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  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# **Fine-Tuning Falcon 7B for Custom Text Generation**"
      ],
      "metadata": {
        "id": "kCVM-n4SMBGl"
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      "cell_type": "markdown",
      "source": [
        "## In this notebook, we explore the process of fine-tuning the Falcon 7B model to generate custom text prompts, specifically for generating descriptive text based on a given prompt. Here’s a step-by-step overview of the notebook:\n",
        "\n"
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      "source": [
        "# Install necessary libraries"
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            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m54.6/54.6 MB\u001b[0m \u001b[31m56.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m102.6/102.6 MB\u001b[0m \u001b[31m56.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m173.2/173.2 MB\u001b[0m \u001b[31m25.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m177.1/177.1 MB\u001b[0m \u001b[31m66.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m63.3/63.3 MB\u001b[0m \u001b[31m52.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
            "torchaudio 2.3.1+cu121 requires torch==2.3.1, but you have torch 2.0.1 which is incompatible.\n",
            "torchtext 0.18.0 requires torch>=2.3.0, but you have torch 2.0.1 which is incompatible.\n",
            "torchvision 0.18.1+cu121 requires torch==2.3.1, but you have torch 2.0.1 which is incompatible.\u001b[0m\u001b[31m\n",
            "\u001b[0m\u001b[33m  WARNING: Did not find branch or tag 'e03a9cc', assuming revision or ref.\u001b[0m\u001b[33m\n",
            "\u001b[0m  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",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m7.8/7.8 MB\u001b[0m \u001b[31m105.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Building wheel for transformers (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
            "\u001b[33m  WARNING: Did not find branch or tag '42a184f', assuming revision or ref.\u001b[0m\u001b[33m\n",
            "\u001b[0m  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",
            "  Building wheel for peft (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
            "\u001b[33m  WARNING: Did not find branch or tag 'c9fbb71', assuming revision or ref.\u001b[0m\u001b[33m\n",
            "\u001b[0m  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",
            "  Building wheel for accelerate (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m527.3/527.3 kB\u001b[0m \u001b[31m13.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m39.9/39.9 MB\u001b[0m \u001b[31m99.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
            "cudf-cu12 24.4.1 requires pyarrow<15.0.0a0,>=14.0.1, but you have pyarrow 17.0.0 which is incompatible.\n",
            "ibis-framework 8.0.0 requires pyarrow<16,>=2, but you have pyarrow 17.0.0 which is incompatible.\u001b[0m\u001b[31m\n",
            "\u001b[0m"
          ]
        }
      ],
      "source": [
        "!pip install -Uqqq pip\n",
        "\n",
        "!pip install -qqq torch==2.0.1\n",
        "!pip install -qqq -U git+https://github.com/huggingface/transformers.git@e03a9cc\n",
        "!pip install -qqq -U git+https://github.com/huggingface/peft.git@42a184f\n",
        "!pip install -qqq -U git+https://github.com/huggingface/accelerate.git@c9fbb71\n",
        "!pip install -qqq datasets==2.21.0\n",
        "!pip install -qqq loralib==0.1.1\n",
        "!pip install -qqq einops==0.6.1"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install bitsandbytes"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JtWrp2CYihy_",
        "outputId": "07846477-0add-4303-d74d-07755f78ac0a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting bitsandbytes\n",
            "  Downloading bitsandbytes-0.43.3-py3-none-manylinux_2_24_x86_64.whl.metadata (3.5 kB)\n",
            "Requirement already satisfied: torch in /usr/local/lib/python3.10/dist-packages (from bitsandbytes) (2.0.1)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.10/dist-packages (from bitsandbytes) (1.26.4)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from torch->bitsandbytes) (3.15.4)\n",
            "Requirement already satisfied: typing-extensions in /usr/local/lib/python3.10/dist-packages (from torch->bitsandbytes) (4.12.2)\n",
            "Requirement already satisfied: sympy in /usr/local/lib/python3.10/dist-packages (from torch->bitsandbytes) (1.13.1)\n",
            "Requirement already satisfied: networkx in /usr/local/lib/python3.10/dist-packages (from torch->bitsandbytes) (3.3)\n",
            "Requirement already satisfied: jinja2 in /usr/local/lib/python3.10/dist-packages (from torch->bitsandbytes) (3.1.4)\n",
            "Requirement already satisfied: nvidia-cuda-nvrtc-cu11==11.7.99 in /usr/local/lib/python3.10/dist-packages (from torch->bitsandbytes) (11.7.99)\n",
            "Requirement already satisfied: nvidia-cuda-runtime-cu11==11.7.99 in /usr/local/lib/python3.10/dist-packages (from torch->bitsandbytes) (11.7.99)\n",
            "Requirement already satisfied: nvidia-cuda-cupti-cu11==11.7.101 in /usr/local/lib/python3.10/dist-packages (from torch->bitsandbytes) (11.7.101)\n",
            "Requirement already satisfied: nvidia-cudnn-cu11==8.5.0.96 in /usr/local/lib/python3.10/dist-packages (from torch->bitsandbytes) (8.5.0.96)\n",
            "Requirement already satisfied: nvidia-cublas-cu11==11.10.3.66 in /usr/local/lib/python3.10/dist-packages (from torch->bitsandbytes) (11.10.3.66)\n",
            "Requirement already satisfied: nvidia-cufft-cu11==10.9.0.58 in /usr/local/lib/python3.10/dist-packages (from torch->bitsandbytes) (10.9.0.58)\n",
            "Requirement already satisfied: nvidia-curand-cu11==10.2.10.91 in /usr/local/lib/python3.10/dist-packages (from torch->bitsandbytes) (10.2.10.91)\n",
            "Requirement already satisfied: nvidia-cusolver-cu11==11.4.0.1 in /usr/local/lib/python3.10/dist-packages (from torch->bitsandbytes) (11.4.0.1)\n",
            "Requirement already satisfied: nvidia-cusparse-cu11==11.7.4.91 in /usr/local/lib/python3.10/dist-packages (from torch->bitsandbytes) (11.7.4.91)\n",
            "Requirement already satisfied: nvidia-nccl-cu11==2.14.3 in /usr/local/lib/python3.10/dist-packages (from torch->bitsandbytes) (2.14.3)\n",
            "Requirement already satisfied: nvidia-nvtx-cu11==11.7.91 in /usr/local/lib/python3.10/dist-packages (from torch->bitsandbytes) (11.7.91)\n",
            "Requirement already satisfied: triton==2.0.0 in /usr/local/lib/python3.10/dist-packages (from torch->bitsandbytes) (2.0.0)\n",
            "Requirement already satisfied: setuptools in /usr/local/lib/python3.10/dist-packages (from nvidia-cublas-cu11==11.10.3.66->torch->bitsandbytes) (71.0.4)\n",
            "Requirement already satisfied: wheel in /usr/local/lib/python3.10/dist-packages (from nvidia-cublas-cu11==11.10.3.66->torch->bitsandbytes) (0.44.0)\n",
            "Requirement already satisfied: cmake in /usr/local/lib/python3.10/dist-packages (from triton==2.0.0->torch->bitsandbytes) (3.30.2)\n",
            "Requirement already satisfied: lit in /usr/local/lib/python3.10/dist-packages (from triton==2.0.0->torch->bitsandbytes) (18.1.8)\n",
            "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from jinja2->torch->bitsandbytes) (2.1.5)\n",
            "Requirement already satisfied: mpmath<1.4,>=1.1.0 in /usr/local/lib/python3.10/dist-packages (from sympy->torch->bitsandbytes) (1.3.0)\n",
            "Downloading bitsandbytes-0.43.3-py3-none-manylinux_2_24_x86_64.whl (137.5 MB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m137.5/137.5 MB\u001b[0m \u001b[31m58.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hInstalling collected packages: bitsandbytes\n",
            "Successfully installed bitsandbytes-0.43.3\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [],
      "metadata": {
        "id": "LXu0GMXfm-M0"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "pip cache purge\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qvG-qtNVi7Bt",
        "outputId": "ac0f58eb-6477-4afd-ac81-3445868bacd4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[33mWARNING: No matching packages\u001b[0m\u001b[33m\n",
            "\u001b[0mFiles removed: 0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Environment Setup"
      ],
      "metadata": {
        "id": "z10m_xIpML3P"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import json\n",
        "import os\n",
        "from pprint import pprint\n",
        "import bitsandbytes as bnb\n",
        "import torch\n",
        "import torch.nn as nn\n",
        "import transformers\n",
        "from datasets import load_dataset\n",
        "from huggingface_hub import notebook_login\n",
        "from peft import (\n",
        "    LoraConfig,\n",
        "    PeftConfig,\n",
        "    PeftModel,\n",
        "    get_peft_model,\n",
        "    prepare_model_for_kbit_training\n",
        ")\n",
        "from transformers import (\n",
        "    AutoConfig,\n",
        "    AutoModelForCausalLM,\n",
        "    AutoTokenizer,\n",
        "    BitsAndBytesConfig\n",
        ")\n",
        "\n",
        "os.environ[\"CUDA_VISIBLE_DEVICES\"] = \"0\""
      ],
      "metadata": {
        "id": "t34I3LodazOx"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "notebook_login()"
      ],
      "metadata": {
        "id": "M6STbmqJCr49",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 145,
          "referenced_widgets": [
            "62ad21b2747943b1995116faad939da9",
            "d3681714d2024f46911eb18a133f725f",
            "c50cc2e13d0a48039ea56507ec59b157",
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            "4f50cae53f1c4c49beddd7d0cd33ff52",
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            "21c3dbae993845e0a11ffd66acadd02f",
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            "6253d87c05754a678615f516290f1bb0",
            "f468c17fddf8464ca69f94ff823cd5ea",
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            "d711257803be4c27a86c393c6f180e3a",
            "71ef95b450fc4b8ab2e7b38198f1af04",
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            "be0911625d204c51b02eb4c4cb8bc992",
            "d399ae5ca91d402cab3ae73f4c6ad795",
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            "751adb5046a546b59c60876f832092c9",
            "cb7c3c4b185742dabeb0fe6335a9aac3",
            "d860a7f4a7f24579bdafa27a176ffd2d",
            "2b6beb5fcaf44f65ab1128799221eda6",
            "ab4af512caae4b36aec7116a1f6d5ada",
            "b68b67ef452f472bbd8d95b3c0b9f286",
            "1966cbd8ef8c4b1d8fcb89f0e8433a4c",
            "773f9fcad0a34805b3a7e693904c592e",
            "87f51e9ce00c4984b87e771d6b46be66",
            "829d963c6a774d788478b0b18698bc8e"
          ]
        },
        "outputId": "7bdc53ed-5601-4ce4-efc1-1970204e1ea7"
      },
      "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": "62ad21b2747943b1995116faad939da9"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Load Falcon Model & Tokenizer\n",
        "\n"
      ],
      "metadata": {
        "id": "MMZ44oN-Ha7w"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "quantization_config = BitsAndBytesConfig(\n",
        "    load_in_4bit=True,\n",
        "    bnb_4bit_quant_type=\"nf4\",\n",
        "    bnb_4bit_compute_dtype=torch.float16,\n",
        "    bnb_4bit_use_double_quant=True,\n",
        ")"
      ],
      "metadata": {
        "id": "mpMUbzmhDu07"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model_id = \"vilsonrodrigues/falcon-7b-instruct-sharded\"\n",
        "model_4bit = AutoModelForCausalLM.from_pretrained(\n",
        "        model_id,\n",
        "        device_map=\"auto\",\n",
        "        quantization_config=quantization_config,\n",
        "        trust_remote_code=True)\n",
        "tokenizer = AutoTokenizer.from_pretrained(model_id)"
      ],
      "metadata": {
        "id": "6KnHGEn2Zr8a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000,
          "referenced_widgets": [
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        },
        "outputId": "991c2982-9a1b-4a45-df84-a44969dbf5b3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:89: UserWarning: \n",
            "The secret `HF_TOKEN` does not exist in your Colab secrets.\n",
            "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n",
            "You will be able to reuse this secret in all of your notebooks.\n",
            "Please note that authentication is recommended but still optional to access public models or datasets.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
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            ],
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        {
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          "text": [
            "A new version of the following files was downloaded from https://huggingface.co/vilsonrodrigues/falcon-7b-instruct-sharded:\n",
            "- configuration_falcon.py\n",
            ". Make sure to double-check they do not contain any added malicious code. To avoid downloading new versions of the code file, you can pin a revision.\n"
          ]
        },
        {
          "output_type": "display_data",
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        {
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          "name": "stderr",
          "text": [
            "A new version of the following files was downloaded from https://huggingface.co/vilsonrodrigues/falcon-7b-instruct-sharded:\n",
            "- modeling_falcon.py\n",
            ". Make sure to double-check they do not contain any added malicious code. To avoid downloading new versions of the code file, you can pin a revision.\n"
          ]
        },
        {
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            ],
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          },
          "metadata": {}
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          "output_type": "display_data",
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
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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": "5ef984d2a7b64a3d9f3a11e704c21c48"
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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              "model_id": "84b8f53237b840778bb2b9b71d9d5102"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "488a8b7feda5426a9775a43ce9609c40"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Loading checkpoint shards:   0%|          | 0/15 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "638eca9faba948c0835ce1b16d0b1a29"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Some weights of FalconForCausalLM were not initialized from the model checkpoint at vilsonrodrigues/falcon-7b-instruct-sharded and are newly initialized: ['lm_head.weight']\n",
            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "185fa488aff84edc98e9a53da1d0aa37"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "d91f43e15ccc4823835cb593cf8723b0"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "54a51bad3ba542d39d6a59c02e38f459"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "bb6a63fb3c374f21ab04ee1f5dc2acb4"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tokenizer = AutoTokenizer.from_pretrained(model_id)\n",
        "tokenizer.pad_token = tokenizer.eos_token"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "p5-RZeP_CGpD",
        "outputId": "271f8b3c-a61f-4575-b7d4-8d1cad0e50a9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.\n",
            "  warnings.warn(\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Calculating Trainable Parameters in a Model"
      ],
      "metadata": {
        "id": "8DGPzsM9Miqt"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def print_trainable_parameters(model_4bit):\n",
        "  \"\"\"\n",
        "  Prints the number of trainable parameters in the model.\n",
        "  \"\"\"\n",
        "  trainable_params = 0\n",
        "  all_param = 0\n",
        "  for _, param in model_4bit.named_parameters():\n",
        "    all_param += param.numel()\n",
        "    if param.requires_grad:\n",
        "      trainable_params += param.numel()\n",
        "  print(\n",
        "      f\"trainable params: {trainable_params} || all params: {all_param} || trainables%: {100 * trainable_params / all_param}\"\n",
        "  )"
      ],
      "metadata": {
        "id": "oNEaUoilWL3z"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model_4bit.gradient_checkpointing_enable()\n",
        "model_4bit = prepare_model_for_kbit_training(model_4bit)"
      ],
      "metadata": {
        "id": "i_QjpX4bWPXw"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "config = LoraConfig(\n",
        "    r=16,\n",
        "    lora_alpha=32,\n",
        "    target_modules=[\"query_key_value\"],\n",
        "    lora_dropout=0.05,\n",
        "    bias=\"none\",\n",
        "    task_type=\"CAUSAL_LM\"\n",
        ")\n",
        "\n",
        "model_4bit = get_peft_model(model_4bit, config)\n",
        "print_trainable_parameters(model_4bit)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xC8uoYM2drMQ",
        "outputId": "3cc45296-ccc6-48e5-9123-ea3e67a0ed6d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "trainable params: 4718592 || all params: 3613463424 || trainables%: 0.13058363808693696\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Test original model"
      ],
      "metadata": {
        "id": "b4qupys5MqdT"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "prompt = \"\"\"\n",
        "<human>: midjourney prompt for a girl sit on the mountain\n",
        "<assistant>:\n",
        "\"\"\".strip()"
      ],
      "metadata": {
        "id": "iFwRjoB8dvg6"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "generation_config = model_4bit.generation_config\n",
        "generation_config.max_new_tokens = 200\n",
        "generation_config.temperature = 0.7\n",
        "generation_config.top_p = 0.7\n",
        "generation_config.num_return_sequences = 1\n",
        "generation_config.pad_token_id = tokenizer.eos_token_id\n",
        "generation_config.eos_token_id = tokenizer.eos_token_id"
      ],
      "metadata": {
        "id": "JZlW-FJmesXX"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "%%time\n",
        "device = \"cuda:0\"\n",
        "\n",
        "encoding = tokenizer(prompt, return_tensors=\"pt\").to(device)\n",
        "with torch.inference_mode():\n",
        "  outputs = model_4bit.generate(\n",
        "      input_ids = encoding.input_ids,\n",
        "      attention_mask = encoding.attention_mask,\n",
        "      generation_config = generation_config\n",
        "  )\n",
        "\n",
        "print(tokenizer.decode(outputs[0], skip_special_tokens=True))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SRpoOIvnezB9",
        "outputId": "6472bcb0-49bc-4b34-e6ce-479e4360d13e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<human>: midjourney prompt for a girl sit on the mountain\n",
            "<assistant>: What do you want to do on the mountain?\n",
            "<human>: I want to take a break and enjoy the view.\n",
            "<assistant>: Alright, take your time.\n",
            "<human>: \n",
            "CPU times: user 4.8 s, sys: 0 ns, total: 4.8 s\n",
            "Wall time: 5.57 s\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Prep dataset"
      ],
      "metadata": {
        "id": "S6cIppQOMz6F"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data = load_dataset(\"csv\", data_files=\"midjourney training dataset - midjourney_prompt_dataset.csv\")"
      ],
      "metadata": {
        "id": "5PLQfLpClq2B"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data[\"train\"][0]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "j4nFDMYRA4G3",
        "outputId": "38d75916-69de-4a5f-9386-7896d7efecc3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'User': '\"midjourney prompt for a Viking warrior with a black iron amulet\"',\n",
              " 'Prompt': '\"Norse amulet made from black iron with a carbon fiber necklace, matte painting, Unreal Engine, --ar 16:9\"'}"
            ]
          },
          "metadata": {},
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def generate_prompt(data_point):\n",
        "  return f\"\"\"\n",
        "<human>: {data_point[\"User\"]}\n",
        "<assistant>: {data_point[\"Prompt\"]}\n",
        "\"\"\".strip()\n",
        "\n",
        "def generate_and_tokenize_prompt(data_point):\n",
        "  full_prompt = generate_prompt(data_point)\n",
        "  tokenized_full_prompt = tokenizer(full_prompt, padding=True, truncation=True)\n",
        "  return tokenized_full_prompt"
      ],
      "metadata": {
        "id": "QAfkvOnylrjG"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data = data[\"train\"].shuffle().map(generate_and_tokenize_prompt)"
      ],
      "metadata": {
        "id": "xM79uYQgqf7U",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49,
          "referenced_widgets": [
            "3efa1c0a0f4949a380da4b600ad512ef",
            "6f496f03a7e94435bd509a1cf30e1add",
            "eb6ce5d2d1c540aa9b2aed582bcd8762",
            "32d31a76ab594a7daef5715701e5c400",
            "8db3de1ffaf4490a8d00efdc1497f11c",
            "cd3ce113f9b24f738533fe2c2426635e",
            "f2a8479b54394443b167b6e15570addc",
            "00911214d8764f4bbdfcee787270a549",
            "939a44b73f014385ad5f4ca054fa9e76",
            "dc226338b21d479db5004bfb494a6c69",
            "357bb618734a4a58a5456111e008d0c7"
          ]
        },
        "outputId": "8470bfee-ac31-4237-c36b-b03a69061d7d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/288 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "3efa1c0a0f4949a380da4b600ad512ef"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Finetune the model"
      ],
      "metadata": {
        "id": "eMSzZloHM5HU"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "training_args = transformers.TrainingArguments(\n",
        "      per_device_train_batch_size=1,\n",
        "      gradient_accumulation_steps=4,\n",
        "      num_train_epochs=1,\n",
        "      learning_rate=2e-4,\n",
        "      fp16=True,\n",
        "      save_total_limit=3,\n",
        "      logging_steps=1,\n",
        "      output_dir=\"experiments\",\n",
        "      optim=\"paged_adamw_8bit\",\n",
        "      lr_scheduler_type=\"cosine\",\n",
        "      warmup_ratio=0.05,\n",
        ")\n",
        "\n",
        "trainer = transformers.Trainer(\n",
        "    model=model_4bit,\n",
        "    train_dataset=data,\n",
        "    args=training_args,\n",
        "    data_collator=transformers.DataCollatorForLanguageModeling(tokenizer, mlm=False)\n",
        ")\n",
        "model_4bit.config.use_cache = False\n",
        "trainer.train()"
      ],
      "metadata": {
        "id": "A9mQG9R6qto3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "51886f75-1ac2-45b1-dfe9-b64750e5612b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "You're using a PreTrainedTokenizerFast 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='72' max='72' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [72/72 06:57, Epoch 1/1]\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>4.550900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2</td>\n",
              "      <td>5.083300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3</td>\n",
              "      <td>4.547700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4</td>\n",
              "      <td>4.559400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>5</td>\n",
              "      <td>4.767100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>6</td>\n",
              "      <td>4.709400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>7</td>\n",
              "      <td>4.415100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>8</td>\n",
              "      <td>4.362500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>9</td>\n",
              "      <td>4.501200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>10</td>\n",
              "      <td>4.399500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>11</td>\n",
              "      <td>4.331100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>12</td>\n",
              "      <td>4.262100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>13</td>\n",
              "      <td>4.215300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>14</td>\n",
              "      <td>3.954300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>15</td>\n",
              "      <td>4.304300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>16</td>\n",
              "      <td>3.969000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>17</td>\n",
              "      <td>3.408300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>18</td>\n",
              "      <td>3.763800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>19</td>\n",
              "      <td>3.237100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>20</td>\n",
              "      <td>3.221900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>21</td>\n",
              "      <td>3.354600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>22</td>\n",
              "      <td>3.306600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>23</td>\n",
              "      <td>3.512100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>24</td>\n",
              "      <td>3.312500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>25</td>\n",
              "      <td>3.123200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>26</td>\n",
              "      <td>3.297600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>27</td>\n",
              "      <td>3.090000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>28</td>\n",
              "      <td>3.408300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>29</td>\n",
              "      <td>2.745300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>30</td>\n",
              "      <td>3.388600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>31</td>\n",
              "      <td>2.823000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>32</td>\n",
              "      <td>2.656400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>33</td>\n",
              "      <td>3.087200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>34</td>\n",
              "      <td>2.267300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>35</td>\n",
              "      <td>2.471400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>36</td>\n",
              "      <td>2.594700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>37</td>\n",
              "      <td>2.211000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>38</td>\n",
              "      <td>2.136000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>39</td>\n",
              "      <td>2.212800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>40</td>\n",
              "      <td>2.465600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>41</td>\n",
              "      <td>2.724800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>42</td>\n",
              "      <td>2.126800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>43</td>\n",
              "      <td>2.427700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>44</td>\n",
              "      <td>2.973400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>45</td>\n",
              "      <td>3.221100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>46</td>\n",
              "      <td>2.917400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>47</td>\n",
              "      <td>2.793400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>48</td>\n",
              "      <td>2.434400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>49</td>\n",
              "      <td>2.275000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>50</td>\n",
              "      <td>3.005600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>51</td>\n",
              "      <td>2.254200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>52</td>\n",
              "      <td>2.699500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>53</td>\n",
              "      <td>2.242400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>54</td>\n",
              "      <td>2.898300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>55</td>\n",
              "      <td>2.680700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>56</td>\n",
              "      <td>2.186900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>57</td>\n",
              "      <td>2.107800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>58</td>\n",
              "      <td>2.647300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>59</td>\n",
              "      <td>2.632600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>60</td>\n",
              "      <td>2.592500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>61</td>\n",
              "      <td>3.347600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>62</td>\n",
              "      <td>2.419600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>63</td>\n",
              "      <td>2.617900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>64</td>\n",
              "      <td>2.298100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>65</td>\n",
              "      <td>2.110300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>66</td>\n",
              "      <td>2.728300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>67</td>\n",
              "      <td>2.597400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>68</td>\n",
              "      <td>2.976700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>69</td>\n",
              "      <td>2.350000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>70</td>\n",
              "      <td>2.829500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>71</td>\n",
              "      <td>3.124900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>72</td>\n",
              "      <td>2.599100</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "TrainOutput(global_step=72, training_loss=3.137059314383401, metrics={'train_runtime': 423.2001, 'train_samples_per_second': 0.681, 'train_steps_per_second': 0.17, 'total_flos': 425393421932544.0, 'train_loss': 3.137059314383401, 'epoch': 1.0})"
            ]
          },
          "metadata": {},
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Save trained model"
      ],
      "metadata": {
        "id": "xzy0TZF5NAon"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model_4bit.save_pretrained(\"trained-model\")"
      ],
      "metadata": {
        "id": "Aucy00h0q7IR"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "PEFT_MODEL = \"finetuned/midjourney-falcon-7b\""
      ],
      "metadata": {
        "id": "E5exEl6kEXDE"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "config = PeftConfig.from_pretrained(PEFT_MODEL)\n",
        "model = AutoModelForCausalLM.from_pretrained(\n",
        "    config.base_model_name_or_path,\n",
        "    return_dict=True,\n",
        "    quantization_config=bnb_config,\n",
        "    device_map=\"auto\",\n",
        "    trust_remote_code=True\n",
        ")\n",
        "\n",
        "tokenizer=AutoTokenizer.from_pretrained(config.base_model_name_or_path)\n",
        "tokenizer.pad_token = tokenizer.eos_token\n",
        "\n",
        "model = PeftModel.from_pretrained(model, PEFT_MODEL)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 137,
          "referenced_widgets": [
            "7766213037db46faa51415ba879fe3ea",
            "313a5ddc8d3a4cf19e6d4cfcad61341b",
            "c8515abcded24b3bbdb5f055dd6813fe",
            "70edeb8826e4424d98f52627924e3fdf",
            "380d8fabed4a46d2915637242be80b02",
            "aac67117111741a3895ab2ae4591fecc",
            "8b9d5a8a55dc4fe48fd5ae2b36400cf9",
            "c9512ae2d27d477893a37e880a95e0c8",
            "eb21eb346d40406689ad2d72623b212d",
            "5521df8d3b1841ddaa995ebe1d3c8dc1",
            "6bb7f9aec41c428f824355862e1fe105",
            "ff075771c46b4baea6275e2b819fb93b",
            "4fc256f94cc54ae1bc53928c83a815b8",
            "04d9f4a0956e424ab5041d28cf35b66e",
            "26d1eba0e77e47f8be6edff866995ab8",
            "f0c514614ccd4905a1b63d9deef9fa47",
            "0cc6e5b7be884401974c38e9207829d8",
            "3ebf375ae5bd4f3e88d1021b4416a098",
            "81c3a498ab304f96bc63d984554d5363",
            "fe4e2f462c3c4bb7acdb4abe1e55d740",
            "01117e8df6564915a6ded26fea9755fd",
            "35eb3e9e612646478837db1af5da2df5"
          ]
        },
        "id": "ZczVtLSyGl7A",
        "outputId": "9bb65d76-9b25-4818-ea4b-50f22fcaf41a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Loading checkpoint shards:   0%|          | 0/15 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "7766213037db46faa51415ba879fe3ea"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Some weights of FalconForCausalLM were not initialized from the model checkpoint at vilsonrodrigues/falcon-7b-instruct-sharded and are newly initialized: ['lm_head.weight']\n",
            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "adapter_model.bin:   0%|          | 0.00/18.9M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "ff075771c46b4baea6275e2b819fb93b"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Run the finetuned model"
      ],
      "metadata": {
        "id": "GvbLk9mvNFCU"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "generation_config = model.generation_config\n",
        "generation_config.max_new_tokens = 200\n",
        "generation_config.temperature = 0.7\n",
        "generation_config.top_p = 0.7\n",
        "generation_config.num_return_sequences = 1\n",
        "generation_config.pad_token_id = tokenizer.eos_token_id\n",
        "generation_config.eos_token_id = tokenizer.eos_token_id"
      ],
      "metadata": {
        "id": "XuyTmuTCIlQi"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "%%time\n",
        "device = \"cuda:0\"\n",
        "\n",
        "prompt = \"\"\"\n",
        "<human>: midjourney prompt for a boy running in the snow\n",
        "<assistant>:\n",
        "\"\"\".strip()\n",
        "\n",
        "encoding = tokenizer(prompt, return_tensors=\"pt\").to(device)\n",
        "with torch.inference_mode():\n",
        "  outputs = model.generate(\n",
        "      input_ids = encoding.input_ids,\n",
        "      attention_mask = encoding.attention_mask,\n",
        "      generation_config = generation_config\n",
        "  )\n",
        "\n",
        "print(tokenizer.decode(outputs[0], skip_special_tokens=True))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vl8MzLkYIm4B",
        "outputId": "c7c1a334-b3cb-432d-b3fc-9995c387fb1c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<human>: midjourney prompt for a boy running in the snow\n",
            "<assistant>: A boy running in the snow, with a backpack, and a red scarf, by the famous artist, \"The Simpsons\" --ar 16:9 --w 3000 --h 2000 --no-blur --ar 16:9 --w 3000 --h 2000 --no-blur --ar 16:9 --w 3000 --h 2000 --no-blur --ar 16:9 --w 3000 --h 2000 --no-blur --ar 16:9 --w 3000 --h 2000 --no-blur --ar 16:9 --w 3000 --h 2000 --no-blur --ar 16:9 --w 3000 --h 2000 --no-blur --ar 16\n",
            "CPU times: user 18.6 s, sys: 22.3 ms, total: 18.6 s\n",
            "Wall time: 25.1 s\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Result:\n",
        "\n",
        "* The output is a detailed description that aligns with the prompt. The system outputs the generated text and includes metadata about the time taken for processing, with CPU times of 18.6 seconds and a total wall time of 25.1 seconds. This output reflects the model's ability to understand and generate content based on the given input.\n",
        "\n",
        "Conclusion:\n",
        "\n",
        "* This process demonstrates how fine-tuned models can effectively generate creative and contextually relevant outputs based on specific prompts. The result highlights the model's capability in interpreting detailed instructions and producing coherent and imaginative responses."
      ],
      "metadata": {
        "id": "KxruY-2-NPcv"
      }
    }
  ]
}