AutoGPTQ_transformers.ipynb

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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/yotamnahum/Mamram-Language-Modelling-Workshop/blob/main/AutoGPTQ_transformers.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# transformers meets AutoGPTQ library for lighter and faster quantized inference of LLMs"
      ],
      "metadata": {
        "id": "H_D9kG_efts3"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Last year the [GPTQ: Accurate Post-Training Quantization for Generative Pre-trained Transformers](https://arxiv.org/abs/2210.17323) has been published by Frantar et al. The paper details an algorithm to compress any transformer-based language model in few bits with a tiny performance degradation.\n",
        "\n",
        "We now support loading models that are quantized with GPTQ algorithm in 🤗 transformers thanks to the [`auto-gptq`](https://github.com/PanQiWei/AutoGPTQ.git) library that is used as backend.\n",
        "\n",
        "Let's check in this notebook the different options (quantize a model, push a quantized model on the 🤗 Hub, load an already quantized model from the Hub, etc.) that are offered in this integration!"
      ],
      "metadata": {
        "id": "Wwsg6nCwoThm"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Load required libraries\n",
        "\n",
        "Let us first load the required libraries that are 🤗 transformers, optimum and auto-gptq library."
      ],
      "metadata": {
        "id": "rzdrF6Xp8-Bw"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "cY3RD1iKfKPD",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "5b6b5b06-be4b-42f0-8bf0-cf93f4b72871"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m7.5/7.5 MB\u001b[0m \u001b[31m24.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m85.6/85.6 kB\u001b[0m \u001b[31m11.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m251.2/251.2 kB\u001b[0m \u001b[31m29.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m380.6/380.6 kB\u001b[0m \u001b[31m40.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m268.8/268.8 kB\u001b[0m \u001b[31m23.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m7.8/7.8 MB\u001b[0m \u001b[31m56.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.3/1.3 MB\u001b[0m \u001b[31m63.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m46.0/46.0 kB\u001b[0m \u001b[31m5.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m519.3/519.3 kB\u001b[0m \u001b[31m27.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.3/1.3 MB\u001b[0m \u001b[31m66.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m86.8/86.8 kB\u001b[0m \u001b[31m10.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m115.3/115.3 kB\u001b[0m \u001b[31m12.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m194.1/194.1 kB\u001b[0m \u001b[31m22.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m134.8/134.8 kB\u001b[0m \u001b[31m15.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h"
          ]
        }
      ],
      "source": [
        "!pip install -q -U transformers peft accelerate optimum"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "For now, until the next release of AutoGPTQ, we will build the library from source!"
      ],
      "metadata": {
        "id": "l7ihqJqv6rDF"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install auto-gptq --extra-index-url https://huggingface.github.io/autogptq-index/whl/cu117/"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pDU1jZvWp7s5",
        "outputId": "c0540556-9e45-4608-8613-34b64e7e381f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting rouge\n",
            "  Downloading rouge-1.0.1-py3-none-any.whl (13 kB)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.10/dist-packages (from rouge) (1.16.0)\n",
            "Installing collected packages: rouge\n",
            "Successfully installed rouge-1.0.1\n",
            "Looking in indexes: https://huggingface.github.io/autogptq-index/whl/cu117/\n",
            "Collecting auto-gptq\n",
            "  Using cached https://huggingface.github.io/autogptq-index/whl/cu117/auto-gptq/auto_gptq-0.4.1%2Bcu117-cp310-cp310-linux_x86_64.whl (1.6 MB)\n",
            "Requirement already satisfied: accelerate>=0.19.0 in /usr/local/lib/python3.10/dist-packages (from auto-gptq) (0.22.0)\n",
            "Requirement already satisfied: datasets in /usr/local/lib/python3.10/dist-packages (from auto-gptq) (2.14.4)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.10/dist-packages (from auto-gptq) (1.23.5)\n",
            "Requirement already satisfied: rouge in /usr/local/lib/python3.10/dist-packages (from auto-gptq) (1.0.1)\n",
            "Requirement already satisfied: torch>=1.13.0 in /usr/local/lib/python3.10/dist-packages (from auto-gptq) (2.0.1+cu118)\n",
            "Requirement already satisfied: safetensors in /usr/local/lib/python3.10/dist-packages (from auto-gptq) (0.3.2)\n",
            "Requirement already satisfied: transformers>=4.31.0 in /usr/local/lib/python3.10/dist-packages (from auto-gptq) (4.32.0)\n",
            "Requirement already satisfied: peft in /usr/local/lib/python3.10/dist-packages (from auto-gptq) (0.5.0)\n",
            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from accelerate>=0.19.0->auto-gptq) (23.1)\n",
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            "Requirement already satisfied: jinja2 in /usr/local/lib/python3.10/dist-packages (from torch>=1.13.0->auto-gptq) (3.1.2)\n",
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            "Requirement already satisfied: huggingface-hub<1.0,>=0.15.1 in /usr/local/lib/python3.10/dist-packages (from transformers>=4.31.0->auto-gptq) (0.16.4)\n",
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            "Requirement already satisfied: tokenizers!=0.11.3,<0.14,>=0.11.1 in /usr/local/lib/python3.10/dist-packages (from transformers>=4.31.0->auto-gptq) (0.13.3)\n",
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            "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from jinja2->torch>=1.13.0->auto-gptq) (2.1.3)\n",
            "Requirement already satisfied: python-dateutil>=2.8.1 in /usr/local/lib/python3.10/dist-packages (from pandas->datasets->auto-gptq) (2.8.2)\n",
            "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas->datasets->auto-gptq) (2023.3)\n",
            "Requirement already satisfied: mpmath>=0.19 in /usr/local/lib/python3.10/dist-packages (from sympy->torch>=1.13.0->auto-gptq) (1.3.0)\n",
            "Installing collected packages: auto-gptq\n",
            "Successfully installed auto-gptq-0.4.1+cu117\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Quantize transformers model using auto-gptq, 🤗 transformers and optimum\n",
        "\n",
        "There are two different scenarios you might be interested in using this integration.\n",
        "\n",
        "1- Quantize a language model from scratch.\n",
        "\n",
        "2- Load a model that has been already quantized from 🤗 Hub\n",
        "\n",
        "\n",
        "The GPTQ algorithm requires to calibrate the quantized weights of the model by doing inference on the quantized model. The detailed quantization algorithm is described in [the original paper](https://arxiv.org/pdf/2210.17323.pdf).\n",
        "\n",
        "For quantizing a model using auto-gptq, we need to pass a dataset to the quantizer. This can be achieved either by passing a supported default dataset among `['wikitext2','c4','c4-new','ptb','ptb-new']` or a list of strings that will be used as a dataset."
      ],
      "metadata": {
        "id": "L2ZTO3OSoL5T"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Quantize a model by passing a supported dataset\n"
      ],
      "metadata": {
        "id": "4_DJf_pA-lnq"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "\n",
        "In the example below, let us try to quantize the model in 4-bit precision using the `\"c4\"` dataset. Supported precisions are `[2, 4, 6, 8]`.\n",
        "\n",
        "Note that this cell will take more than 3 minutes to be completed. If you want to check how to quantize the model by passing a custom dataset, check out the next section."
      ],
      "metadata": {
        "id": "-H2019RkoiM-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import AutoModelForCausalLM, AutoTokenizer, GPTQConfig\n",
        "import torch\n",
        "\n",
        "model_id = \"facebook/opt-125m\"\n",
        "\n",
        "quantization_config = GPTQConfig(\n",
        "     bits=4,\n",
        "     group_size=128,\n",
        "     dataset=\"c4\",\n",
        "     desc_act=False,\n",
        ")\n",
        "\n",
        "tokenizer = AutoTokenizer.from_pretrained(model_id)\n",
        "quant_model = AutoModelForCausalLM.from_pretrained(model_id, quantization_config=quantization_config, device_map='auto')"
      ],
      "metadata": {
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      "execution_count": null,
      "outputs": [
        {
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            "Repo card metadata block was not found. Setting CardData to empty.\n",
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        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating train split: 0 examples [00:00, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "c9ab1ea09ddc45db9c649bb0db4d4f4b"
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        {
          "output_type": "display_data",
          "data": {
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          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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          "metadata": {}
        },
        {
          "output_type": "display_data",
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              "version_major": 2,
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              "model_id": "17a4f65808b94b7cb637a4ddddd227e5"
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        },
        {
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        {
          "output_type": "display_data",
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              "Quantizing layers inside the block:   0%|          | 0/6 [00:00<?, ?it/s]"
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        {
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              "Quantizing layers inside the block:   0%|          | 0/6 [00:00<?, ?it/s]"
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              "version_major": 2,
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              "model_id": "077cba201cbf436dac4ff29df159bcf2"
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        {
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              "Quantizing layers inside the block:   0%|          | 0/6 [00:00<?, ?it/s]"
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    },
    {
      "cell_type": "markdown",
      "source": [
        "You can make sure the model has been correctly quantized by checking the attributes of the linear layers, they should contain `qweight` and `qzeros` attributes that should be in `torch.int32` dtype."
      ],
      "metadata": {
        "id": "1NP3sd8X-QEo"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "quant_model.model.decoder.layers[0].self_attn.q_proj.__dict__"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sJRv3bGyZtcB",
        "outputId": "58b886e8-2449-4389-d31b-ca6be9f77054"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'training': True,\n",
              " '_parameters': OrderedDict(),\n",
              " '_buffers': OrderedDict([('qweight',\n",
              "               tensor([[ 1711760090, -1248295259, -2025411892,  ..., -1486452502,\n",
              "                         2019142072, -1735820810],\n",
              "                       [-2000132747,  -578262345,  1484081337,  ..., -1230600537,\n",
              "                        -2019252040, -2023311003],\n",
              "                       [ -710293851, -1153090188,  1431922298,  ..., -1768449094,\n",
              "                         2042194587, -2004125258],\n",
              "                       ...,\n",
              "                       [-1183500136, -1494510422, -1772782904,  ..., -1518753378,\n",
              "                         -411710600,  -392845654],\n",
              "                       [-1990626701,  1469278281,  1469864108,  ...,  1740208533,\n",
              "                        -1732560507, -1738077576],\n",
              "                       [ 2015914598,  2040232821,  2005572185,  ..., -1463179655,\n",
              "                        -1450400136, -2024523156]], device='cuda:0', dtype=torch.int32)),\n",
              "              ('qzeros',\n",
              "               tensor([[2004318071, 2004318071, 2004318071, 2004318071, 2004318071, 2004318071,\n",
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              "                        2004318071, 2004318071, 2004318071, 2004318071, 2004318071, 2004318071,\n",
              "                        2004318071, 2004318071, 2004318071, 2004318071, 2004318071, 2004318071,\n",
              "                        2004318071, 2004318071, 2004318071, 2004318071, 2004318071, 2004318071,\n",
              "                        2004318071, 2004318071, 2004318071, 2004318071, 2004318071, 2004318071,\n",
              "                        2004318071, 2004318071, 2004318071, 2004318071, 2004318071, 2004318071,\n",
              "                        2004318071, 2004318071, 2004318071, 2004318071, 2004318071, 2004318071,\n",
              "                        2004318071, 2004318071, 2004318071, 2004318071, 2004318071, 2004318071,\n",
              "                        2004318071, 2004318071, 2004318071, 2004318071, 2004318071, 2004318071]],\n",
              "                      device='cuda:0', dtype=torch.int32)),\n",
              "              ('scales',\n",
              "               tensor([[0.0472, 0.0420, 0.0370,  ..., 0.0180, 0.0131, 0.0191],\n",
              "                       [0.0506, 0.0483, 0.0370,  ..., 0.0103, 0.0141, 0.0115],\n",
              "                       [0.0418, 0.0524, 0.0418,  ..., 0.0190, 0.0142, 0.0217],\n",
              "                       [0.0481, 0.0362, 0.0438,  ..., 0.0124, 0.0149, 0.0118],\n",
              "                       [0.0437, 0.0489, 0.0376,  ..., 0.0199, 0.0162, 0.0143],\n",
              "                       [0.0474, 0.0491, 0.0381,  ..., 0.0144, 0.0144, 0.0163]],\n",
              "                      device='cuda:0', dtype=torch.float16)),\n",
              "              ('g_idx',\n",
              "               tensor([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
              "                       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
              "                       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
              "                       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
              "                       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
              "                       0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "                       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "                       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "                       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "                       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "                       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2,\n",
              "                       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
              "                       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
              "                       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
              "                       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
              "                       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
              "                       3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n",
              "                       3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n",
              "                       3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n",
              "                       3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n",
              "                       3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n",
              "                       3, 3, 3, 3, 3, 3, 3, 3, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4,\n",
              "                       4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4,\n",
              "                       4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4,\n",
              "                       4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4,\n",
              "                       4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4,\n",
              "                       4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 5, 5, 5, 5, 5, 5, 5, 5,\n",
              "                       5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5,\n",
              "                       5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5,\n",
              "                       5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5,\n",
              "                       5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5,\n",
              "                       5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5],\n",
              "                      device='cuda:0', dtype=torch.int32)),\n",
              "              ('bias',\n",
              "               tensor([-1.4294e-01, -3.6646e-01, -2.9221e-02, -2.8613e-01, -3.0615e-01,\n",
              "                       -2.6880e-01,  7.9773e-02,  8.0261e-02, -1.7957e-01, -5.2887e-02,\n",
              "                        1.4319e-01, -1.8982e-01,  3.6694e-01, -6.1554e-02,  3.2617e-01,\n",
              "                       -2.8857e-01,  6.8481e-02, -2.7271e-01,  1.5649e-01, -2.1838e-01,\n",
              "                       -2.1082e-01, -1.3550e-01, -2.5049e-01, -3.3594e-01,  2.9150e-01,\n",
              "                       -7.9773e-02,  1.3440e-01,  8.8196e-02,  3.8110e-01,  1.4294e-01,\n",
              "                       -7.5989e-02, -1.9873e-01, -2.1973e-02,  1.3397e-02,  1.1505e-01,\n",
              "                        2.5146e-01,  2.8589e-01,  2.9956e-01, -2.2217e-01,  1.3135e-01,\n",
              "                       -2.5269e-01, -2.0190e-01,  8.5083e-02, -5.4901e-02,  2.3267e-01,\n",
              "                       -1.3831e-01, -5.8624e-02, -1.1206e-01,  5.0488e-01,  1.4526e-01,\n",
              "                        5.2295e-01,  2.3157e-01, -1.0779e-01,  2.0374e-01,  1.8323e-01,\n",
              "                       -3.3862e-01,  1.9885e-01, -1.6028e-01, -6.7139e-02,  3.3740e-01,\n",
              "                        1.3025e-01,  1.1078e-01,  3.5034e-01, -2.9590e-01,  5.2539e-01,\n",
              "                        2.8003e-01, -3.3691e-02,  3.4546e-01,  1.3342e-01,  1.0510e-01,\n",
              "                       -1.2433e-01,  2.7686e-01, -4.4098e-02,  6.0921e-03,  1.0352e-01,\n",
              "                        5.0635e-01, -1.7041e-01,  5.0391e-01, -3.5010e-01,  5.4590e-01,\n",
              "                       -1.5039e-01, -1.8030e-01, -1.4600e-01,  3.5498e-01,  1.1182e-01,\n",
              "                        2.5659e-01, -3.5352e-01, -3.8379e-01,  5.3174e-01, -2.0215e-01,\n",
              "                       -2.5928e-01, -1.6821e-01,  3.0304e-02, -2.7026e-01, -4.4312e-01,\n",
              "                       -2.2449e-01,  1.2000e-01,  6.0059e-02,  2.6953e-01,  3.9722e-01,\n",
              "                       -5.0195e-01, -4.9927e-02,  6.1920e-02, -1.1469e-01, -1.8988e-03,\n",
              "                       -6.6650e-02, -1.6272e-01,  3.7134e-01, -3.2568e-01,  7.4196e-03,\n",
              "                        6.5079e-03, -3.2318e-02, -2.0874e-02, -3.4790e-01, -4.3579e-02,\n",
              "                        3.3618e-01, -1.6418e-01, -4.5441e-02,  2.7246e-01,  1.1267e-01,\n",
              "                       -3.1738e-01, -9.1919e-02, -3.3008e-01,  4.5532e-02,  2.4094e-02,\n",
              "                       -2.0227e-01, -1.8384e-01,  9.4910e-02, -5.8167e-02, -2.6343e-01,\n",
              "                       -1.4722e-01,  3.2153e-01, -8.6609e-02, -3.0957e-01,  2.8174e-01,\n",
              "                       -2.3413e-01,  1.7090e-01,  2.5317e-01,  1.2683e-01, -3.0444e-01,\n",
              "                        2.6855e-01,  3.8849e-02,  2.2925e-01,  1.9922e-01,  2.4719e-01,\n",
              "                        4.0942e-01,  1.1780e-01,  1.1475e-01, -6.1127e-02,  2.1313e-01,\n",
              "                        3.8184e-01, -5.8899e-02,  1.0278e-01,  2.3950e-01,  2.8784e-01,\n",
              "                       -3.0457e-02, -1.3049e-01, -3.7646e-01,  2.2119e-01, -1.6406e-01,\n",
              "                       -1.9495e-01,  1.7273e-01, -2.5464e-01,  3.6768e-01, -2.9102e-01,\n",
              "                       -1.5918e-01,  2.0471e-01, -3.2812e-01,  2.2131e-01, -1.1847e-01,\n",
              "                        2.0642e-01,  3.2324e-01, -4.1895e-01, -1.8042e-01, -2.6733e-01,\n",
              "                       -2.9834e-01, -2.4695e-01, -2.6465e-01,  2.0676e-02,  6.9763e-02,\n",
              "                        1.1255e-01,  3.0957e-01,  5.0293e-01,  3.1421e-01,  1.4185e-01,\n",
              "                       -2.6562e-01, -2.4817e-01, -5.6854e-02,  1.8518e-01, -2.5269e-01,\n",
              "                       -2.2961e-01, -8.8867e-02, -4.0796e-01, -1.9324e-01,  4.3262e-01,\n",
              "                       -3.6523e-01,  4.0016e-03,  1.8518e-01,  2.6416e-01,  2.4414e-01,\n",
              "                        9.4177e-02, -1.8005e-01, -1.4246e-01, -2.9395e-01,  3.0396e-01,\n",
              "                       -3.4131e-01, -4.0039e-01, -2.5732e-01, -3.2959e-01,  1.6553e-01,\n",
              "                        2.8491e-01, -2.8394e-01,  5.0342e-01,  2.0190e-01, -4.0576e-01,\n",
              "                        1.8066e-01,  2.8882e-01,  4.3945e-01,  7.6561e-03, -3.0322e-01,\n",
              "                        2.8101e-01,  1.8518e-01, -3.6682e-02,  1.5088e-01,  2.3340e-01,\n",
              "                       -4.3042e-01, -3.6475e-01, -3.6640e-03, -3.4912e-01, -3.8013e-01,\n",
              "                       -4.0436e-02,  3.5474e-01, -4.4434e-01,  5.0830e-01,  3.2373e-01,\n",
              "                        2.6807e-01,  2.6416e-01, -4.2505e-01,  2.9956e-01,  2.4902e-01,\n",
              "                        2.5269e-01,  3.7646e-01, -4.3091e-01,  3.6304e-01,  1.9238e-01,\n",
              "                        1.1406e-02, -9.9258e-03, -2.1826e-01, -4.3396e-02, -3.0566e-01,\n",
              "                       -3.7842e-01, -1.7554e-01, -5.0293e-01, -3.7109e-01,  3.6548e-01,\n",
              "                       -4.4824e-01,  3.2654e-02, -1.1391e-02,  6.0730e-02,  1.7426e-02,\n",
              "                       -1.6431e-01, -8.7219e-02,  1.8884e-01,  7.8552e-02, -4.7821e-02,\n",
              "                        9.8724e-03, -1.9165e-02, -9.9945e-03, -1.0797e-01, -4.2084e-02,\n",
              "                        3.9635e-03, -2.5732e-01,  1.3252e-02,  7.1289e-02,  1.6113e-02,\n",
              "                       -1.3382e-02, -2.1393e-02,  8.0872e-02, -2.5952e-01,  2.6932e-02,\n",
              "                       -1.4763e-02, -2.5284e-02, -2.7905e-01, -3.7659e-02, -1.9409e-02,\n",
              "                       -8.9050e-02, -9.7733e-03, -6.6223e-02,  3.5400e-02, -7.4654e-03,\n",
              "                        6.4697e-02,  1.6052e-01, -1.9073e-02, -1.9806e-02,  3.6835e-02,\n",
              "                        6.1737e-02, -1.0675e-01,  1.5793e-02,  1.3147e-01,  2.5772e-02,\n",
              "                        3.2501e-02,  3.2440e-02, -4.0619e-02,  4.0359e-03, -5.0140e-02,\n",
              "                       -7.7087e-02,  1.2772e-02,  7.0862e-02, -5.2246e-02,  5.9509e-04,\n",
              "                       -2.0569e-02, -4.2999e-02,  1.4465e-02, -2.6562e-01,  5.4359e-03,\n",
              "                       -9.1858e-02, -6.3171e-02,  1.6586e-02,  4.0649e-02,  8.2214e-02,\n",
              "                        2.0844e-02,  5.9967e-03,  4.3488e-02,  2.6047e-02, -2.3384e-03,\n",
              "                       -1.2207e-02, -2.7603e-02,  5.6250e-01, -1.6510e-02, -1.4435e-02,\n",
              "                        5.2930e-01,  2.1805e-02, -1.0262e-03, -2.6810e-02, -2.4414e-03,\n",
              "                       -2.5978e-03,  5.5615e-01, -1.9653e-02, -3.8357e-03, -1.4435e-02,\n",
              "                       -1.5656e-02, -5.8441e-02, -1.7746e-02, -2.8870e-02,  4.0314e-02,\n",
              "                        7.3671e-04,  2.7588e-02, -2.4231e-02,  1.5434e-02,  7.2289e-03,\n",
              "                       -1.5221e-02,  3.0075e-02,  1.4343e-02,  1.7609e-02, -7.1869e-03,\n",
              "                       -2.0630e-02, -3.5736e-02,  3.8574e-02,  6.7139e-04,  1.4320e-02,\n",
              "                        4.2343e-03, -3.5156e-02,  2.5558e-02,  4.6659e-04, -1.2123e-02,\n",
              "                       -2.3880e-02,  1.8906e-02,  7.2937e-03, -2.8259e-02,  1.5465e-02,\n",
              "                       -5.1074e-01,  3.4149e-02,  1.1234e-03,  1.4786e-02,  1.7639e-02,\n",
              "                        5.1849e-02, -6.3591e-03,  4.0741e-03, -2.8381e-03, -2.1534e-03,\n",
              "                       -3.2532e-02,  4.7424e-02,  1.7365e-02,  5.9853e-03,  1.7651e-01,\n",
              "                       -6.3477e-02, -3.6133e-02, -1.9006e-01,  2.5586e-01, -3.6938e-01,\n",
              "                       -3.5034e-01, -1.9250e-01, -3.6938e-01, -1.6724e-01,  3.4619e-01,\n",
              "                        2.4377e-01,  1.3721e-01,  1.8158e-02,  1.2109e-01,  3.1128e-01,\n",
              "                       -4.5228e-04,  1.8616e-01,  4.4580e-01,  2.2864e-01, -3.3661e-02,\n",
              "                       -2.1655e-01,  4.4507e-01, -1.2964e-01, -1.9812e-01, -2.3779e-01,\n",
              "                       -2.4524e-01,  2.4585e-01, -2.5439e-01,  4.7095e-01, -6.5308e-02,\n",
              "                       -3.3765e-01, -4.6045e-01, -1.1108e-01,  2.8442e-01,  1.3000e-01,\n",
              "                        5.0342e-01,  1.7236e-01,  1.1676e-01,  1.6870e-01,  3.2983e-01,\n",
              "                       -2.1204e-01,  1.1945e-01,  9.6313e-02,  1.2390e-01, -3.0811e-01,\n",
              "                        3.0981e-01, -7.5623e-02,  1.7664e-01,  3.6572e-01,  4.6582e-01,\n",
              "                        1.7114e-01, -2.4744e-01,  7.0496e-02, -3.2837e-02,  3.2568e-01,\n",
              "                        1.4648e-01, -2.9785e-01, -1.1273e-01,  1.7529e-01, -3.9429e-01,\n",
              "                       -1.6736e-01, -3.0609e-02, -4.4824e-01, -6.6284e-02,  1.2543e-02,\n",
              "                       -1.6614e-01,  2.9614e-01, -3.0859e-01, -1.7639e-01,  8.0322e-02,\n",
              "                       -3.1348e-01, -2.6514e-01, -2.7295e-01, -2.3755e-01,  1.0217e-01,\n",
              "                        8.8379e-02,  8.1177e-02,  3.8391e-02, -5.9509e-02,  3.3325e-01,\n",
              "                       -7.1594e-02, -7.3669e-02,  2.3544e-02,  4.9347e-02,  1.7932e-01,\n",
              "                        1.3123e-01,  1.7517e-01, -3.6499e-01,  2.6733e-01, -2.6245e-01,\n",
              "                       -8.0490e-03,  7.1487e-03, -2.1204e-01,  5.1123e-01, -1.5356e-01,\n",
              "                       -4.1382e-01, -1.7810e-01, -6.2866e-02,  1.1420e-01,  8.2520e-02,\n",
              "                       -3.1641e-01, -1.5335e-02, -5.0995e-02, -2.9199e-01, -1.7847e-01,\n",
              "                        4.2145e-02,  2.9663e-01, -7.2144e-02, -1.3220e-01,  3.0322e-01,\n",
              "                       -1.2683e-01, -2.8961e-02,  2.2430e-02,  7.2876e-02,  7.6599e-02,\n",
              "                       -2.7145e-02,  2.5854e-01, -1.2128e-01, -3.8867e-01, -6.0699e-02,\n",
              "                        2.7786e-02, -3.5059e-01,  6.4636e-02,  1.3660e-01,  2.9395e-01,\n",
              "                       -1.2329e-01, -8.0444e-02, -9.2163e-02, -5.5518e-01,  1.5649e-01,\n",
              "                       -5.0732e-01, -5.2539e-01,  5.5127e-01, -2.2064e-02, -6.2158e-01,\n",
              "                        5.0586e-01, -5.2490e-01,  1.0199e-01, -2.4683e-01,  5.1807e-01,\n",
              "                        3.6499e-01, -5.5615e-01,  5.6299e-01, -1.1469e-01, -5.1172e-01,\n",
              "                        1.5137e-01,  5.5811e-01, -5.7861e-01, -5.0928e-01,  3.2617e-01,\n",
              "                        2.3938e-01, -5.7666e-01,  1.6016e-01, -2.5488e-01, -3.4863e-01,\n",
              "                        2.4780e-01,  3.9337e-02, -1.6858e-01, -8.2825e-02, -3.3325e-02,\n",
              "                        5.0146e-01,  2.9688e-01,  1.3110e-01,  5.7812e-01,  2.0154e-01,\n",
              "                        8.7585e-02, -5.1416e-01, -2.4902e-01, -1.6833e-01,  5.3760e-01,\n",
              "                       -5.3857e-01,  5.6915e-02,  5.0195e-01,  1.3464e-01,  5.0586e-01,\n",
              "                        6.7383e-02,  1.7346e-01, -2.5781e-01, -5.3857e-01,  7.5455e-03,\n",
              "                       -5.0781e-01, -3.9404e-01,  3.0176e-01,  5.0977e-01, -1.2421e-01,\n",
              "                       -5.0293e-01, -1.2347e-01,  5.1416e-01, -5.3320e-01, -5.7080e-01,\n",
              "                       -3.4644e-01,  5.0781e-01, -7.8796e-02, -4.2236e-02, -2.6221e-01,\n",
              "                        1.0114e-01,  1.0971e-02, -7.5562e-02, -2.2595e-01, -1.4502e-01,\n",
              "                        3.7671e-01,  3.6938e-01,  1.5576e-01,  5.8632e-03,  1.6602e-01,\n",
              "                        2.8955e-01, -4.7217e-01, -1.1194e-01,  2.9077e-01,  2.6758e-01,\n",
              "                       -6.3293e-02,  1.1340e-01,  5.3174e-01,  4.0210e-01,  1.3965e-01,\n",
              "                        1.1505e-01,  8.7891e-02, -8.8562e-02, -4.9023e-01,  3.9380e-01,\n",
              "                        1.3611e-01,  2.3767e-01,  1.9424e-02, -5.5322e-01,  2.3218e-01,\n",
              "                       -5.2881e-01, -1.0211e-01,  2.5854e-01, -3.2520e-01,  1.6632e-02,\n",
              "                       -2.7979e-01,  2.8857e-01, -7.1289e-02, -5.2979e-01,  2.5195e-01,\n",
              "                       -3.1226e-01, -2.7466e-03,  1.2793e-01,  9.7168e-02,  6.2103e-02,\n",
              "                        9.3567e-02,  2.4023e-01,  4.6069e-01, -5.0926e-03,  2.7271e-01,\n",
              "                        1.3184e-01, -2.3901e-01, -3.5205e-01, -1.8982e-01,  3.2806e-02,\n",
              "                        1.8774e-01, -1.1591e-01, -5.9021e-02,  2.8702e-02, -3.8379e-01,\n",
              "                        1.9470e-01,  2.2986e-01,  1.3220e-01,  1.9446e-01,  3.7292e-02,\n",
              "                        1.5076e-01, -6.5308e-02, -3.5461e-02, -7.5867e-02,  1.3379e-01,\n",
              "                        1.0284e-01, -2.7740e-02, -4.9324e-03,  1.3000e-01, -9.5032e-02,\n",
              "                        2.3041e-02,  4.3884e-02,  1.4294e-01, -5.5054e-02,  6.8909e-02,\n",
              "                        5.5939e-02,  6.2408e-02, -2.1582e-01,  2.5000e-01,  5.3076e-01,\n",
              "                       -2.2791e-01, -2.5049e-01,  1.6138e-01, -9.7229e-02, -2.1667e-01,\n",
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              "                       -1.5002e-01, -8.4473e-02, -9.4681e-03,  8.1055e-02,  9.6252e-02,\n",
              "                        1.7773e-01, -8.1604e-02,  1.3806e-01, -1.8677e-01,  1.6895e-01,\n",
              "                       -3.7628e-02,  5.9662e-02, -9.2773e-02, -7.1533e-02, -1.9885e-01,\n",
              "                        5.9174e-02,  2.5000e-01, -2.3514e-02,  7.3608e-02,  2.2290e-01,\n",
              "                        6.6772e-02, -1.2500e-01, -1.3550e-01, -6.0028e-02, -1.3147e-01,\n",
              "                       -1.2756e-01,  6.3416e-02,  1.3062e-01,  1.2488e-01,  6.1584e-02,\n",
              "                       -6.7101e-03, -3.3875e-02, -1.6556e-02,  1.8738e-02, -4.7836e-03,\n",
              "                       -3.2776e-02,  1.4427e-02, -5.1221e-01, -4.6661e-02,  3.5343e-03,\n",
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              "                        1.5762e-02, -8.2779e-03, -7.0190e-02,  1.4915e-02, -1.6663e-02,\n",
              "                        2.6138e-02, -8.9645e-03,  1.2150e-03, -1.5222e-01, -2.7023e-02,\n",
              "                       -2.4277e-02, -8.3237e-03,  2.5192e-02, -7.4120e-03, -7.1487e-03,\n",
              "                       -3.6377e-01,  7.0496e-02, -2.0630e-01,  2.1744e-02,  2.2049e-02,\n",
              "                       -1.7487e-02, -5.1910e-02,  3.1799e-02,  1.8585e-02,  2.3880e-02,\n",
              "                       -1.8433e-02,  9.0866e-03,  1.8753e-02,  1.7273e-02, -4.1580e-03,\n",
              "                        1.5976e-02, -2.8519e-02,  4.3030e-03, -2.9312e-02,  3.5217e-02,\n",
              "                        4.0771e-02, -1.8188e-01,  9.0103e-03,  5.0586e-01,  2.0020e-02,\n",
              "                        1.9806e-02,  1.6861e-02,  2.5436e-02,  1.7548e-02, -8.7357e-03,\n",
              "                       -1.6785e-02,  1.8906e-02, -1.3695e-02], device='cuda:0',\n",
              "                      dtype=torch.float16))]),\n",
              " '_non_persistent_buffers_set': set(),\n",
              " '_backward_pre_hooks': OrderedDict(),\n",
              " '_backward_hooks': OrderedDict(),\n",
              " '_is_full_backward_hook': None,\n",
              " '_forward_hooks': OrderedDict(),\n",
              " '_forward_hooks_with_kwargs': OrderedDict(),\n",
              " '_forward_pre_hooks': OrderedDict(),\n",
              " '_forward_pre_hooks_with_kwargs': OrderedDict(),\n",
              " '_state_dict_hooks': OrderedDict(),\n",
              " '_state_dict_pre_hooks': OrderedDict(),\n",
              " '_load_state_dict_pre_hooks': OrderedDict(),\n",
              " '_load_state_dict_post_hooks': OrderedDict(),\n",
              " '_modules': OrderedDict(),\n",
              " 'infeatures': 768,\n",
              " 'outfeatures': 768,\n",
              " 'bits': 4,\n",
              " 'group_size': 128,\n",
              " 'trainable': False,\n",
              " 'maxq': 15,\n",
              " 'device': device(type='cuda', index=0),\n",
              " '_use_act_order': False,\n",
              " 'width': 768,\n",
              " 'q4': 102183765918528}"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Now let's perform an inference on the quantized model. Use the same API as transformers!"
      ],
      "metadata": {
        "id": "ve_FkzIy-gZ5"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "tokenizer = AutoTokenizer.from_pretrained(model_id)\n",
        "\n",
        "text = \"Hello my name is\"\n",
        "inputs = tokenizer(text, return_tensors=\"pt\").to(0)\n",
        "\n",
        "out = quant_model.generate(**inputs)\n",
        "print(tokenizer.decode(out[0], skip_special_tokens=True))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CoXV8zrIuORr",
        "outputId": "44ccab95-8a4e-4d5e-dc25-a90b7bdb5f4f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/transformers/generation/utils.py:1254: UserWarning: Using the model-agnostic default `max_length` (=20) to control thegeneration length. We recommend setting `max_new_tokens` to control the maximum length of the generation.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Hello my name is Kari and I am a student at the University of California, San Diego\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Quantize a model by passing a custom dataset"
      ],
      "metadata": {
        "id": "RhCaFfJ5-k1l"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "You can also quantize a model by passing a custom dataset, for that you can provide a list of strings to the quantization config. A good number of sample to pass is 128. If you do not pass enough data, the performance of the model will suffer."
      ],
      "metadata": {
        "id": "dN-hRO_vtJTB"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import AutoModelForCausalLM, GPTQConfig, AutoTokenizer\n",
        "\n",
        "model_id = \"facebook/opt-125m\"\n",
        "\n",
        "quantization_config = GPTQConfig(\n",
        "    bits=4,\n",
        "    group_size=128,\n",
        "    desc_act=False,\n",
        "    dataset=[\"auto-gptq is an easy-to-use model quantization library with user-friendly apis, based on GPTQ algorithm.\"]\n",
        ")\n",
        "\n",
        "tokenizer = AutoTokenizer.from_pretrained(model_id)\n",
        "quant_model = AutoModelForCausalLM.from_pretrained(model_id, quantization_config=quantization_config, torch_dtype=torch.float16, device_map=\"auto\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49,
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        },
        "id": "Cmll3BZntI88",
        "outputId": "ab8ea4c1-0157-42a9-cafa-39bdc7dd46e0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Quantizing model.decoder.layers blocks :   0%|          | 0/12 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
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            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Quantizing layers inside the block:   0%|          | 0/6 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Quantizing layers inside the block:   0%|          | 0/6 [00:00<?, ?it/s]"
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            "application/vnd.jupyter.widget-view+json": {
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          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Quantizing layers inside the block:   0%|          | 0/6 [00:00<?, ?it/s]"
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            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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          "metadata": {}
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        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Quantizing layers inside the block:   0%|          | 0/6 [00:00<?, ?it/s]"
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            "application/vnd.jupyter.widget-view+json": {
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          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Quantizing layers inside the block:   0%|          | 0/6 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
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              "version_minor": 0,
              "model_id": "dd4d924ddd714bf193b6cc199e2c859e"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Quantizing layers inside the block:   0%|          | 0/6 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "dd68838c461046e8818d86e8fe7e2f58"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Quantizing layers inside the block:   0%|          | 0/6 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "682a8ce4d46c475fbad1028ae585cefd"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Quantizing layers inside the block:   0%|          | 0/6 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "81928d041e8443549350d214f2dfc9af"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Quantizing layers inside the block:   0%|          | 0/6 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "1047b8265c614ff7aa92017d44ca4a45"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Quantizing layers inside the block:   0%|          | 0/6 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "f031b7e3039e42b1a21e232d79ae6abf"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Quantizing layers inside the block:   0%|          | 0/6 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "aa29073f5a74427483398eeaaa6d4604"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Quantizing layers inside the block:   0%|          | 0/6 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "d71a0c5cf79c4740a6a4060f0cf72082"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "As you can see from the generation below, the performance seems to be slightly worse than the model quantized using the `c4` dataset."
      ],
      "metadata": {
        "id": "BAWDL4Vr-8T2"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "text = \"My name is\"\n",
        "inputs = tokenizer(text, return_tensors=\"pt\").to(0)\n",
        "\n",
        "out = quant_model.generate(**inputs)\n",
        "print(tokenizer.decode(out[0], skip_special_tokens=True))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "N6yeN0zcxUGg",
        "outputId": "175ba704-c55d-44f1-e8df-34b52aaf7318"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "My name is a bit of a bit of a bit of a bit of a bit of a\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Share quantized models on 🤗 Hub"
      ],
      "metadata": {
        "id": "QRQvt-zr_Neh"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "After quantizing the model, it can be used out-of-the-box for inference or you can push the quantized weights on the 🤗 Hub to share your quantized model with the community"
      ],
      "metadata": {
        "id": "iI5DICq-rQ8o"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from huggingface_hub import notebook_login\n",
        "\n",
        "notebook_login()"
      ],
      "metadata": {
        "id": "kDMHIKQQvhsy",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 331,
          "referenced_widgets": [
            "9fa597ccbe6e45f4b9f785b73f705be4",
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            "b60f341f5bb948c5be2d125930230538"
          ]
        },
        "outputId": "443f1034-5819-4ee8-c6d6-cf9309031cc4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "VBox(children=(HTML(value='<center> <img\\nsrc=https://huggingface.co/front/assets/huggingface_logo-noborder.sv…"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "9fa597ccbe6e45f4b9f785b73f705be4"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "quant_model.push_to_hub(\"opt-125m-gptq-4bit\")\n",
        "tokenizer.push_to_hub(\"opt-125m-gptq-4bit\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 84,
          "referenced_widgets": [
            "226e678a63dc4177a7afc3f52d090809",
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            "e35352cd3a284782b13b52cb97a70107"
          ]
        },
        "id": "MEWp3ZdLrn6E",
        "outputId": "63501693-392e-4045-80ee-8d93af4bf5d5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "pytorch_model.bin:   0%|          | 0.00/125M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "226e678a63dc4177a7afc3f52d090809"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "CommitInfo(commit_url='https://huggingface.co/ybelkada/opt-125m-gptq-4bit/commit/0389ccfb07e2e474186014d68e90ea70e193264f', commit_message='Upload tokenizer', commit_description='', oid='0389ccfb07e2e474186014d68e90ea70e193264f', pr_url=None, pr_revision=None, pr_num=None)"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Load quantized models from the 🤗 Hub"
      ],
      "metadata": {
        "id": "P4SEJSqPgRpR"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "You can load models that have been quantized using the auto-gptq library out of the box from the 🤗 Hub directly using `from_pretrained` method.\n",
        "\n",
        "Make sure that the model on the Hub have an attribute `quantization_config` on the model config file, with the field `quant_method` set to `\"gptq\"` as you can see below.\n",
        "\n",
        "\n",
        "<img src=\"https://huggingface.co/ybelkada/scratch-repo/resolve/main/images/gptq-example.png\" width=\"300\"/>\n",
        "\n",
        "Most used quantized models can be found under [TheBloke](https://huggingface.co/TheBloke) namespace that contains most used models converted with auto-gptq library. The integration should work with most of these models out of the box (to confirm and test).\n",
        "\n",
        "<img src=\"https://huggingface.co/ybelkada/scratch-repo/resolve/main/images/thebloke-example.png\" width=\"800\"/>\n"
      ],
      "metadata": {
        "id": "k10HGjhnmbU_"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Below we will load a llama 7b quantized in 4bit."
      ],
      "metadata": {
        "id": "VVSMJL7uolks"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import AutoTokenizer, AutoModelForCausalLM\n",
        "\n",
        "model_id = \"TheBloke/Llama-2-7b-Chat-GPTQ\"\n",
        "model = AutoModelForCausalLM.from_pretrained(model_id, device_map=\"auto\")\n",
        "tokenizer = AutoTokenizer.from_pretrained(model_id)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 241,
          "referenced_widgets": [
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        "id": "yK0JlLm5gPix",
        "outputId": "ce8abf12-0251-495b-de06-7142bd85d5b0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)lve/main/config.json:   0%|          | 0.00/789 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "4762f9cf4fd14c93b6471fc44ef09589"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading model.safetensors:   0%|          | 0.00/3.90G [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "90813bc9eb684754a7c44cb4aae7d270"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)neration_config.json:   0%|          | 0.00/137 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "a4851468c59a4e30b0642a0e3f8a3d56"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)okenizer_config.json:   0%|          | 0.00/727 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "d0f7e2ea6c9c40d7a261b8d697de1cd4"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading tokenizer.model:   0%|          | 0.00/500k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "bdcd054a1a3d4c86a97b67525e293e3c"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)/main/tokenizer.json:   0%|          | 0.00/1.84M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "2ac6cd594abb4271aa30289036dff85b"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)cial_tokens_map.json:   0%|          | 0.00/411 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "ad50ff4ff26c461b9de673576ddc23ac"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Once tokenizer and model has been loaded, let's generate some text. Before that, we can inspect the model to make sure it has loaded a quantized model"
      ],
      "metadata": {
        "id": "64Q1kLX5g_Et"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(model)"
      ],
      "metadata": {
        "id": "bOigq7eFl6vq",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "41d67c4d-4b23-489d-a056-ebbbd6bfe24e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "LlamaForCausalLM(\n",
            "  (model): LlamaModel(\n",
            "    (embed_tokens): Embedding(32000, 4096, padding_idx=0)\n",
            "    (layers): ModuleList(\n",
            "      (0-31): 32 x LlamaDecoderLayer(\n",
            "        (self_attn): LlamaAttention(\n",
            "          (rotary_emb): LlamaRotaryEmbedding()\n",
            "          (k_proj): QuantLinear()\n",
            "          (o_proj): QuantLinear()\n",
            "          (q_proj): QuantLinear()\n",
            "          (v_proj): QuantLinear()\n",
            "        )\n",
            "        (mlp): LlamaMLP(\n",
            "          (act_fn): SiLUActivation()\n",
            "          (down_proj): QuantLinear()\n",
            "          (gate_proj): QuantLinear()\n",
            "          (up_proj): QuantLinear()\n",
            "        )\n",
            "        (input_layernorm): LlamaRMSNorm()\n",
            "        (post_attention_layernorm): LlamaRMSNorm()\n",
            "      )\n",
            "    )\n",
            "    (norm): LlamaRMSNorm()\n",
            "  )\n",
            "  (lm_head): Linear(in_features=4096, out_features=32000, bias=False)\n",
            ")\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "As you can see, linear layers have been modified to `QuantLinear` modules from auto-gptq library."
      ],
      "metadata": {
        "id": "dWx-UOo0l8A7"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Furthermore, we can see that from the quantization config that we are using exllama kernel (`disable_exllama = False`). Note that it only works with 4-bit model."
      ],
      "metadata": {
        "id": "U7zYDr0AbHwe"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model.config.quantization_config.to_dict()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Tg855O9EbFKI",
        "outputId": "507bbf28-8f06-4396-eca1-7ab1cdf869d9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'quant_method': <QuantizationMethod.GPTQ: 'gptq'>,\n",
              " 'bits': 4,\n",
              " 'tokenizer': None,\n",
              " 'dataset': None,\n",
              " 'group_size': 128,\n",
              " 'damp_percent': 0.01,\n",
              " 'desc_act': False,\n",
              " 'sym': True,\n",
              " 'true_sequential': True,\n",
              " 'use_cuda_fp16': False,\n",
              " 'model_seqlen': None,\n",
              " 'block_name_to_quantize': None,\n",
              " 'module_name_preceding_first_block': None,\n",
              " 'batch_size': 1,\n",
              " 'pad_token_id': None,\n",
              " 'disable_exllama': False}"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "text = \"Hello my name is\"\n",
        "inputs = tokenizer(text, return_tensors=\"pt\").to(0)\n",
        "\n",
        "out = model.generate(**inputs, max_new_tokens=50)\n",
        "print(tokenizer.decode(out[0], skip_special_tokens=True))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bFYKL5RzmGI1",
        "outputId": "5ac2b062-c565-4710-abbf-1fdfe95705e8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Hello my name is Sarah and I am a 3rd year PhD student in the Department of Computer Science at the University of Cambridge. My research focuses on developing machine learning algorithms for medical image analysis, with a particular interest in brain imaging. I am super\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Train quantized model using 🤗 PEFT"
      ],
      "metadata": {
        "id": "td0bmYW_i_PB"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Let's train the `llama-2` model using PEFT library from Hugging Face 🤗. We disable the exllama kernel because training with exllama kernel is unstable. To do that, we pass a `GPTQConfig` object with `disable_exllama=True`. This will overwrite the value stored in the config of the model."
      ],
      "metadata": {
        "id": "l-EwnZd0jpwB"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from peft import prepare_model_for_kbit_training\n",
        "\n",
        "model_id = \"TheBloke/Llama-2-7b-Chat-GPTQ\"\n",
        "quantization_config_loading = GPTQConfig(bits=4, disable_exllama=True)\n",
        "model = AutoModelForCausalLM.from_pretrained(model_id,quantization_config=quantization_config_loading, device_map=\"auto\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RkU58PaRjMXC",
        "outputId": "fd441b58-61e2-4610-c65d-14fa93021b24"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "You passed `quantization_config` to `from_pretrained` but the model you're loading already has a `quantization_config` attribute and has already quantized weights. However, loading attributes (e.g. disable_exllama, use_cuda_fp16) will be overwritten with the one you passed to `from_pretrained`. The rest will be ignored.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.config.quantization_config.to_dict()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dFLHUonOghug",
        "outputId": "40292200-3554-4bde-d27d-1ba8efaf557b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'quant_method': <QuantizationMethod.GPTQ: 'gptq'>,\n",
              " 'bits': 4,\n",
              " 'tokenizer': None,\n",
              " 'dataset': None,\n",
              " 'group_size': 128,\n",
              " 'damp_percent': 0.01,\n",
              " 'desc_act': False,\n",
              " 'sym': True,\n",
              " 'true_sequential': True,\n",
              " 'use_cuda_fp16': False,\n",
              " 'model_seqlen': None,\n",
              " 'block_name_to_quantize': None,\n",
              " 'module_name_preceding_first_block': None,\n",
              " 'batch_size': 1,\n",
              " 'pad_token_id': None,\n",
              " 'disable_exllama': True}"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "First, we have to apply some preprocessing to the model to prepare it for training. For that, use the `prepare_model_for_kbit_training` method from PEFT."
      ],
      "metadata": {
        "id": "a9Es3lzQgAXo"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model.gradient_checkpointing_enable()\n",
        "model = prepare_model_for_kbit_training(model)"
      ],
      "metadata": {
        "id": "Lio6QV7MgEeD"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Then, we need to convert the model into a peft model using get_peft_model."
      ],
      "metadata": {
        "id": "P5lRlaaEl0WK"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from peft import LoraConfig, get_peft_model\n",
        "config = LoraConfig(\n",
        "    r=8,\n",
        "    lora_alpha=32,\n",
        "    target_modules=[\"k_proj\",\"o_proj\",\"q_proj\",\"v_proj\"],\n",
        "    lora_dropout=0.05,\n",
        "    bias=\"none\",\n",
        "    task_type=\"CAUSAL_LM\"\n",
        ")\n",
        "\n",
        "model = get_peft_model(model, config)\n",
        "model.print_trainable_parameters()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BvbgQ8UclufV",
        "outputId": "cbc0aadc-ccb6-419c-bdec-54ddc128e822"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "trainable params: 8,388,608 || all params: 270,798,848 || trainable%: 3.097726619575575\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Finally, let's load a dataset and we can train our model."
      ],
      "metadata": {
        "id": "3sEQ0nW0m9I4"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from datasets import load_dataset\n",
        "\n",
        "data = load_dataset(\"Abirate/english_quotes\")\n",
        "data = data.map(lambda samples: tokenizer(samples[\"quote\"]), batched=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 209,
          "referenced_widgets": [
            "d99b0ff7d2ff4216b39f12721ceccad9",
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            "c1bb1700fef543dc9dcbd54bda40c502",
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            "87eecd0b7f084d038526aaa735dca8f6",
            "02f85f6d9eb146d1a6b61e0ad041bd24",
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            "2a6412c318b34ef6a1f12e4770c77a95",
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            "54c34447bb8044479dbc06fa8b206ec0",
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        },
        "id": "QOLRaTq2ox90",
        "outputId": "1b98fd06-f6e9-4ffe-9559-4264e8c093b8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading readme:   0%|          | 0.00/5.55k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "d99b0ff7d2ff4216b39f12721ceccad9"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading data files:   0%|          | 0/1 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "db08892841db4fe6be1c62747aa93fb0"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading data:   0%|          | 0.00/647k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "b90438e334e345999cbf8bf3e9fc636a"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Extracting data files:   0%|          | 0/1 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
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            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating train split: 0 examples [00:00, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "2c8eac44d2004a6893995e3a1cd82eb2"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/2508 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import Trainer, TrainingArguments, DataCollatorForLanguageModeling\n",
        "\n",
        "# needed for llama 2 tokenizer\n",
        "tokenizer.pad_token = tokenizer.eos_token\n",
        "\n",
        "trainer = Trainer(\n",
        "    model=model,\n",
        "    train_dataset=data[\"train\"],\n",
        "    args=TrainingArguments(\n",
        "        per_device_train_batch_size=1,\n",
        "        gradient_accumulation_steps=4,\n",
        "        warmup_steps=2,\n",
        "        max_steps=10,\n",
        "        learning_rate=2e-4,\n",
        "        fp16=True,\n",
        "        logging_steps=1,\n",
        "        output_dir=\"outputs\",\n",
        "        optim=\"adamw_hf\"\n",
        "    ),\n",
        "    data_collator=DataCollatorForLanguageModeling(tokenizer, mlm=False)\n",
        ")\n",
        "model.config.use_cache = False  # silence the warnings. Please re-enable for inference!\n",
        "trainer.train()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 495
        },
        "id": "vT0XjNc2jYKy",
        "outputId": "7aeac46f-00ec-4780-9d6c-2c7b972fa786"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/transformers/optimization.py:411: FutureWarning: This implementation of AdamW is deprecated and will be removed in a future version. Use the PyTorch implementation torch.optim.AdamW instead, or set `no_deprecation_warning=True` to disable this warning\n",
            "  warnings.warn(\n",
            "You're using a LlamaTokenizerFast tokenizer. Please note that with a fast tokenizer, using the `__call__` method is faster than using a method to encode the text followed by a call to the `pad` method to get a padded encoding.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='10' max='10' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [10/10 01:08, Epoch 0/1]\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>1</td>\n",
              "      <td>2.073900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2</td>\n",
              "      <td>2.358900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3</td>\n",
              "      <td>2.679900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4</td>\n",
              "      <td>2.672600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>5</td>\n",
              "      <td>2.130200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>6</td>\n",
              "      <td>1.815900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>7</td>\n",
              "      <td>2.234900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>8</td>\n",
              "      <td>2.153500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>9</td>\n",
              "      <td>2.077000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>10</td>\n",
              "      <td>2.140900</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "TrainOutput(global_step=10, training_loss=2.233762800693512, metrics={'train_runtime': 73.8366, 'train_samples_per_second': 0.542, 'train_steps_per_second': 0.135, 'total_flos': 1505696514048.0, 'train_loss': 2.233762800693512, 'epoch': 0.02})"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Exploring further"
      ],
      "metadata": {
        "id": "6WeZYWQp_6vL"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "AutoGPTQ library offers multiple advanced options for users that wants to explore features such as fused attention or triton backend.\n",
        "\n",
        "Therefore, we kindly advise users to explore [AutoGPTQ](https://github.com/PanQiWei/AutoGPTQ) library for more details."
      ],
      "metadata": {
        "id": "NT8YBGjq_74X"
      }
    }
  ]
}