llm-finetuning

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            "value": "\n<b>Pro Tip:</b> If you don't already have one, you can create a dedicated\n'notebooks' token with 'write' access, that you can then easily reuse for all\nnotebooks. </center>"
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            "_view_module": "@jupyter-widgets/base",
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        }
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  "cells": [
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    {
11246
      "cell_type": "markdown",
11247
      "metadata": {
11248
        "id": "view-in-github",
11249
        "colab_type": "text"
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      },
11251
      "source": [
11252
        "<a href=\"https://colab.research.google.com/github/ashishpatel26/LLM-Finetuning/blob/main/7.FineTune_LLAMA2_with_QLORA.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
11253
      ]
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    },
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    {
11256
      "cell_type": "code",
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      "source": [
11258
        "!nvidia-smi"
11259
      ],
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      "metadata": {
11261
        "colab": {
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          "base_uri": "https://localhost:8080/"
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        },
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        "id": "Y3IgtdTvAvTr",
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        "outputId": "9a0d6276-d46c-43ac-e6f4-5db3ff4583b4"
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      },
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      "execution_count": 3,
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      "outputs": [
11269
        {
11270
          "output_type": "stream",
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          "name": "stdout",
11272
          "text": [
11273
            "Mon Aug 28 07:20:30 2023       \n",
11274
            "+-----------------------------------------------------------------------------+\n",
11275
            "| NVIDIA-SMI 525.105.17   Driver Version: 525.105.17   CUDA Version: 12.0     |\n",
11276
            "|-------------------------------+----------------------+----------------------+\n",
11277
            "| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |\n",
11278
            "| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |\n",
11279
            "|                               |                      |               MIG M. |\n",
11280
            "|===============================+======================+======================|\n",
11281
            "|   0  Tesla T4            Off  | 00000000:00:04.0 Off |                    0 |\n",
11282
            "| N/A   48C    P8    10W /  70W |      0MiB / 15360MiB |      0%      Default |\n",
11283
            "|                               |                      |                  N/A |\n",
11284
            "+-------------------------------+----------------------+----------------------+\n",
11285
            "                                                                               \n",
11286
            "+-----------------------------------------------------------------------------+\n",
11287
            "| Processes:                                                                  |\n",
11288
            "|  GPU   GI   CI        PID   Type   Process name                  GPU Memory |\n",
11289
            "|        ID   ID                                                   Usage      |\n",
11290
            "|=============================================================================|\n",
11291
            "|  No running processes found                                                 |\n",
11292
            "+-----------------------------------------------------------------------------+\n"
11293
          ]
11294
        }
11295
      ]
11296
    },
11297
    {
11298
      "cell_type": "markdown",
11299
      "source": [
11300
        "# Finetune Llama-2-7b on a Google colab\n",
11301
        "Welcome to this Google Colab notebook that shows how to fine-tune the recent Llama-2-7b model on a single Google colab and turn it into a chatbot\n",
11302
        "\n",
11303
        "We will leverage PEFT library from Hugging Face ecosystem, as well as QLoRA for more memory efficient finetuning\n",
11304
        "\n",
11305
        "# Setup\n",
11306
        "Run the cells below to setup and install the required libraries. For our experiment we will need `accelerate`, `peft`, `transformers`, `datasets` and `TRL` to leverage the recent `SFTTrainer`. We will use `bitsandbytes` to [quantize the base model into 4bit](https://huggingface.co/blog/4bit-transformers-bitsandbytes). We will also install einops as it is a requirement to load Falcon models."
11307
      ],
11308
      "metadata": {
11309
        "id": "imgHssi6BF0T"
11310
      }
11311
    },
11312
    {
11313
      "cell_type": "code",
11314
      "source": [
11315
        "!pip install -q -U trl transformers accelerate git+https://github.com/huggingface/peft.git\n",
11316
        "!pip install -q datasets bitsandbytes einops wandb"
11317
      ],
11318
      "metadata": {
11319
        "colab": {
11320
          "base_uri": "https://localhost:8080/"
11321
        },
11322
        "id": "JLyW25iEAvWJ",
11323
        "outputId": "48b17dbd-4688-42d1-9dbb-a8ad1039a33c"
11324
      },
11325
      "execution_count": 4,
11326
      "outputs": [
11327
        {
11328
          "output_type": "stream",
11329
          "name": "stdout",
11330
          "text": [
11331
            "  Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
11332
            "  Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n",
11333
            "  Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
11334
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m42.2/42.2 kB\u001b[0m \u001b[31m1.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
11335
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m2.1/2.1 MB\u001b[0m \u001b[31m31.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
11336
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m188.5/188.5 kB\u001b[0m \u001b[31m20.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
11337
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m215.6/215.6 kB\u001b[0m \u001b[31m24.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
11338
            "\u001b[?25h  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
11339
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m62.7/62.7 kB\u001b[0m \u001b[31m8.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
11340
            "\u001b[?25h  Building wheel for pathtools (setup.py) ... \u001b[?25l\u001b[?25hdone\n"
11341
          ]
11342
        }
11343
      ]
11344
    },
11345
    {
11346
      "cell_type": "markdown",
11347
      "source": [
11348
        "# Dataset"
11349
      ],
11350
      "metadata": {
11351
        "id": "SGFaPwxiBjbn"
11352
      }
11353
    },
11354
    {
11355
      "cell_type": "code",
11356
      "source": [
11357
        "from huggingface_hub import login\n",
11358
        "login()"
11359
      ],
11360
      "metadata": {
11361
        "colab": {
11362
          "base_uri": "https://localhost:8080/",
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          "height": 145,
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          "referenced_widgets": [
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          ]
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        "id": "iVAM1gwoAvYi",
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        "outputId": "aec41b05-18ef-4e5c-8f8c-f034c67a9af6"
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      },
11402
      "execution_count": 5,
11403
      "outputs": [
11404
        {
11405
          "output_type": "display_data",
11406
          "data": {
11407
            "text/plain": [
11408
              "VBox(children=(HTML(value='<center> <img\\nsrc=https://huggingface.co/front/assets/huggingface_logo-noborder.sv…"
11409
            ],
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            "application/vnd.jupyter.widget-view+json": {
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              "model_id": "feea65261cf44a7f90a8582354f95041"
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            }
11415
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        }
11418
      ]
11419
    },
11420
    {
11421
      "cell_type": "code",
11422
      "source": [
11423
        "from datasets import load_dataset\n",
11424
        "\n",
11425
        "dataset_name = 'nisaar/Articles_Constitution_3300_Instruction_Set'\n",
11426
        "dataset = load_dataset(dataset_name, split=\"train\")"
11427
      ],
11428
      "metadata": {
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        "colab": {
11430
          "base_uri": "https://localhost:8080/",
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          "height": 200,
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          ]
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        "id": "F5cXNIBGAvbI",
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        "outputId": "d4d461b2-fc11-44b8-9312-014683e15d35"
11492
      },
11493
      "execution_count": 6,
11494
      "outputs": [
11495
        {
11496
          "output_type": "display_data",
11497
          "data": {
11498
            "text/plain": [
11499
              "Downloading readme:   0%|          | 0.00/1.54k [00:00<?, ?B/s]"
11500
            ],
11501
            "application/vnd.jupyter.widget-view+json": {
11502
              "version_major": 2,
11503
              "version_minor": 0,
11504
              "model_id": "58437a4f57d640e6a1902ee92c816fe3"
11505
            }
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          },
11507
          "metadata": {}
11508
        },
11509
        {
11510
          "output_type": "stream",
11511
          "name": "stdout",
11512
          "text": [
11513
            "Downloading and preparing dataset json/nisaar--Articles_Constitution_3300_Instruction_Set to /root/.cache/huggingface/datasets/nisaar___json/nisaar--Articles_Constitution_3300_Instruction_Set-f7dd4b93e884d248/0.0.0/8bb11242116d547c741b2e8a1f18598ffdd40a1d4f2a2872c7a28b697434bc96...\n"
11514
          ]
11515
        },
11516
        {
11517
          "output_type": "display_data",
11518
          "data": {
11519
            "text/plain": [
11520
              "Downloading data files:   0%|          | 0/1 [00:00<?, ?it/s]"
11521
            ],
11522
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              "version_major": 2,
11524
              "version_minor": 0,
11525
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            }
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        },
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        {
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          "output_type": "display_data",
11532
          "data": {
11533
            "text/plain": [
11534
              "Downloading data:   0%|          | 0.00/8.01M [00:00<?, ?B/s]"
11535
            ],
11536
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11543
        },
11544
        {
11545
          "output_type": "display_data",
11546
          "data": {
11547
            "text/plain": [
11548
              "Extracting data files:   0%|          | 0/1 [00:00<?, ?it/s]"
11549
            ],
11550
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11551
              "version_major": 2,
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          "output_type": "display_data",
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          "data": {
11561
            "text/plain": [
11562
              "Generating train split: 0 examples [00:00, ? examples/s]"
11563
            ],
11564
            "application/vnd.jupyter.widget-view+json": {
11565
              "version_major": 2,
11566
              "version_minor": 0,
11567
              "model_id": "3a15bc5d81c1454fbca431eb3ec36d6f"
11568
            }
11569
          },
11570
          "metadata": {}
11571
        },
11572
        {
11573
          "output_type": "stream",
11574
          "name": "stdout",
11575
          "text": [
11576
            "Dataset json downloaded and prepared to /root/.cache/huggingface/datasets/nisaar___json/nisaar--Articles_Constitution_3300_Instruction_Set-f7dd4b93e884d248/0.0.0/8bb11242116d547c741b2e8a1f18598ffdd40a1d4f2a2872c7a28b697434bc96. Subsequent calls will reuse this data.\n"
11577
          ]
11578
        }
11579
      ]
11580
    },
11581
    {
11582
      "cell_type": "code",
11583
      "source": [
11584
        "dataset[1]"
11585
      ],
11586
      "metadata": {
11587
        "colab": {
11588
          "base_uri": "https://localhost:8080/"
11589
        },
11590
        "id": "-RHMNDfxAvde",
11591
        "outputId": "f06a11ee-9703-4ebb-afb9-773c3c454cd9"
11592
      },
11593
      "execution_count": 7,
11594
      "outputs": [
11595
        {
11596
          "output_type": "execute_result",
11597
          "data": {
11598
            "text/plain": [
11599
              "{'instruction': 'Identify and summarize the key legal issues in the provided case.',\n",
11600
              " 'input': 'Case Citation: Central Inland Water Transport Corporation Ltd. vs Brojo Nath Ganguly & Anr., 1986 AIR 1571, 1986 SCR (2) 278. The case revolves around the termination of employment of the respondents by the appellant without assigning any reason by invoking Rule 9(i) of the service rules. The respondents challenged the termination orders and the validity of Rule 9(i) in the High Court under Article 226. The Division Bench of the High Court struck down Rule 9(i) as arbitrary and violative of Article 14. The appellant filed appeals in the Supreme Court against the High Court judgment.',\n",
11601
              " 'output': \"The key legal issues in the case Central Inland Water Transport Corporation Ltd. vs Brojo Nath Ganguly & Anr. are as follows: 1. The first issue is whether a government company falls under the definition of 'State' as per Article 12 of the Indian Constitution. This is important as the constitutional protections against arbitrary state action apply to 'State' entities. 2. The second issue is the constitutional validity of Rule 9(i) that allows termination of service without reason. The respondents challenged this rule as arbitrary and violative of the right to equality under Article 14 of the Constitution. 3. The third issue is the application of principles of natural justice and constitutional protections in contractual relationships, particularly in employment contracts of state entities. 4. The fourth issue is the impact of arbitrary termination of employment on the right to life and livelihood under Article 21 of the Constitution. 5. The final issue is the scope of judicial review of state action in contractual matters, particularly in the context of employment relationships.\",\n",
11602
              " 'prompt': 'Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.\\n\\n### Instruction:\\nIdentify and summarize the key legal issues in the provided case.\\n\\n### Input:\\nCase Citation: Central Inland Water Transport Corporation Ltd. vs Brojo Nath Ganguly & Anr., 1986 AIR 1571, 1986 SCR (2) 278. The case revolves around the termination of employment of the respondents by the appellant without assigning any reason by invoking Rule 9(i) of the service rules. The respondents challenged the termination orders and the validity of Rule 9(i) in the High Court under Article 226. The Division Bench of the High Court struck down Rule 9(i) as arbitrary and violative of Article 14. The appellant filed appeals in the Supreme Court against the High Court judgment.\\n\\n### Response:'}"
11603
            ]
11604
          },
11605
          "metadata": {},
11606
          "execution_count": 7
11607
        }
11608
      ]
11609
    },
11610
    {
11611
      "cell_type": "code",
11612
      "source": [
11613
        "print(dataset)"
11614
      ],
11615
      "metadata": {
11616
        "colab": {
11617
          "base_uri": "https://localhost:8080/"
11618
        },
11619
        "id": "Pi7qFUStAvf0",
11620
        "outputId": "cfdae4d1-c55b-4813-9f06-0f528f745ae0"
11621
      },
11622
      "execution_count": 8,
11623
      "outputs": [
11624
        {
11625
          "output_type": "stream",
11626
          "name": "stdout",
11627
          "text": [
11628
            "Dataset({\n",
11629
            "    features: ['instruction', 'input', 'output', 'prompt'],\n",
11630
            "    num_rows: 3311\n",
11631
            "})\n"
11632
          ]
11633
        }
11634
      ]
11635
    },
11636
    {
11637
      "cell_type": "code",
11638
      "source": [
11639
        "from datasets import Dataset\n",
11640
        "\n",
11641
        "# Assuming `dataset` is your Dataset object\n",
11642
        "dataset = dataset.map(lambda example: {'text': example['prompt'] + example['output']})"
11643
      ],
11644
      "metadata": {
11645
        "colab": {
11646
          "base_uri": "https://localhost:8080/",
11647
          "height": 17,
11648
          "referenced_widgets": [
11649
            "0a84af2c3ab44a25aef6f500673dba0f",
11650
            "dba9cba0b1e9450b898506eba3a8fc02",
11651
            "9359c2f49917447792a156d111f5ffb3",
11652
            "d11c506816b249eb96ba9043b0735fdb",
11653
            "0ed5d553d5034f9bbfd52a36bc69c268",
11654
            "12f5afa99d37458aa4cc90bb1794f140",
11655
            "ff49c1c27c4c4eb982cd373a6e198013",
11656
            "185740f57ede4564864e055ec9cb8b99",
11657
            "ee3913d1e2e44b368ebfd04b33d5828e",
11658
            "cd89fbf43e3d47899067343efa81f093",
11659
            "261ffd2f550243a69eabcba1ba5cb27f"
11660
          ]
11661
        },
11662
        "id": "bP40uYdQAviK",
11663
        "outputId": "932469e3-b656-4e71-bf38-fed577beb035"
11664
      },
11665
      "execution_count": 9,
11666
      "outputs": [
11667
        {
11668
          "output_type": "display_data",
11669
          "data": {
11670
            "text/plain": [
11671
              "Map:   0%|          | 0/3311 [00:00<?, ? examples/s]"
11672
            ],
11673
            "application/vnd.jupyter.widget-view+json": {
11674
              "version_major": 2,
11675
              "version_minor": 0,
11676
              "model_id": "0a84af2c3ab44a25aef6f500673dba0f"
11677
            }
11678
          },
11679
          "metadata": {}
11680
        }
11681
      ]
11682
    },
11683
    {
11684
      "cell_type": "code",
11685
      "source": [
11686
        "dataset"
11687
      ],
11688
      "metadata": {
11689
        "colab": {
11690
          "base_uri": "https://localhost:8080/"
11691
        },
11692
        "id": "KzXeCGBMAvk_",
11693
        "outputId": "c1198e66-9d6f-41d8-886e-31e0b0eb7363"
11694
      },
11695
      "execution_count": 11,
11696
      "outputs": [
11697
        {
11698
          "output_type": "execute_result",
11699
          "data": {
11700
            "text/plain": [
11701
              "Dataset({\n",
11702
              "    features: ['instruction', 'input', 'output', 'prompt', 'text'],\n",
11703
              "    num_rows: 3311\n",
11704
              "})"
11705
            ]
11706
          },
11707
          "metadata": {},
11708
          "execution_count": 11
11709
        }
11710
      ]
11711
    },
11712
    {
11713
      "cell_type": "markdown",
11714
      "source": [
11715
        "# Loading the Model"
11716
      ],
11717
      "metadata": {
11718
        "id": "u9HdpDFFCJde"
11719
      }
11720
    },
11721
    {
11722
      "cell_type": "code",
11723
      "source": [
11724
        "import torch\n",
11725
        "from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig, AutoTokenizer\n",
11726
        "\n",
11727
        "model_name = \"TinyPixel/Llama-2-7B-bf16-sharded\""
11728
      ],
11729
      "metadata": {
11730
        "id": "s4ERwNnJCAmu"
11731
      },
11732
      "execution_count": 12,
11733
      "outputs": []
11734
    },
11735
    {
11736
      "cell_type": "code",
11737
      "source": [
11738
        "bnb_config = BitsAndBytesConfig(\n",
11739
        "    load_in_4bit=True,\n",
11740
        "    bnb_4bit_quant_type=\"nf4\",\n",
11741
        "    bnb_4bit_compute_dtype=torch.float16,\n",
11742
        ")"
11743
      ],
11744
      "metadata": {
11745
        "id": "LeDoLgeFChU6"
11746
      },
11747
      "execution_count": 13,
11748
      "outputs": []
11749
    },
11750
    {
11751
      "cell_type": "code",
11752
      "source": [
11753
        "model = AutoModelForCausalLM.from_pretrained(\n",
11754
        "    model_name,\n",
11755
        "    quantization_config=bnb_config,\n",
11756
        "    trust_remote_code=True\n",
11757
        ")\n",
11758
        "model.config.use_cache = False"
11759
      ],
11760
      "metadata": {
11761
        "colab": {
11762
          "base_uri": "https://localhost:8080/",
11763
          "height": 625,
11764
          "referenced_widgets": [
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        "outputId": "68c9efa9-5260-4bfe-b2a3-965207f1fc19"
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      "execution_count": 14,
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        {
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        },
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        {
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        }
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      ]
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    },
12249
    {
12250
      "cell_type": "code",
12251
      "source": [
12252
        "tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)\n",
12253
        "tokenizer.pad_token = tokenizer.eos_token"
12254
      ],
12255
      "metadata": {
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        "colab": {
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          "base_uri": "https://localhost:8080/",
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          "height": 145,
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          "referenced_widgets": [
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        "id": "EOzkiEOcCj9Q",
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        "outputId": "396d68d2-5626-46cd-e926-1c12c7bd7700"
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      },
12309
      "execution_count": 15,
12310
      "outputs": [
12311
        {
12312
          "output_type": "display_data",
12313
          "data": {
12314
            "text/plain": [
12315
              "Downloading (…)okenizer_config.json:   0%|          | 0.00/676 [00:00<?, ?B/s]"
12316
            ],
12317
            "application/vnd.jupyter.widget-view+json": {
12318
              "version_major": 2,
12319
              "version_minor": 0,
12320
              "model_id": "59933ece2a4648c98953606f95e009d9"
12321
            }
12322
          },
12323
          "metadata": {}
12324
        },
12325
        {
12326
          "output_type": "display_data",
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          "data": {
12328
            "text/plain": [
12329
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12330
            ],
12331
            "application/vnd.jupyter.widget-view+json": {
12332
              "version_major": 2,
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              "version_minor": 0,
12334
              "model_id": "9fdc1782a4a549aaac3e2547ab868ad2"
12335
            }
12336
          },
12337
          "metadata": {}
12338
        },
12339
        {
12340
          "output_type": "display_data",
12341
          "data": {
12342
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            ],
12345
            "application/vnd.jupyter.widget-view+json": {
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              "version_minor": 0,
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              "model_id": "b73a2f416df84a1fbec426810b95b975"
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            }
12350
          },
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          "metadata": {}
12352
        },
12353
        {
12354
          "output_type": "display_data",
12355
          "data": {
12356
            "text/plain": [
12357
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12358
            ],
12359
            "application/vnd.jupyter.widget-view+json": {
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              "model_id": "749e9ed367c3416ab90a3f478ffc1070"
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            }
12364
          },
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          "metadata": {}
12366
        }
12367
      ]
12368
    },
12369
    {
12370
      "cell_type": "code",
12371
      "source": [
12372
        "from peft import LoraConfig, get_peft_model\n",
12373
        "\n",
12374
        "lora_alpha = 16\n",
12375
        "lora_dropout = 0.1\n",
12376
        "lora_r = 64"
12377
      ],
12378
      "metadata": {
12379
        "id": "wGwdASSTEsQm"
12380
      },
12381
      "execution_count": 16,
12382
      "outputs": []
12383
    },
12384
    {
12385
      "cell_type": "code",
12386
      "source": [
12387
        "peft_config = LoraConfig(\n",
12388
        "    lora_alpha=lora_alpha,\n",
12389
        "    lora_dropout=lora_dropout,\n",
12390
        "    r=lora_r,\n",
12391
        "    bias=\"none\",\n",
12392
        "    task_type=\"CAUSAL_LM\"\n",
12393
        ")"
12394
      ],
12395
      "metadata": {
12396
        "id": "xlwVc75QExMl"
12397
      },
12398
      "execution_count": 17,
12399
      "outputs": []
12400
    },
12401
    {
12402
      "cell_type": "code",
12403
      "source": [
12404
        "from transformers import TrainingArguments"
12405
      ],
12406
      "metadata": {
12407
        "id": "OOJhvD5wE0gt"
12408
      },
12409
      "execution_count": 18,
12410
      "outputs": []
12411
    },
12412
    {
12413
      "cell_type": "code",
12414
      "source": [
12415
        "output_dir = \"./results\"\n",
12416
        "per_device_train_batch_size = 1\n",
12417
        "gradient_accumulation_steps = 2\n",
12418
        "optim = \"paged_adamw_32bit\"\n",
12419
        "save_steps = 1\n",
12420
        "num_train_epochs = 4\n",
12421
        "logging_steps = 1\n",
12422
        "learning_rate = 2e-4\n",
12423
        "max_grad_norm = 0.3\n",
12424
        "max_steps = 20\n",
12425
        "warmup_ratio = 0.03\n",
12426
        "lr_scheduler_type = \"linear\""
12427
      ],
12428
      "metadata": {
12429
        "id": "bbxYDRwbE40V"
12430
      },
12431
      "execution_count": 35,
12432
      "outputs": []
12433
    },
12434
    {
12435
      "cell_type": "code",
12436
      "source": [
12437
        "training_arguments = TrainingArguments(\n",
12438
        "    output_dir=output_dir,\n",
12439
        "    per_device_train_batch_size=per_device_train_batch_size,\n",
12440
        "    gradient_accumulation_steps=gradient_accumulation_steps,\n",
12441
        "    optim=optim,\n",
12442
        "    num_train_epochs=num_train_epochs,\n",
12443
        "    save_steps=save_steps,\n",
12444
        "    logging_steps=logging_steps,\n",
12445
        "    learning_rate=learning_rate,\n",
12446
        "    fp16=True,\n",
12447
        "    max_grad_norm=max_grad_norm,\n",
12448
        "    max_steps=max_steps,\n",
12449
        "    warmup_ratio=warmup_ratio,\n",
12450
        "    group_by_length=True,\n",
12451
        "    lr_scheduler_type=lr_scheduler_type,\n",
12452
        ")"
12453
      ],
12454
      "metadata": {
12455
        "id": "eM-jZqNDFBQk"
12456
      },
12457
      "execution_count": 36,
12458
      "outputs": []
12459
    },
12460
    {
12461
      "cell_type": "code",
12462
      "source": [
12463
        "from trl import SFTTrainer"
12464
      ],
12465
      "metadata": {
12466
        "id": "brStwghtFC3G"
12467
      },
12468
      "execution_count": 37,
12469
      "outputs": []
12470
    },
12471
    {
12472
      "cell_type": "code",
12473
      "source": [
12474
        "max_seq_length = 2048"
12475
      ],
12476
      "metadata": {
12477
        "id": "GmQN8F7kFE2R"
12478
      },
12479
      "execution_count": 38,
12480
      "outputs": []
12481
    },
12482
    {
12483
      "cell_type": "code",
12484
      "source": [
12485
        "trainer = SFTTrainer(\n",
12486
        "    model=model,\n",
12487
        "    train_dataset=dataset,\n",
12488
        "    peft_config=peft_config,\n",
12489
        "    dataset_text_field=\"text\",\n",
12490
        "    max_seq_length=max_seq_length,\n",
12491
        "    tokenizer=tokenizer,\n",
12492
        "    args=training_arguments,\n",
12493
        ")"
12494
      ],
12495
      "metadata": {
12496
        "colab": {
12497
          "base_uri": "https://localhost:8080/"
12498
        },
12499
        "id": "fRLXAyGdFGCM",
12500
        "outputId": "403476e7-65df-430a-99d2-3ef04dbad354"
12501
      },
12502
      "execution_count": 39,
12503
      "outputs": [
12504
        {
12505
          "output_type": "stream",
12506
          "name": "stderr",
12507
          "text": [
12508
            "WARNING:datasets.arrow_dataset:Loading cached processed dataset at /root/.cache/huggingface/datasets/nisaar___json/nisaar--Articles_Constitution_3300_Instruction_Set-f7dd4b93e884d248/0.0.0/8bb11242116d547c741b2e8a1f18598ffdd40a1d4f2a2872c7a28b697434bc96/cache-d315fa7f4915eeac.arrow\n"
12509
          ]
12510
        }
12511
      ]
12512
    },
12513
    {
12514
      "cell_type": "code",
12515
      "source": [
12516
        "for name, module in trainer.model.named_modules():\n",
12517
        "    if \"norm\" in name:\n",
12518
        "        module = module.to(torch.float32)"
12519
      ],
12520
      "metadata": {
12521
        "id": "H40gHk2aFZ-c"
12522
      },
12523
      "execution_count": 40,
12524
      "outputs": []
12525
    },
12526
    {
12527
      "cell_type": "code",
12528
      "source": [
12529
        "trainer.train()"
12530
      ],
12531
      "metadata": {
12532
        "colab": {
12533
          "base_uri": "https://localhost:8080/",
12534
          "height": 737
12535
        },
12536
        "id": "LDj1P1diFeiA",
12537
        "outputId": "b4fcd178-feeb-4221-ad5f-1c866e19c3e0"
12538
      },
12539
      "execution_count": 41,
12540
      "outputs": [
12541
        {
12542
          "output_type": "display_data",
12543
          "data": {
12544
            "text/plain": [
12545
              "<IPython.core.display.HTML object>"
12546
            ],
12547
            "text/html": [
12548
              "\n",
12549
              "    <div>\n",
12550
              "      \n",
12551
              "      <progress value='20' max='20' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
12552
              "      [20/20 02:45, Epoch 0/1]\n",
12553
              "    </div>\n",
12554
              "    <table border=\"1\" class=\"dataframe\">\n",
12555
              "  <thead>\n",
12556
              " <tr style=\"text-align: left;\">\n",
12557
              "      <th>Step</th>\n",
12558
              "      <th>Training Loss</th>\n",
12559
              "    </tr>\n",
12560
              "  </thead>\n",
12561
              "  <tbody>\n",
12562
              "    <tr>\n",
12563
              "      <td>1</td>\n",
12564
              "      <td>1.080600</td>\n",
12565
              "    </tr>\n",
12566
              "    <tr>\n",
12567
              "      <td>2</td>\n",
12568
              "      <td>1.084400</td>\n",
12569
              "    </tr>\n",
12570
              "    <tr>\n",
12571
              "      <td>3</td>\n",
12572
              "      <td>1.225800</td>\n",
12573
              "    </tr>\n",
12574
              "    <tr>\n",
12575
              "      <td>4</td>\n",
12576
              "      <td>1.244200</td>\n",
12577
              "    </tr>\n",
12578
              "    <tr>\n",
12579
              "      <td>5</td>\n",
12580
              "      <td>1.032500</td>\n",
12581
              "    </tr>\n",
12582
              "    <tr>\n",
12583
              "      <td>6</td>\n",
12584
              "      <td>1.129500</td>\n",
12585
              "    </tr>\n",
12586
              "    <tr>\n",
12587
              "      <td>7</td>\n",
12588
              "      <td>1.243900</td>\n",
12589
              "    </tr>\n",
12590
              "    <tr>\n",
12591
              "      <td>8</td>\n",
12592
              "      <td>1.116600</td>\n",
12593
              "    </tr>\n",
12594
              "    <tr>\n",
12595
              "      <td>9</td>\n",
12596
              "      <td>1.015100</td>\n",
12597
              "    </tr>\n",
12598
              "    <tr>\n",
12599
              "      <td>10</td>\n",
12600
              "      <td>1.170400</td>\n",
12601
              "    </tr>\n",
12602
              "    <tr>\n",
12603
              "      <td>11</td>\n",
12604
              "      <td>1.081400</td>\n",
12605
              "    </tr>\n",
12606
              "    <tr>\n",
12607
              "      <td>12</td>\n",
12608
              "      <td>1.108300</td>\n",
12609
              "    </tr>\n",
12610
              "    <tr>\n",
12611
              "      <td>13</td>\n",
12612
              "      <td>1.026100</td>\n",
12613
              "    </tr>\n",
12614
              "    <tr>\n",
12615
              "      <td>14</td>\n",
12616
              "      <td>1.239200</td>\n",
12617
              "    </tr>\n",
12618
              "    <tr>\n",
12619
              "      <td>15</td>\n",
12620
              "      <td>1.406800</td>\n",
12621
              "    </tr>\n",
12622
              "    <tr>\n",
12623
              "      <td>16</td>\n",
12624
              "      <td>1.019600</td>\n",
12625
              "    </tr>\n",
12626
              "    <tr>\n",
12627
              "      <td>17</td>\n",
12628
              "      <td>1.150400</td>\n",
12629
              "    </tr>\n",
12630
              "    <tr>\n",
12631
              "      <td>18</td>\n",
12632
              "      <td>1.168400</td>\n",
12633
              "    </tr>\n",
12634
              "    <tr>\n",
12635
              "      <td>19</td>\n",
12636
              "      <td>1.203600</td>\n",
12637
              "    </tr>\n",
12638
              "    <tr>\n",
12639
              "      <td>20</td>\n",
12640
              "      <td>1.090500</td>\n",
12641
              "    </tr>\n",
12642
              "  </tbody>\n",
12643
              "</table><p>"
12644
            ]
12645
          },
12646
          "metadata": {}
12647
        },
12648
        {
12649
          "output_type": "execute_result",
12650
          "data": {
12651
            "text/plain": [
12652
              "TrainOutput(global_step=20, training_loss=1.1418639481067658, metrics={'train_runtime': 178.0812, 'train_samples_per_second': 0.225, 'train_steps_per_second': 0.112, 'total_flos': 558884728135680.0, 'train_loss': 1.1418639481067658, 'epoch': 0.01})"
12653
            ]
12654
          },
12655
          "metadata": {},
12656
          "execution_count": 41
12657
        }
12658
      ]
12659
    },
12660
    {
12661
      "cell_type": "code",
12662
      "source": [
12663
        "model_to_save = trainer.model.module if hasattr(trainer.model, 'module') else trainer.model  # Take care of distributed/parallel training\n",
12664
        "model_to_save.save_pretrained(\"outputs\")"
12665
      ],
12666
      "metadata": {
12667
        "id": "9qs8VNC9Hv8_"
12668
      },
12669
      "execution_count": 43,
12670
      "outputs": []
12671
    },
12672
    {
12673
      "cell_type": "code",
12674
      "source": [
12675
        "lora_config = LoraConfig.from_pretrained('outputs')\n",
12676
        "model = get_peft_model(model, lora_config)"
12677
      ],
12678
      "metadata": {
12679
        "id": "vqqTp9DTHxgf"
12680
      },
12681
      "execution_count": 46,
12682
      "outputs": []
12683
    },
12684
    {
12685
      "cell_type": "code",
12686
      "source": [
12687
        "model.push_to_hub(\"ashishpatel26/Llama2_Finetuned_Articles_Constitution_3300_Instruction_Set\",create_pr=1)"
12688
      ],
12689
      "metadata": {
12690
        "colab": {
12691
          "base_uri": "https://localhost:8080/",
12692
          "height": 101,
12693
          "referenced_widgets": [
12694
            "fe0ab67ea8214426ab71fb5de36d69a5",
12695
            "43d9ea6d875c4aa38b72f5070a60f2cd",
12696
            "db6b06a2e2e1405bb8c3a2fe6f766aa9",
12697
            "cbe40a157f6b4182a3f67c7c9df4eba6",
12698
            "bc1a943a610a4dc49a9cd5d980284a31",
12699
            "d6a2b95027f5403b9cbfe8d5c6d47777",
12700
            "8eae3643c149437eb233ed152b902983",
12701
            "986037576263467398ce986a8fa324da",
12702
            "f2e476812ce5481a83da23319f11ad18",
12703
            "6d81bc83184046f18a8628bde4fab5e5",
12704
            "ee762ef897b6403ea012e1e77db4bb18"
12705
          ]
12706
        },
12707
        "id": "YovTZRBoH_22",
12708
        "outputId": "1f0e2b48-4f5a-4372-9285-581ba1209483"
12709
      },
12710
      "execution_count": 47,
12711
      "outputs": [
12712
        {
12713
          "output_type": "display_data",
12714
          "data": {
12715
            "text/plain": [
12716
              "adapter_model.bin:   0%|          | 0.00/134M [00:00<?, ?B/s]"
12717
            ],
12718
            "application/vnd.jupyter.widget-view+json": {
12719
              "version_major": 2,
12720
              "version_minor": 0,
12721
              "model_id": "fe0ab67ea8214426ab71fb5de36d69a5"
12722
            }
12723
          },
12724
          "metadata": {}
12725
        },
12726
        {
12727
          "output_type": "execute_result",
12728
          "data": {
12729
            "text/plain": [
12730
              "CommitInfo(commit_url='https://huggingface.co/ashishpatel26/Llama2_Finetuned_Articles_Constitution_3300_Instruction_Set/commit/0e6e09e0e362c05fdb5bee1d181514793a1eecaa', commit_message='Upload model', commit_description='', oid='0e6e09e0e362c05fdb5bee1d181514793a1eecaa', pr_url='https://huggingface.co/ashishpatel26/Llama2_Finetuned_Articles_Constitution_3300_Instruction_Set/discussions/1', pr_revision='refs/pr/1', pr_num=1)"
12731
            ]
12732
          },
12733
          "metadata": {},
12734
          "execution_count": 47
12735
        }
12736
      ]
12737
    },
12738
    {
12739
      "cell_type": "code",
12740
      "source": [
12741
        "dataset['text'][0]"
12742
      ],
12743
      "metadata": {
12744
        "colab": {
12745
          "base_uri": "https://localhost:8080/",
12746
          "height": 121
12747
        },
12748
        "id": "ySq0kPfwIDgD",
12749
        "outputId": "fd103a8e-151f-42f4-c521-e332b82acd47"
12750
      },
12751
      "execution_count": 52,
12752
      "outputs": [
12753
        {
12754
          "output_type": "execute_result",
12755
          "data": {
12756
            "text/plain": [
12757
              "\"Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.\\n\\n### Instruction:\\nAnalyze and explain the legal reasoning behind the judgment in the given case.\\n\\n### Input:\\nCentral Inland Water Transport Corporation Ltd. vs Brojo Nath Ganguly & Anr., 1986 AIR 1571, 1986 SCR (2) 278\\n\\n### Response:The Supreme Court in this case applied a broad interpretation of the term 'State' under Article 12 of the Constitution. The court reasoned that a government company undertaking public functions qualifies as 'State' based on factors like government control, public importance of activities etc. This interpretation was based on previous decisions that have defined 'State' under Article 12 broadly to include various agencies and instrumentalities beyond just statutory bodies. The court also applied the principle that unreasonable and arbitrary contractual terms can be struck down under Article 14 of the Constitution. The court found that Rule 9(i) of the service rules, which allowed for termination of service without reason, conferred unfettered power to terminate employment without hearing. This was deemed arbitrary and violative of principles of natural justice and right to equality under Article 14. Furthermore, the court held that the right to life and livelihood under Article 21 is affected by arbitrary termination of employment. The court reasoned that the right to livelihood is an integral part of the right to life, and any arbitrary action that affects a person's livelihood would be a violation of Article 21.In conclusion, the court's legal reasoning was based on a broad interpretation of the term 'State', the application of the principle of equality and natural justice under Article 14, and the protection of the right to life and livelihood under Article 21.\""
12758
            ],
12759
            "application/vnd.google.colaboratory.intrinsic+json": {
12760
              "type": "string"
12761
            }
12762
          },
12763
          "metadata": {},
12764
          "execution_count": 52
12765
        }
12766
      ]
12767
    },
12768
    {
12769
      "cell_type": "code",
12770
      "source": [
12771
        "dataset['output'][0]"
12772
      ],
12773
      "metadata": {
12774
        "colab": {
12775
          "base_uri": "https://localhost:8080/",
12776
          "height": 121
12777
        },
12778
        "id": "ZHBt78Y0I3Fk",
12779
        "outputId": "58918ee9-2a84-48d4-c2af-ca5285dfc4d3"
12780
      },
12781
      "execution_count": 55,
12782
      "outputs": [
12783
        {
12784
          "output_type": "execute_result",
12785
          "data": {
12786
            "text/plain": [
12787
              "\"The Supreme Court in this case applied a broad interpretation of the term 'State' under Article 12 of the Constitution. The court reasoned that a government company undertaking public functions qualifies as 'State' based on factors like government control, public importance of activities etc. This interpretation was based on previous decisions that have defined 'State' under Article 12 broadly to include various agencies and instrumentalities beyond just statutory bodies. The court also applied the principle that unreasonable and arbitrary contractual terms can be struck down under Article 14 of the Constitution. The court found that Rule 9(i) of the service rules, which allowed for termination of service without reason, conferred unfettered power to terminate employment without hearing. This was deemed arbitrary and violative of principles of natural justice and right to equality under Article 14. Furthermore, the court held that the right to life and livelihood under Article 21 is affected by arbitrary termination of employment. The court reasoned that the right to livelihood is an integral part of the right to life, and any arbitrary action that affects a person's livelihood would be a violation of Article 21.In conclusion, the court's legal reasoning was based on a broad interpretation of the term 'State', the application of the principle of equality and natural justice under Article 14, and the protection of the right to life and livelihood under Article 21.\""
12788
            ],
12789
            "application/vnd.google.colaboratory.intrinsic+json": {
12790
              "type": "string"
12791
            }
12792
          },
12793
          "metadata": {},
12794
          "execution_count": 55
12795
        }
12796
      ]
12797
    },
12798
    {
12799
      "cell_type": "code",
12800
      "source": [
12801
        "text = dataset['text'][0]\n",
12802
        "device = \"cuda:0\""
12803
      ],
12804
      "metadata": {
12805
        "id": "qgOxiM-UIPKK"
12806
      },
12807
      "execution_count": 53,
12808
      "outputs": []
12809
    },
12810
    {
12811
      "cell_type": "code",
12812
      "source": [
12813
        "inputs = tokenizer(text, return_tensors=\"pt\").to(device)\n",
12814
        "outputs = model.generate(**inputs, max_new_tokens=50)\n",
12815
        "print(tokenizer.decode(outputs[0], skip_special_tokens=True))"
12816
      ],
12817
      "metadata": {
12818
        "colab": {
12819
          "base_uri": "https://localhost:8080/"
12820
        },
12821
        "id": "qJsffCCTIauA",
12822
        "outputId": "83f61a89-077f-40fe-8d2d-eafa13757b76"
12823
      },
12824
      "execution_count": 57,
12825
      "outputs": [
12826
        {
12827
          "output_type": "stream",
12828
          "name": "stdout",
12829
          "text": [
12830
            "Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.\n",
12831
            "\n",
12832
            "### Instruction:\n",
12833
            "Analyze and explain the legal reasoning behind the judgment in the given case.\n",
12834
            "\n",
12835
            "### Input:\n",
12836
            "Central Inland Water Transport Corporation Ltd. vs Brojo Nath Ganguly & Anr., 1986 AIR 1571, 1986 SCR (2) 278\n",
12837
            "\n",
12838
            "### Response:The Supreme Court in this case applied a broad interpretation of the term 'State' under Article 12 of the Constitution. The court reasoned that a government company undertaking public functions qualifies as 'State' based on factors like government control, public importance of activities etc. This interpretation was based on previous decisions that have defined 'State' under Article 12 broadly to include various agencies and instrumentalities beyond just statutory bodies. The court also applied the principle that unreasonable and arbitrary contractual terms can be struck down under Article 14 of the Constitution. The court found that Rule 9(i) of the service rules, which allowed for termination of service without reason, conferred unfettered power to terminate employment without hearing. This was deemed arbitrary and violative of principles of natural justice and right to equality under Article 14. Furthermore, the court held that the right to life and livelihood under Article 21 is affected by arbitrary termination of employment. The court reasoned that the right to livelihood is an integral part of the right to life, and any arbitrary action that affects a person's livelihood would be a violation of Article 21.In conclusion, the court's legal reasoning was based on a broad interpretation of the term 'State', the application of the principle of equality and natural justice under Article 14, and the protection of the right to life and livelihood under Article 21.\n",
12839
            "\n",
12840
            "### Instruction:\n",
12841
            "Analyze and explain the legal reasoning behind the judgment in the given case.\n",
12842
            "\n",
12843
            "### Input:\n",
12844
            "M. Nagaraj vs Union of India, 2006 AIR 1\n"
12845
          ]
12846
        }
12847
      ]
12848
    }
12849
  ]
12850
}