DialoGPT_large_vetdataset_finetuning.ipynb

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  "cells": [
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LNsMhNS1tIyy",
        "outputId": "1085294c-b602-4a90-db4a-26b5ad396570"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting accelerate\n",
            "  Downloading accelerate-0.29.3-py3-none-any.whl (297 kB)\n",
            "\u001b[?25l     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/297.6 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K     \u001b[91m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━\u001b[0m \u001b[32m256.0/297.6 kB\u001b[0m \u001b[31m7.6 MB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0m\r\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m297.6/297.6 kB\u001b[0m \u001b[31m6.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting peft\n",
            "  Downloading peft-0.10.0-py3-none-any.whl (199 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m199.1/199.1 kB\u001b[0m \u001b[31m12.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting bitsandbytes\n",
            "  Downloading bitsandbytes-0.43.1-py3-none-manylinux_2_24_x86_64.whl (119.8 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m119.8/119.8 MB\u001b[0m \u001b[31m8.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: transformers in /usr/local/lib/python3.10/dist-packages (4.40.0)\n",
            "Collecting trl\n",
            "  Downloading trl-0.8.6-py3-none-any.whl (245 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m245.2/245.2 kB\u001b[0m \u001b[31m31.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.10/dist-packages (from accelerate) (1.25.2)\n",
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            "Requirement already satisfied: tokenizers<0.20,>=0.19 in /usr/local/lib/python3.10/dist-packages (from transformers) (0.19.1)\n",
            "Collecting datasets (from trl)\n",
            "  Downloading datasets-2.19.0-py3-none-any.whl (542 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m542.0/542.0 kB\u001b[0m \u001b[31m50.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting tyro>=0.5.11 (from trl)\n",
            "  Downloading tyro-0.8.3-py3-none-any.whl (102 kB)\n",
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            "\u001b[?25hRequirement already satisfied: fsspec>=2023.5.0 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub->accelerate) (2023.6.0)\n",
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            "Requirement already satisfied: networkx in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate) (3.3)\n",
            "Requirement already satisfied: jinja2 in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate) (3.1.3)\n",
            "Collecting nvidia-cuda-nvrtc-cu12==12.1.105 (from torch>=1.10.0->accelerate)\n",
            "  Using cached nvidia_cuda_nvrtc_cu12-12.1.105-py3-none-manylinux1_x86_64.whl (23.7 MB)\n",
            "Collecting nvidia-cuda-runtime-cu12==12.1.105 (from torch>=1.10.0->accelerate)\n",
            "  Using cached nvidia_cuda_runtime_cu12-12.1.105-py3-none-manylinux1_x86_64.whl (823 kB)\n",
            "Collecting nvidia-cuda-cupti-cu12==12.1.105 (from torch>=1.10.0->accelerate)\n",
            "  Using cached nvidia_cuda_cupti_cu12-12.1.105-py3-none-manylinux1_x86_64.whl (14.1 MB)\n",
            "Collecting nvidia-cudnn-cu12==8.9.2.26 (from torch>=1.10.0->accelerate)\n",
            "  Using cached nvidia_cudnn_cu12-8.9.2.26-py3-none-manylinux1_x86_64.whl (731.7 MB)\n",
            "Collecting nvidia-cublas-cu12==12.1.3.1 (from torch>=1.10.0->accelerate)\n",
            "  Using cached nvidia_cublas_cu12-12.1.3.1-py3-none-manylinux1_x86_64.whl (410.6 MB)\n",
            "Collecting nvidia-cufft-cu12==11.0.2.54 (from torch>=1.10.0->accelerate)\n",
            "  Using cached nvidia_cufft_cu12-11.0.2.54-py3-none-manylinux1_x86_64.whl (121.6 MB)\n",
            "Collecting nvidia-curand-cu12==10.3.2.106 (from torch>=1.10.0->accelerate)\n",
            "  Using cached nvidia_curand_cu12-10.3.2.106-py3-none-manylinux1_x86_64.whl (56.5 MB)\n",
            "Collecting nvidia-cusolver-cu12==11.4.5.107 (from torch>=1.10.0->accelerate)\n",
            "  Using cached nvidia_cusolver_cu12-11.4.5.107-py3-none-manylinux1_x86_64.whl (124.2 MB)\n",
            "Collecting nvidia-cusparse-cu12==12.1.0.106 (from torch>=1.10.0->accelerate)\n",
            "  Using cached nvidia_cusparse_cu12-12.1.0.106-py3-none-manylinux1_x86_64.whl (196.0 MB)\n",
            "Collecting nvidia-nccl-cu12==2.19.3 (from torch>=1.10.0->accelerate)\n",
            "  Using cached nvidia_nccl_cu12-2.19.3-py3-none-manylinux1_x86_64.whl (166.0 MB)\n",
            "Collecting nvidia-nvtx-cu12==12.1.105 (from torch>=1.10.0->accelerate)\n",
            "  Using cached nvidia_nvtx_cu12-12.1.105-py3-none-manylinux1_x86_64.whl (99 kB)\n",
            "Requirement already satisfied: triton==2.2.0 in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate) (2.2.0)\n",
            "Collecting nvidia-nvjitlink-cu12 (from nvidia-cusolver-cu12==11.4.5.107->torch>=1.10.0->accelerate)\n",
            "  Using cached nvidia_nvjitlink_cu12-12.4.127-py3-none-manylinux2014_x86_64.whl (21.1 MB)\n",
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            "Collecting shtab>=1.5.6 (from tyro>=0.5.11->trl)\n",
            "  Downloading shtab-1.7.1-py3-none-any.whl (14 kB)\n",
            "Requirement already satisfied: pyarrow>=12.0.0 in /usr/local/lib/python3.10/dist-packages (from datasets->trl) (14.0.2)\n",
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            "Collecting dill<0.3.9,>=0.3.0 (from datasets->trl)\n",
            "  Downloading dill-0.3.8-py3-none-any.whl (116 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m116.3/116.3 kB\u001b[0m \u001b[31m18.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: pandas in /usr/local/lib/python3.10/dist-packages (from datasets->trl) (2.0.3)\n",
            "Collecting xxhash (from datasets->trl)\n",
            "  Downloading xxhash-3.4.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (194 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m194.1/194.1 kB\u001b[0m \u001b[31m29.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting multiprocess (from datasets->trl)\n",
            "  Downloading multiprocess-0.70.16-py310-none-any.whl (134 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m134.8/134.8 kB\u001b[0m \u001b[31m17.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: aiohttp in /usr/local/lib/python3.10/dist-packages (from datasets->trl) (3.9.5)\n",
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            "  Downloading huggingface_hub-0.22.2-py3-none-any.whl (388 kB)\n",
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            "Requirement already satisfied: pygments<3.0.0,>=2.13.0 in /usr/local/lib/python3.10/dist-packages (from rich>=11.1.0->tyro>=0.5.11->trl) (2.16.1)\n",
            "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from jinja2->torch>=1.10.0->accelerate) (2.1.5)\n",
            "Requirement already satisfied: python-dateutil>=2.8.2 in /usr/local/lib/python3.10/dist-packages (from pandas->datasets->trl) (2.8.2)\n",
            "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas->datasets->trl) (2023.4)\n",
            "Requirement already satisfied: tzdata>=2022.1 in /usr/local/lib/python3.10/dist-packages (from pandas->datasets->trl) (2024.1)\n",
            "Requirement already satisfied: mpmath>=0.19 in /usr/local/lib/python3.10/dist-packages (from sympy->torch>=1.10.0->accelerate) (1.3.0)\n",
            "Requirement already satisfied: mdurl~=0.1 in /usr/local/lib/python3.10/dist-packages (from markdown-it-py>=2.2.0->rich>=11.1.0->tyro>=0.5.11->trl) (0.1.2)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.8.2->pandas->datasets->trl) (1.16.0)\n",
            "Installing collected packages: xxhash, shtab, nvidia-nvtx-cu12, nvidia-nvjitlink-cu12, nvidia-nccl-cu12, nvidia-curand-cu12, nvidia-cufft-cu12, nvidia-cuda-runtime-cu12, nvidia-cuda-nvrtc-cu12, nvidia-cuda-cupti-cu12, nvidia-cublas-cu12, dill, nvidia-cusparse-cu12, nvidia-cudnn-cu12, multiprocess, huggingface-hub, tyro, nvidia-cusolver-cu12, datasets, bitsandbytes, accelerate, trl, peft\n",
            "  Attempting uninstall: huggingface-hub\n",
            "    Found existing installation: huggingface-hub 0.20.3\n",
            "    Uninstalling huggingface-hub-0.20.3:\n",
            "      Successfully uninstalled huggingface-hub-0.20.3\n",
            "Successfully installed accelerate-0.29.3 bitsandbytes-0.43.1 datasets-2.19.0 dill-0.3.8 huggingface-hub-0.22.2 multiprocess-0.70.16 nvidia-cublas-cu12-12.1.3.1 nvidia-cuda-cupti-cu12-12.1.105 nvidia-cuda-nvrtc-cu12-12.1.105 nvidia-cuda-runtime-cu12-12.1.105 nvidia-cudnn-cu12-8.9.2.26 nvidia-cufft-cu12-11.0.2.54 nvidia-curand-cu12-10.3.2.106 nvidia-cusolver-cu12-11.4.5.107 nvidia-cusparse-cu12-12.1.0.106 nvidia-nccl-cu12-2.19.3 nvidia-nvjitlink-cu12-12.4.127 nvidia-nvtx-cu12-12.1.105 peft-0.10.0 shtab-1.7.1 trl-0.8.6 tyro-0.8.3 xxhash-3.4.1\n"
          ]
        }
      ],
      "source": [
        "!pip3 install accelerate peft bitsandbytes transformers trl"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from huggingface_hub import notebook_login\n",
        "notebook_login()"
      ],
      "metadata": {
        "id": "Onpcbr19tpuo",
        "outputId": "0133f5a1-845f-4995-c011-a9aedb60b941",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 145,
          "referenced_widgets": [
            "6724b28b45214e00a096d17cbc7088e5",
            "3037d0116f864c6b85e47ff8c4f224ee",
            "a88dbb663720432f988bedc74ac7d78a",
            "c9023eb2a32a47d9b3bc3c087506827b",
            "c5fbedb6c4f34da7b1cf7821506222c2",
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            "fdf8f074b8674bf4a99c0021e022065c",
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            "394a806d3e9f4d09af9f6845ff87d755",
            "9dd37bc9c1484512bce8c04e3583db55"
          ]
        }
      },
      "execution_count": 2,
      "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": "6724b28b45214e00a096d17cbc7088e5"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# load the required packages.\n",
        "import torch\n",
        "from datasets import load_dataset, Dataset\n",
        "from peft import LoraConfig, AutoPeftModelForCausalLM\n",
        "from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig, TrainingArguments\n",
        "from trl import SFTTrainer\n",
        "import os"
      ],
      "metadata": {
        "id": "8mnl7YgdtxAb"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "dataset=\"mudata.json\"\n",
        "model_id=\"microsoft/DialoGPT-large\"\n",
        "output_model=\"DialoGPT-large-vetdataset-finetuned\""
      ],
      "metadata": {
        "id": "4Ho3JJDLt6Pa"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Data preparation"
      ],
      "metadata": {
        "id": "GnimXYnRvR9C"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import json\n",
        "\n",
        "# Read the original JSON file\n",
        "with open('mudata.json', 'r') as file:\n",
        "    data = json.load(file)\n",
        "\n",
        "# Add IDs to each row\n",
        "for idx, item in enumerate(data, start=1):\n",
        "    item['id'] = idx\n",
        "\n",
        "# Write the updated data to a new file\n",
        "new_file_path = 'updated_data.json'\n",
        "with open(new_file_path, 'w') as new_file:\n",
        "    json.dump(data, new_file, indent=2)\n",
        "\n",
        "print(f'Updated data with IDs written to {new_file_path}')"
      ],
      "metadata": {
        "id": "28VnkJnUlyFa",
        "outputId": "7f3d2951-fafe-48fa-eecc-706d4d2056e7",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Updated data with IDs written to updated_data.json\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from datasets import load_dataset\n",
        "\n",
        "# Load dataset from the hub\n",
        "dataset = load_dataset(\"json\", data_files=\"updated_data.json\")\n",
        "\n",
        "datasets_train_test = dataset[\"train\"].train_test_split(test_size=150)\n",
        "datasets_train_validation = datasets_train_test[\"train\"].train_test_split(test_size=150)\n",
        "\n",
        "dataset[\"train\"] = datasets_train_validation[\"train\"]\n",
        "dataset[\"validation\"] = datasets_train_validation[\"test\"]\n",
        "dataset[\"test\"] = datasets_train_test[\"test\"]\n",
        "\n",
        "print(f\"Train dataset size: {len(dataset['train'])}\")\n",
        "print(f\"Test dataset size: {len(dataset['test'])}\")\n",
        "\n",
        "# Train dataset size: 14732\n",
        "# Test dataset size: 819"
      ],
      "metadata": {
        "id": "-hRThMyilzRK",
        "outputId": "51b943ff-d057-456f-eb24-7a7d1f4af6a5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 84,
          "referenced_widgets": [
            "acd26c280ab340d995e8bea365a410a1",
            "b0b6c6353c7744dbbb90eb08c7e085be",
            "ab1a123af1b140f581d20d3ac7024dbf",
            "6b2ec0a5d3a0407da20c031ca286766d",
            "a6882f77a9df434eab6ccd3d675edb57",
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            "dd0fdbe84f0541cebfee87db65f0b727",
            "a522c31a315a49b09fadfbc8a73b37b3",
            "22ce41d0857a494ca2a081cda39b0816",
            "10887e42863c40fd95cf0027a73f2d43",
            "2849f9da5298450eaf9d3b2fc9a66ac7"
          ]
        }
      },
      "execution_count": 6,
      "outputs": [
        {
          "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": "acd26c280ab340d995e8bea365a410a1"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train dataset size: 7444\n",
            "Test dataset size: 150\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Model the Model (not the base version)"
      ],
      "metadata": {
        "id": "Mnej4AYwvawA"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def get_model_and_tokenizer(mode_id):\n",
        "\n",
        "    tokenizer = AutoTokenizer.from_pretrained(mode_id)\n",
        "    tokenizer.pad_token = tokenizer.eos_token\n",
        "    bnb_config = BitsAndBytesConfig(\n",
        "        load_in_4bit=True, bnb_4bit_quant_type=\"nf4\", bnb_4bit_compute_dtype=\"float16\", bnb_4bit_use_double_quant=True\n",
        "    )\n",
        "    model = AutoModelForCausalLM.from_pretrained(\n",
        "        mode_id, quantization_config=bnb_config, device_map=\"auto\"\n",
        "    )\n",
        "    model.config.use_cache=False\n",
        "    model.config.pretraining_tp=1\n",
        "    return model, tokenizer"
      ],
      "metadata": {
        "id": "WrniDfzlvNd7"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# !pip install -i https://test.pypi.org/simple/bitsandbytes"
      ],
      "metadata": {
        "id": "BaD23-Fev7sK"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model, tokenizer = get_model_and_tokenizer(model_id)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 333,
          "referenced_widgets": [
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          ]
        },
        "id": "ogWCiBBzvk1C",
        "outputId": "b67bf50f-59b3-4417-ce04-a0570676f186"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/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": [
              "tokenizer_config.json:   0%|          | 0.00/614 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "926f224c391644628b0e19b251bce68e"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "vocab.json:   0%|          | 0.00/1.04M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "3e4d9ed618214152945e3655d4b17c27"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "merges.txt:   0%|          | 0.00/456k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "03751cd3ff2e461ba751ed9e67baee7a"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "config.json:   0%|          | 0.00/642 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "9cbb6be211e24ec8a87e734dfa183fc2"
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "pytorch_model.bin:   0%|          | 0.00/1.75G [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "e082c2db518c4d159f72bd4bc873187a"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "generation_config.json:   0%|          | 0.00/124 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "496b8f110635459dac79df1d40ea5774"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from datasets import concatenate_datasets\n",
        "\n",
        "# The maximum total input sequence length after tokenization.\n",
        "# Sequences longer than this will be truncated, sequences shorter will be padded.\n",
        "tokenized_inputs = concatenate_datasets([dataset[\"train\"], dataset[\"test\"]]).map(lambda x: tokenizer(x[\"question\"], truncation=True), batched=True)\n",
        "max_source_length = max([len(x) for x in tokenized_inputs[\"input_ids\"]])\n",
        "print(f\"Max source length: {max_source_length}\")\n",
        "\n",
        "# The maximum total sequence length for target text after tokenization.\n",
        "# Sequences longer than this will be truncated, sequences shorter will be padded.\"\n",
        "tokenized_targets = concatenate_datasets([dataset[\"train\"], dataset[\"test\"]]).map(lambda x: tokenizer(x[\"answer\"], truncation=True), batched=True)\n",
        "max_target_length = max([len(x) for x in tokenized_targets[\"input_ids\"]])\n",
        "print(f\"Max target length: {max_target_length}\")\n"
      ],
      "metadata": {
        "id": "ygt68NwWmMWs",
        "outputId": "4273f89f-5080-4074-ed64-100f65fd8286",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 116,
          "referenced_widgets": [
            "28691968e1f54b34bd901e3b53f04497",
            "6e704d8b9930450bbde82e3312a089e7",
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            "f74281a27139459fb590f1cf91a33d5b",
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            "65365a7e550b45ee984ee7ada43a3c0e",
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            "03e7566119b14f5eb79e7495e9867afe",
            "2f9e6c5a3b6741e48b980e9d6487fa1c",
            "97057e1b0b6847d499c74fbaf8658745",
            "fb53d50fa5d3466db9e1ae19226d59b5",
            "388a011857a542669369fac991fd08c0"
          ]
        }
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/7594 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "28691968e1f54b34bd901e3b53f04497"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Max source length: 51\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/7594 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "b54d6058737e412088cff5056802aedb"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Max target length: 125\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def preprocess_function(sample,padding=\"max_length\"):\n",
        "    # add prefix to the input for t5\n",
        "    inputs = [\"answer the following question: \" + item for item in sample[\"question\"]]\n",
        "\n",
        "    # tokenize inputs\n",
        "    model_inputs = tokenizer(inputs, max_length=max_source_length, padding=padding, truncation=True)\n",
        "\n",
        "    # Tokenize targets with the `text_target` keyword argument\n",
        "    labels = tokenizer(text_target=sample[\"answer\"], max_length=max_target_length, padding=padding, truncation=True)\n",
        "\n",
        "    # If we are padding here, replace all tokenizer.pad_token_id in the labels by -100 when we want to ignore\n",
        "    # padding in the loss.\n",
        "    if padding == \"max_length\":\n",
        "        labels[\"input_ids\"] = [\n",
        "            [(l if l != tokenizer.pad_token_id else -100) for l in label] for label in labels[\"input_ids\"]\n",
        "        ]\n",
        "\n",
        "    model_inputs[\"labels\"] = labels[\"input_ids\"]\n",
        "    return model_inputs\n",
        "\n",
        "tokenized_dataset = dataset.map(preprocess_function, batched=True)\n",
        "print(f\"Keys of tokenized dataset: {list(tokenized_dataset['train'].features)}\")"
      ],
      "metadata": {
        "id": "YpCmKE10mp95",
        "outputId": "216d16b5-a303-4908-c00f-bbb0695deed2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 130,
          "referenced_widgets": [
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            "33dced8b9d6e400c8e58417c8a6546d1"
          ]
        }
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/7444 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "5ef66d13af204f84b173daafe4a314a7"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/150 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "26f87ded4c7f4d98be6796c59bab2ad6"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/150 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "0522ca1c637046fd8c44acd680e14724"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Keys of tokenized dataset: ['id', 'question', 'answer', 'input_ids', 'attention_mask', 'labels']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tokenized_dataset = tokenized_dataset.map(lambda example: {'text': example['question'] + example['answer']})"
      ],
      "metadata": {
        "id": "1Tb5o7upmTDg",
        "outputId": "d032823d-e140-4dc6-e8fa-34b07fd69d37",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 113,
          "referenced_widgets": [
            "c3484f6d3dcb44a5b21b4c8339696716",
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            "0fb94d6377a94642ae7d90200892e927",
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            "d053e3a3e82c4c78af97339d6dc2ee68",
            "7fdabeadff134986b62562ff98ee6ab3",
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            "64b2bc2029f746a08a989e741cb289f9",
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            "95ff7b1bff264f7c826f7aa88d48918b",
            "c4c8593e796e4717a13e4e557ed80ced"
          ]
        }
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/7444 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "c3484f6d3dcb44a5b21b4c8339696716"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/150 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "8599de3e7af34be99060748a06cbbbe7"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/150 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "65c1f8717b734f06a04dd4ef6f7a7c2e"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Setting up the LoRA"
      ],
      "metadata": {
        "id": "d2hOfQBZw_YX"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "peft_config = LoraConfig(\n",
        "        r=8, lora_alpha=16, lora_dropout=0.05, bias=\"none\", task_type=\"CAUSAL_LM\"\n",
        "    )"
      ],
      "metadata": {
        "id": "JAGakLbmvtj6"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "training_arguments = TrainingArguments(\n",
        "        output_dir=output_model,\n",
        "        per_device_train_batch_size=16,\n",
        "        gradient_accumulation_steps=4,\n",
        "        optim=\"paged_adamw_32bit\",\n",
        "        learning_rate=2e-4,\n",
        "        lr_scheduler_type=\"cosine\",\n",
        "        save_strategy=\"epoch\",\n",
        "        logging_steps=10,\n",
        "        num_train_epochs=3,\n",
        "        max_steps=250,\n",
        "        fp16=True,\n",
        "        push_to_hub=True\n",
        "    )"
      ],
      "metadata": {
        "id": "Suz5-CbVxDGm"
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "trainer = SFTTrainer(\n",
        "        model=model,\n",
        "        train_dataset=tokenized_dataset[\"train\"],\n",
        "        peft_config=peft_config,\n",
        "        dataset_text_field=\"text\",\n",
        "        args=training_arguments,\n",
        "        tokenizer=tokenizer,\n",
        "        packing=False,\n",
        "        max_seq_length=1024\n",
        "    )"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "g7KtgzA8xGVs",
        "outputId": "6dd987b8-007d-40e0-a0a5-b0fc84d2a39d"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "max_steps is given, it will override any value given in num_train_epochs\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "trainer.train()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 911
        },
        "id": "nXOXGKK0xU6X",
        "outputId": "5a04214e-7d8f-4ce1-fc74-011133c4ac01"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='250' max='250' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [250/250 06:50, Epoch 2/3]\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>10</td>\n",
              "      <td>6.942900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>20</td>\n",
              "      <td>6.514400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>30</td>\n",
              "      <td>5.785600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>40</td>\n",
              "      <td>4.943800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>50</td>\n",
              "      <td>4.454000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>60</td>\n",
              "      <td>3.914100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>70</td>\n",
              "      <td>3.574400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>80</td>\n",
              "      <td>3.375300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>90</td>\n",
              "      <td>3.257400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>100</td>\n",
              "      <td>3.159900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>110</td>\n",
              "      <td>3.082600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>120</td>\n",
              "      <td>2.990500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>130</td>\n",
              "      <td>2.977900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>140</td>\n",
              "      <td>3.036000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>150</td>\n",
              "      <td>2.865800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>160</td>\n",
              "      <td>2.892300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>170</td>\n",
              "      <td>2.806000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>180</td>\n",
              "      <td>2.739600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>190</td>\n",
              "      <td>2.829300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>200</td>\n",
              "      <td>2.781000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>210</td>\n",
              "      <td>2.795200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>220</td>\n",
              "      <td>2.767300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>230</td>\n",
              "      <td>2.787600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>240</td>\n",
              "      <td>2.786800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>250</td>\n",
              "      <td>2.651900</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "TrainOutput(global_step=250, training_loss=3.548452667236328, metrics={'train_runtime': 413.7897, 'train_samples_per_second': 38.667, 'train_steps_per_second': 0.604, 'total_flos': 3470285595893760.0, 'train_loss': 3.548452667236328, 'epoch': 2.1459227467811157})"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Save trained model\n",
        "trainer.model.save_pretrained(output_model)"
      ],
      "metadata": {
        "id": "VwKEgchY103o"
      },
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Merging the LoRA with the base model"
      ],
      "metadata": {
        "id": "Z1DJz6Zr4Jke"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Empty VRAM\n",
        "# del model\n",
        "# del trainer\n",
        "# import gc\n",
        "# gc.collect()\n",
        "# gc.collect()"
      ],
      "metadata": {
        "id": "B0mcDBnlu8iD"
      },
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from peft import AutoPeftModelForCausalLM, PeftModel\n",
        "from transformers import AutoModelForCausalLM\n",
        "import torch\n",
        "import os\n",
        "\n",
        "base_model = AutoModelForCausalLM.from_pretrained(model_id, low_cpu_mem_usage=True, return_dict=True, torch_dtype=torch.float16, load_in_8bit=False,\n",
        "                                             device_map=\"auto\",\n",
        "                                             trust_remote_code=True)\n",
        "\n",
        "model_path = \"/content/DialoGPT-large-vetdataset-finetuned\"\n",
        "\n",
        "peft_model = PeftModel.from_pretrained(base_model, output_model, from_transformers=True, device_map=\"auto\")\n",
        "\n",
        "model = peft_model.merge_and_unload()\n",
        "\n",
        "# Reload tokenizer to save it\n",
        "tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)\n",
        "tokenizer.pad_token = tokenizer.eos_token\n",
        "tokenizer.padding_side = \"right\""
      ],
      "metadata": {
        "id": "V6QKygPu4E91"
      },
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.push_to_hub(output_model, use_temp_dir=False)\n",
        "tokenizer.push_to_hub(output_model, use_temp_dir=False)"
      ],
      "metadata": {
        "id": "mudcKgOknSLX",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 133,
          "referenced_widgets": [
            "d982174ec6134d1b91e4dc68ed1f3e55",
            "8df28ded0d4247df97a5b7980eb3d35d",
            "4c2e1fd20b224d38ad862d31fca32f61",
            "1c84b316bb604dfd8f45b9fbfd8cc029",
            "d49d7bcfb4d4499db914d6927cc9477f",
            "c661c751ea9c4df180dae83e68b5ae3b",
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            "fa45c6aa7dc14b3aaafbb2f5ed9866e4",
            "cf85bd7f82b0438fb43ed8dbfacef99c",
            "dc77305795b9406493a86265316b4822",
            "a50362c17fe04ee19869b1909b9d6740",
            "a0046769815b40c6b94fbc5c69110833",
            "a3eef1a81b9c4daa9f768e598927bbb3",
            "8708b14c766349e29a82a6c241a54911",
            "137660bdcaa64752a4a4b04397ed4b7d",
            "d77936aa95e14d16966a3dce061b42d3",
            "e8ec6bd66c2343d391ce436007762103",
            "b1dcf4c2ed384c948f7c498d9b55d887",
            "20b15f4f1dad48c7a9ba1100b6c2826c",
            "bb6a2bdfd64c4499a85165e2b8356663",
            "f245df087c514ef7b0bdb305126b63a0"
          ]
        },
        "outputId": "26b5811a-7754-4c23-9f03-45f9fc68dc0f"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "model.safetensors:   0%|          | 0.00/1.55G [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "d982174ec6134d1b91e4dc68ed1f3e55"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "README.md:   0%|          | 0.00/5.17k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "a50362c17fe04ee19869b1909b9d6740"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "CommitInfo(commit_url='https://huggingface.co/ahmed807762/DialoGPT-large-vetdataset-finetuned/commit/f825b069c8c76de1f05a0a457e548eb37ba030e9', commit_message='Upload tokenizer', commit_description='', oid='f825b069c8c76de1f05a0a457e548eb37ba030e9', pr_url=None, pr_revision=None, pr_num=None)"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SU2EIsuI4v50",
        "outputId": "39e7a122-2ff9-41b4-eb60-4e9a6e292c73"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "GPT2LMHeadModel(\n",
              "  (transformer): GPT2Model(\n",
              "    (wte): Embedding(50257, 1280)\n",
              "    (wpe): Embedding(1024, 1280)\n",
              "    (drop): Dropout(p=0.1, inplace=False)\n",
              "    (h): ModuleList(\n",
              "      (0-35): 36 x GPT2Block(\n",
              "        (ln_1): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
              "        (attn): GPT2Attention(\n",
              "          (c_attn): Conv1D()\n",
              "          (c_proj): Conv1D()\n",
              "          (attn_dropout): Dropout(p=0.1, inplace=False)\n",
              "          (resid_dropout): Dropout(p=0.1, inplace=False)\n",
              "        )\n",
              "        (ln_2): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
              "        (mlp): GPT2MLP(\n",
              "          (c_fc): Conv1D()\n",
              "          (c_proj): Conv1D()\n",
              "          (act): NewGELUActivation()\n",
              "          (dropout): Dropout(p=0.1, inplace=False)\n",
              "        )\n",
              "      )\n",
              "    )\n",
              "    (ln_f): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
              "  )\n",
              "  (lm_head): Linear(in_features=1280, out_features=50257, bias=False)\n",
              ")"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Inference from the LLM"
      ],
      "metadata": {
        "id": "n1nOANDaynhU"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import requests\n",
        "\n",
        "API_URL = \"https://api-inference.huggingface.co/models/ahmed807762/DialoGPT-large-vetdataset-finetuned\"\n",
        "headers = {\"Authorization\": \"Bearer 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\"}\n",
        "\n",
        "def query(payload):\n",
        "\tresponse = requests.post(API_URL, headers=headers, json=payload)\n",
        "\treturn response.json()\n",
        "\n",
        "output = query({\n",
        "\t\"inputs\": \"My cow is sick, what should I do? Answer:\",\n",
        "  \"options\": {\"wait_for_model\": True},\n",
        "  \"parameters\": {\"max_new_tokens\": 250}\n",
        "})"
      ],
      "metadata": {
        "id": "zdLnBObCoYVV"
      },
      "execution_count": 32,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "output"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9_6iqHfrtOSm",
        "outputId": "3ecaaa89-e438-4a68-c906-d56fafe759ba"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[{'generated_text': 'My cow is sick, what should I do? Answer: eat more cow.'}]"
            ]
          },
          "metadata": {},
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import GenerationConfig\n",
        "from time import perf_counter\n",
        "\n",
        "def generate_response(user_input):\n",
        "\n",
        "  prompt = formatted_prompt(user_input)\n",
        "\n",
        "  inputs = tokenizer([prompt], return_tensors=\"pt\")\n",
        "  generation_config = GenerationConfig(penalty_alpha=0.6,do_sample = True,\n",
        "      top_k=5,temperature=0.5,repetition_penalty=1.2,\n",
        "      max_new_tokens=12,pad_token_id=tokenizer.eos_token_id\n",
        "  )\n",
        "  start_time = perf_counter()\n",
        "\n",
        "  inputs = tokenizer(prompt, return_tensors=\"pt\").to('cuda')\n",
        "\n",
        "  outputs = model.generate(**inputs, generation_config=generation_config)\n",
        "  print(tokenizer.decode(outputs[0], skip_special_tokens=True))\n",
        "  output_time = perf_counter() - start_time\n",
        "  print(f\"Time taken for inference: {round(output_time,2)} seconds\")"
      ],
      "metadata": {
        "id": "mWKVO_5j5pSk"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def formatted_prompt(question)-> str:\n",
        "    return f\"<|im_start|>user\\n{question}<|im_end|>\\n<|im_start|>assistant:\""
      ],
      "metadata": {
        "id": "yIT0IuT9HtEp"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def print_color_space(hex_color):\n",
        "    def hex_to_rgb(hex_color):\n",
        "        hex_color = hex_color.lstrip('#')\n",
        "        return tuple(int(hex_color[i:i+2], 16) for i in (0, 2, 4))\n",
        "    r, g, b = hex_to_rgb(hex_color)\n",
        "    print(f'{hex_color}: \\033[48;2;{r};{g};{b}m           \\033[0m')"
      ],
      "metadata": {
        "id": "cbRpHaNwHthY"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "generate_response(user_input='Light Orange color')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_FeisorS5pZD",
        "outputId": "83830eb7-8cf2-450b-dd19-cb310c12237b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "user\n",
            "Light Orange color \n",
            " assistant: #f0901a \n",
            " light\n",
            "Time taken for inference: 0.35 seconds\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print_color_space('#f0901a ')\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QvuqsFit5pcU",
        "outputId": "461399d8-0eac-4f80-80be-777432aeedf8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "#f0901a : \u001b[48;2;240;144;26m           \u001b[0m\n"
          ]
        }
      ]
    }
  ]
}