Gemma/Integrate_with_OneTwo.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Tce3stUlHN0L"
      },
      "source": [
        "##### 版權所有 2024 Google LLC."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "cellView": "form",
        "id": "tuOe1ymfHZPu"
      },
      "outputs": [],
      "source": [
        "# @title Licensed under the Apache License, Version 2.0 (the \"License\");\n",
        "# you may not use this file except in compliance with the License.\n",
        "# You may obtain a copy of the License at\n",
        "#\n",
        "# https://www.apache.org/licenses/LICENSE-2.0\n",
        "#\n",
        "# Unless required by applicable law or agreed to in writing, software\n",
        "# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
        "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
        "# See the License for the specific language governing permissions and\n",
        "# limitations under the License."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dfsDR_omdNea"
      },
      "source": [
        "# Gemma - Integrate with OneTwo\n",
        "\n",
        "這個筆記本展示了如何通過 [OneTwo](https://github.com/google-deepmind/onetwo) 使用 Gemma。OneTwo 是由 Google DeepMind 發佈的一個 Python 函式庫，旨在簡化與 LLMs 的互動。\n",
        "\n",
        "<table align=\"left\">\n",
        "  <td>\n",
        "    <a target=\"_blank\" href=\"https://colab.research.google.com/github/doggy8088/gemma-cookbook/blob/zh-tw-240628/Gemma/Integrate_with_OneTwo.ipynb\"><img src=\"https://www.tensorflow.org/images/colab_logo_32px.png\" />Run in Google Colab</a>\n",
        "  </td>\n",
        "</table>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FaqZItBdeokU"
      },
      "source": [
        "## 設定\n",
        "\n",
        "### 選擇 Colab 執行環境\n",
        "要完成此指南，你需要擁有一個具有足夠資源的 Colab 執行環境來執行 Gemma 模型。在這種情況下，你可以使用 T4 GPU：\n",
        "\n",
        "1. 在 Colab 視窗的右上角，選擇 **▾ (其他連接選項)** 。\n",
        "2. 選擇 **變更執行環境類型** 。\n",
        "3. 在 **硬體加速器** 下，選擇 **T4 GPU** 。\n",
        "\n",
        "### Gemma 設定\n",
        "\n",
        "要完成此指南，你首先需要完成 [Gemma 設定](https://ai.google.dev/gemma/docs/setup)中的設定指示。Gemma 設定指示會告訴你如何執行以下操作：\n",
        "\n",
        "* 獲取在 [Kaggle](kaggle.com) 上的 Gemma 訪問權限。\n",
        "* 生成並配置 [Kaggle 用戶名和 API 金鑰](https://www.kaggle.com/docs/api)。將它們設置為 Colab 機密 'KAGGLE_USERNAME' 和 'KAGGLE_KEY'。"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "A9sUQ4WrP-Yr"
      },
      "outputs": [],
      "source": [
        "import os\n",
        "from google.colab import userdata\n",
        "\n",
        "# Note: `userdata.get` is a Colab API. If you're not using Colab, set the env\n",
        "# vars as appropriate for your system.\n",
        "os.environ[\"KAGGLE_USERNAME\"] = userdata.get(\"KAGGLE_USERNAME\")\n",
        "os.environ[\"KAGGLE_KEY\"] = userdata.get(\"KAGGLE_KEY\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZjSga1qnNdGh",
        "outputId": "c0988fed-2d09-470c-fe3e-c120d1277469"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: kaggle in /usr/local/lib/python3.10/dist-packages (1.6.14)\n",
            "Requirement already satisfied: six>=1.10 in /usr/local/lib/python3.10/dist-packages (from kaggle) (1.16.0)\n",
            "Requirement already satisfied: certifi>=2023.7.22 in /usr/local/lib/python3.10/dist-packages (from kaggle) (2024.2.2)\n",
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            "Requirement already satisfied: kagglehub in /usr/local/lib/python3.10/dist-packages (0.2.5)\n",
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            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->kagglehub) (2024.2.2)\n"
          ]
        }
      ],
      "source": [
        "# Install kaggle\n",
        "! pip install kaggle\n",
        "! pip install kagglehub"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 319,
          "referenced_widgets": [
            "5bc98192c4d04446950daf04d8323995",
            "90faadd0f12b4652a87a02c302da90a2",
            "d2c1d4f4999c442c9f402533a7fb1f00",
            "d23b1a9972704ac1bfab6bf46b4eb370",
            "a49fcded24564bf09baeaaa6750b6236",
            "f229e35869a8484a95c00495cf203f27",
            "5120c2f46848474f9bfc162276ebe2f4",
            "42dc31541eca4f7e9ac1b2af05006001",
            "62b3fed6f1304d59aea0759347ada417",
            "ef9f62106d864e98aa4d51ec5364cf7d",
            "73e83fe9cbdc4c71abfeb6505902ba68",
            "f5e5d87e28c44d13b3c921cbcfe30ac5",
            "53d827b6833c4a5e8c76b64f0c0ac2e6",
            "0403fb0a10364be7858141a0f5c21a52",
            "d760e23ee70a4a50a16975b64c6acfaa",
            "86e2cb29085e4b96b909075ac67d4e3f",
            "7ed33c702b37440fa1cf2ef0efcf431c"
          ]
        },
        "id": "HdTPjx_sHPUG",
        "outputId": "49cb4a04-e062-4e44-860c-59f8e52769a8"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Kaggle credentials set.\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "5bc98192c4d04446950daf04d8323995",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "VBox(children=(HTML(value='<center> <img\\nsrc=https://www.kaggle.com/static/images/site-logo.png\\nalt=\\'Kaggle…"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "import kagglehub\n",
        "from kagglehub import auth\n",
        "\n",
        "auth.set_kaggle_credentials(\n",
        "    username=userdata.get(\"KAGGLE_USERNAME\"), api_key=userdata.get(\"KAGGLE_KEY\")\n",
        ")\n",
        "kagglehub.login()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "06e_SNhjI0Jd"
      },
      "outputs": [],
      "source": [
        "import os\n",
        "\n",
        "VARIANT = \"2b\"  # @param ['2b', '1.1-2b-it', '7b', '1.1-7b-it'] {type:\"string\"}\n",
        "weights_dir = kagglehub.model_download(f\"google/gemma/Flax/{VARIANT}\")\n",
        "\n",
        "checkpoint_path = os.path.join(weights_dir, VARIANT)\n",
        "vocab_path = os.path.join(weights_dir, \"tokenizer.model\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dn3BirzvHglS"
      },
      "source": [
        "## 使用 Gemma 與 OneTwo\n",
        "\n",
        "### 安裝 OneTwo"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "77GlsVYpHoFN",
        "outputId": "d323e810-d151-4154-bdb8-faf4fadec367"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Collecting git+https://github.com/google-deepmind/onetwo\n",
            "  Cloning https://github.com/google-deepmind/onetwo to /tmp/pip-req-build-zjyeelh4\n",
            "  Running command git clone --filter=blob:none --quiet https://github.com/google-deepmind/onetwo /tmp/pip-req-build-zjyeelh4\n",
            "  Resolved https://github.com/google-deepmind/onetwo to commit 43c3b67e4b479e49e31baefb304d1487b4c893bb\n",
            "  Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
            "  Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n",
            "  Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
            "Collecting gemma@ git+https://github.com/google-deepmind/gemma.git (from onetwo==0.1.0)\n",
            "  Cloning https://github.com/google-deepmind/gemma.git to /tmp/pip-install-8hrdcipz/gemma_c35bc1f9a46943db8dce9b89f9a3baed\n",
            "  Running command git clone --filter=blob:none --quiet https://github.com/google-deepmind/gemma.git /tmp/pip-install-8hrdcipz/gemma_c35bc1f9a46943db8dce9b89f9a3baed\n",
            "  Resolved https://github.com/google-deepmind/gemma.git to commit a24194737dcb54b7392091e9ba772aea1cb68ffb\n",
            "  Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
            "  Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n",
            "  Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
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          ]
        }
      ],
      "source": [
        "!pip install git+https://github.com/google-deepmind/onetwo"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "blfwJXpeHxPS"
      },
      "source": [
        "### 匯入"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "50CME0WvHy55"
      },
      "outputs": [],
      "source": [
        "from onetwo import ot\n",
        "from onetwo.builtins import llm"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "C-8S-I2fIBIn"
      },
      "source": [
        "### 註冊 Gemma 作為後端"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Ig8q7ZcMHSjC"
      },
      "outputs": [],
      "source": [
        "from onetwo.backends import gemma_local\n",
        "\n",
        "backend = gemma_local.Gemma(checkpoint_path=checkpoint_path, vocab_path=vocab_path)\n",
        "backend.register()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "I-hmUfo2IKyH"
      },
      "source": [
        "### 執行推論與 `llm.generate_text()`"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PIAy8pXHIRlK",
        "outputId": "8199bcda-686d-44f6-db87-b0f9d89509b2"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            " the cities of <strong>Duisburg</strong>, <strong>Cologne</strong> and <strong>Bonn</strong>.\n",
            "\n",
            "<strong>Duisburg</strong> is a city\n"
          ]
        }
      ],
      "source": [
        "# Ask the model to continue the given text.\n",
        "e = llm.generate_text(\n",
        "    \"Three not so well known cities in Gemany are\", max_tokens=30, temperature=0.3\n",
        ")\n",
        "print(ot.run(e))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "8CFyZX5BI7Cf"
      },
      "outputs": [],
      "source": []
    }
  ],
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