
"Wall time: 14.4 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": "928f5ad5-91af-4ed9-a90b-6f18d3768362"
},
"execution_count": 15,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"二番目に高い山は、富士山に次ぐ標高3,776メートルの北岳（きただけ）です。南アルプスに位置するこの山は、登山家や自然愛好家から人気があります。\n",
"CPU times: user 12.6 ms, sys: 2.19 ms, total: 14.8 ms\n",
"Wall time: 2.84 s\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": "709d7b0d-8aeb-4e90-b100-05e00299a2f1"
},
"execution_count": 16,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"\n",
"まとめ：\n",
"ウィキペディアは多言語のフリーインターネット百科事典で、世界中のボランティアが執筆している。オープンコンテントで商業広告がなく、非営利団体「ウィキメディア財団」によって所有・運営されている。ウィキというシステムを使用した百科事典であり、設立者の一人ラリー・サンガーによって命名された造語である。\n",
"CPU times: user 26.3 ms, sys: 5.91 ms, total: 32.2 ms\n",
"Wall time: 6.28 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": "f6842f12-59df-432c-c90a-a271656e1281"
},
"execution_count": 17,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"\n",
"まずはじめに、オペランドの優先順位に従って計算を進めていきます。\n",
"\n",
"1 + 1 * 2 = 3 (左から計算して1と1を足し合わせた後、そこに2を掛ける)\n",
"\n",
"続いて次の計算ですが、掛け算が先に来ます。\n",
"\n",
"3 + 3/2 = 4 (分数を整数で割った場合は切り上げられるため、3.5を切り上げて4となる)\n",
"\n",
"次に小数の計算を行います。\n",
"\n",
"2^10 = 1024 (10乗を計算した結果が1024である)\n",
"\n",
"最後に、計算結果を足し合わせます。\n",
"\n",
"3 + 4 + 1024\n",
"\n",
"これらを足し合わせると以下のようになります。\n",
"\n",
"7063 (3 + 4 = 7, そこに1024を加えた結果が7063である)\n",
"\n",
"検算のため、再度計算してみましょう。\n",
"\n",
"1 + 1 * 2 = 3 (同じ計算)\n",
"\n",
"3 + 3/2 = 4 (分数を整数で割って切り上げると4になる)\n",
"\n",
"最後に足し合わせます。\n",
"\n",
"7063\n",
"\n",
"検算の結果も一致しています。\n",
"\n",
"計算経過：1 + 1*2 + 3/2 + 2^10\n",
"計算結果：7063\n",
"\n",
"最終的な計算経過と結果は、上記の通りです。\n",
"CPU times: user 69.6 ms, sys: 10.7 ms, total: 80.3 ms\n",
"Wall time: 16.2 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=\"Ninja-V2-7B-Q8_0\",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": "fb7824c3-fdcb-4787-9695-608ab615e284",
"colab": {
"base_uri": "https://localhost:8080/"
}
},
"execution_count": 18,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"\n",
"「なんで哲学者は頭が良すぎるとハゲるの？」\n",
"答え：「彼らは深く考え過ぎて、自分の髪の毛まで哲学的問題にしちゃうから。」\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": "c61972c4-ae45-46ae-f02f-4d3ff5c3c8e1",
"colab": {
"base_uri": "https://localhost:8080/"
}
},
"execution_count": 19,
"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": [
"\n",
"\n",
"This short story, written by Kawabata Yasunari's contemporary, Tanizaki Junichiro, depicts a desperate situation of a beggar who steals from an old woman to feed his hunger. The narrative explores the human psyche and the moral dilemma faced by the protagonist as he struggles with starvation and societal norms. The story ends tragically, leaving the reader to ponder on the nature of humanity and survival in harsh conditions.\n",
"\n",
"\"\n",
"\n",
"\n",
"TITLE: \"The Old Woman's Coat\" (原題:「老婆の外套」)\n",
"AUTHOR: Tanizaki Junichiro (谷崎潤一郎)\n",
"PUBLICATION DATE: 1915 (大正4年) November Issue of \"Teikoku Bungaku\" (帝国文学)\n",
"\n",
"SUMMARY: The story follows a starving beggar who, after listening to an old woman's tale about her own poverty and desperation, succumbs to temptation and steals her coat. The protagonist's inner turmoil between his hunger and societal morality is vividly portrayed, culminating in a tragic ending that raises questions about human nature and survival under extreme conditions. Tanizaki Junichiro's exploration of these themes makes \"The Old Woman's Coat\" a poignant and thought-provoking tale.\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": 20,
"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": "6ace3f71-f601-447e-b004-027da7eebf1a",
"colab": {
"base_uri": "https://localhost:8080/"
}
},
"execution_count": 21,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
" 北岳（きただけ）は、南アルプス国立公園内に位置する標高3,190メートルの山であり、日本百名山および日本三百名山に選定されている。北岳は、仙丈ヶ岳と共に南アルプス主峰を形成しており、登山者からは険しくも美しい景観が人気である。また、周辺には北岳山荘やキャンプ場などの施設も整備されており、アクセスしやすい環境となっている。\n",
"\n",
"\" \n",
"\n",
"（注：この要約は、ウィキペディア日本語版北岳の記事から生成されました。）\n",
"CPU times: user 927 ms, sys: 11.1 ms, total: 938 ms\n",
"Wall time: 11.1 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": "cf51ca57-59a3-4c09-b2f3-06974b949a61",
"colab": {
"base_uri": "https://localhost:8080/"
}
},
"execution_count": 22,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
" 北岳（きただけ）は、南アルプス国立公園内に位置する標高3,190メートルの山であり、日本百名山および日本三百名山に選ばれている。北岳は、仙丈ヶ岳と共に南アルプス主峰を形成しており、登山者に人気がある。また、周辺には多くの高山植物や野生動物も生息している自然豊かな場所である。\n",
"\n",
"\" \n",
"\n",
"北岳（きただけ）は、南アルプス国立公園内に位置する日本を代表する山の一つで、標高3,190メートルを誇ります。この山は、日本百名山と日本三百名山の両方に選ばれており、登山愛好家たちから非常に人気があります。北岳は仙丈ヶ岳と並んで南アルプス主峰を形成しており、その雄大な景観は多くの登山者を魅了し続けています。\n",
"\n",
"さらに、北岳周辺には、美しい高山植物や希少な野生動物が生息しており、自然保護の観点からも重要な場所となっています。登山者は、自然を尊重し、ルールやマナーを守りながら、北岳の美しさを楽しむことが求められます。\n",
"\n",
"また、北岳へのアクセスには、登山口からのハイキングや山小屋での宿泊が必要となります。事前の準備や体調管理をしっかり行い、安全に楽しい登山を心がけることが大切です。\n",
"CPU times: user 876 ms, sys: 24.6 ms, total: 901 ms\n",
"Wall time: 22.2 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": "5803473f-d8f9-4364-86dd-014b2d611ea1",
"colab": {
"base_uri": "https://localhost:8080/"
}
},
"execution_count": 23,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
" 北岳は、山梨県に位置する南アルプスの一峰であり、日本百名山にも選ばれている3000メートル級の山である。また、隠しカテゴリとして外部リンクが切れている記事や座標書きかけの節のある項目なども含まれる。最終更新は2024年5月17日で、テキストはクリエイティブ・コモンズ 表示-継承ライセンスによって利用可能である。\n",
"CPU times: user 788 ms, sys: 7.23 ms, total: 795 ms\n",
"Wall time: 7.69 s\n"
]
}
]
},
{
"cell_type": "markdown",
"source": [
"# チャット形式遊ぶ"
],
"metadata": {
"id": "klIe7eoRDxNf"
}
},
{
"cell_type": "code",
"source": [],
"metadata": {
"id": "R5Gg8glIHWgH"
},
"execution_count": 23,
"outputs": []
},
{
"cell_type": "markdown",
"source": [
"## mesop"
],
"metadata": {
"id": "h8aJAR8xHM1h"
}
},
{
"cell_type": "code",
"source": [],
"metadata": {
"id": "8r-jfnqE_fZI"
},
"execution_count": 23,
"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": "1c39f949-c471-4eb3-b385-3fb14271849f"
},
"execution_count": 24,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"\n",
"\u001b[32mRunning server on: http://localhost:32123\u001b[0m\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": "24c03f5e-ce3f-43f2-983f-0af26fc882d3"
},
"execution_count": 25,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
" * Serving Flask app 'mesop.server.server'\n",
" * Debug mode: off\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": 728
},
"id": "1pYXJQkB_blP",
"outputId": "837bd5da-c79a-4e2e-961b-3b2bc6b5a2a8"
},
"execution_count": 26,
"outputs": [
{
"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": {}
},
{
"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",
"INFO:werkzeug:\u001b[33mPress CTRL+C to quit\u001b[0m\n"
]
}
]
}
]
}
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