Qwen_Qwen1_5_7B_Chat_GPTQ_Int.ipynb

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        "2668ba1020954e35baa50942afde044f": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "ProgressStyleModel",
          "model_module_version": "1.5.0",
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            "_model_module_version": "1.5.0",
            "_model_name": "ProgressStyleModel",
            "_view_count": null,
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            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
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        "d6ad15f560f44bf091cf48a72eacaca7": {
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          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
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            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
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            "_view_module_version": "1.2.0",
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  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/chottokun/colaboratory/blob/main/Qwen_Qwen1_5_7B_Chat_GPTQ_Int.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# import locale\n",
        "# locale.getpreferredencoding = lambda: \"UTF-8\""
      ],
      "metadata": {
        "id": "k4E-HdxFSdZr"
      },
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install -U transformers>=4.37.0\n",
        "!pip install accelerate auto-gptq optimum autoawq"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mAgkgSRoKr6T",
        "outputId": "ee0608b8-57d6-4b22-a3b4-abee809a7d77"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
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        "id": "P_J6l4HaKfaS",
        "outputId": "9db7ddfd-7b00-4361-b52a-14b27d7e24cb"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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        {
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        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
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            ],
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        {
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            ],
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            ],
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          "metadata": {}
        },
        {
          "output_type": "display_data",
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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              "model_id": "b66a321472a246a78232ce8f2d9c2193"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.\n",
            "The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n",
            "Setting `pad_token_id` to `eos_token_id`:151645 for open-end generation.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "A large language model is a sophisticated artificial intelligence system that has been trained on an immense amount of text data, typically billions or trillions of words. These models, often based on deep learning architectures like transformers, use neural networks to learn patterns and relationships in natural language. They are designed to generate human-like responses, understand context, and perform a wide range of language-related tasks such as text completion, translation, summarization, and even creative writing.\n",
            "\n",
            "By processing and analyzing vast amounts of information, large language models can demonstrate impressive capabilities in generating coherent and relevant output, making them valuable tools for various applications like chatbots, content creation, customer support, and research. However, they also require careful management and ethical considerations due to their potential to produce biased or unintended outputs.\n"
          ]
        }
      ],
      "source": [
        "from transformers import AutoModelForCausalLM, AutoTokenizer\n",
        "\n",
        "device = \"cuda\"\n",
        "\n",
        "# model_name =  \"Qwen/Qwen1.5-7B-Chat-GPTQ-Int8\"\n",
        "# model_name = \"Qwen/Qwen1.5-7B-Chat-AWQ\"\n",
        "model_name = \"Qwen/Qwen1.5-14B-Chat-AWQ\"\n",
        "\n",
        "\n",
        "model = AutoModelForCausalLM.from_pretrained(\n",
        "    model_name,\n",
        "    device_map=\"auto\"\n",
        ")\n",
        "tokenizer = AutoTokenizer.from_pretrained(model_name)\n",
        "\n",
        "prompt = \"Give me a short introduction to large language model.\"\n",
        "\n",
        "def generate_responce(prompt):\n",
        "  messages = [\n",
        "      {\"role\": \"system\", \"content\": \"You are a helpful assistant.\"},\n",
        "      {\"role\": \"user\", \"content\": prompt}\n",
        "  ]\n",
        "  text = tokenizer.apply_chat_template(\n",
        "      messages,\n",
        "      tokenize=False,\n",
        "      add_generation_prompt=True\n",
        "  )\n",
        "  model_inputs = tokenizer([text], return_tensors=\"pt\").to(device)\n",
        "\n",
        "  generated_ids = model.generate(\n",
        "      model_inputs.input_ids,\n",
        "      max_new_tokens=512\n",
        "  )\n",
        "  generated_ids = [\n",
        "      output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)\n",
        "  ]\n",
        "\n",
        "  response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]\n",
        "  return response\n",
        "\n",
        "print(generate_responce(prompt))\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "%%time\n",
        "prompt = \"日本で１番目に高い山は富士山です。それでは２番目に高い山は？\"\n",
        "print(generate_responce(prompt))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lo87YnxOND7j",
        "outputId": "c76ed57e-f562-4ea2-87fc-8ff46f246e72"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n",
            "Setting `pad_token_id` to `eos_token_id`:151645 for open-end generation.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "日本の2番目に高い山は、秩父山脈の最高峰である槍ヶ峰（やぎがけ）です。標高は2,999メートルです。\n",
            "CPU times: user 3.15 s, sys: 0 ns, total: 3.15 s\n",
            "Wall time: 3.3 s\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "%%time\n",
        "prompt = \"まどか☆マギカで一番かわいいのは\"\n",
        "print(generate_responce(prompt))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Bc3Xh-xSUGYV",
        "outputId": "79560eaa-897d-49b1-c064-77b0bf897109"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n",
            "Setting `pad_token_id` to `eos_token_id`:151645 for open-end generation.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "「まどか☆マギカ」のキャラクターはそれぞれ魅力があり、誰が最もかわいらしいかは個人差があります。一般的には、子供たちのキャラクターたちは非常に可愛く描写されています。特に、主人公・鞍马前髪（Madoka）と彼女の友人・峰岸南（Homura）が人気があります。\n",
            "\n",
            "- 鞍馬前髪 (Madoka)：物語の中心人物であり、純粋で優しい性格で、子供たちの中でも最も親しみやすい存在として愛されています。\n",
            "- 峰岸南 (Homura)：冷静で頑強な性格ですが、Madokaに深い思いを持ち、成長していく姿に多くのファンが惹かれます。\n",
            "- 野上幸姬 (Kaname)：美しく優雅で、また知性も兼ね備えた人物で、ファンから支持されています。\n",
            "\n",
            "ただし、アニメやマンガの好みによっては、他のキャラクターがもっとかわいく感じる人もいるかもしれません。それぞれのキャラクターが異なる魅力を持っているため、自分にとって最もかわいらしいキャラクターを見つけることが大切です。\n",
            "CPU times: user 20.4 s, sys: 22.4 ms, total: 20.4 s\n",
            "Wall time: 23.5 s\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "%%time\n",
        "prompt = (\n",
        "    \"以下の文章を１０文字でまとめてください。\\n\\n\"\n",
        "    \"『吸血鬼ドラキュラ』（きゅうけつきドラキュラ、原題：Dracula）は、1897年に出版されたアイルランド（イギリス）の小説家ブラム・ストーカーによる怪奇小説（ゴシックホラー）。複数の語り手による手紙や日記、新聞記事という形で展開される書簡体小説であり、トランシルヴァニアの貴族で吸血鬼であるドラキュラ伯爵がイギリスへと渡り災いを招くこと、また、それをエイブラハム・ヴァン・ヘルシング教授含むグループが討伐する物語が展開される。\"\n",
        "    )\n",
        "print(generate_responce(prompt))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7fieEaJTU26R",
        "outputId": "268606c7-fe77-4eee-8b8a-582114860adb"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n",
            "Setting `pad_token_id` to `eos_token_id`:151645 for open-end generation.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "吸血鬼德拉キュラ：1897年のブラム・ストーカーの怪奇小説、書簡体で吸血鬼伯爵の興亡を描く。\n",
            "CPU times: user 4.03 s, sys: 32.7 ms, total: 4.07 s\n",
            "Wall time: 4.08 s\n"
          ]
        }
      ]
    }
  ]
}