bloom_3b_4bit_qlora_finetuning_with_dolly.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github"
      },
      "source": [
        "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/lwieske/bloom-and-qlora/blob/main/bloom_3b_4bit_qlora_finetuning_with_dolly.ipynb)\n",
        "[![Open in SageMaker Studio Lab](https://studiolab.sagemaker.aws/studiolab.svg)](https://studiolab.sagemaker.aws/import/github/lwieske/bloom-and-qlora/blob/main/bloom_3b_4bit_qlora_finetuning_with_dolly.ipynb)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tFL1bbDvdyhe"
      },
      "source": [
        "# BLOOM 3B QLoRA 4bit Finetuning with Dolly Dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_UyyuMGnCPjA",
        "outputId": "82932310-bb44-4dee-c671-123f9b86a940"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m265.7/265.7 kB\u001b[0m \u001b[31m4.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m133.9/133.9 kB\u001b[0m \u001b[31m16.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m115.3/115.3 kB\u001b[0m \u001b[31m14.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m134.8/134.8 kB\u001b[0m \u001b[31m16.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m100.9/100.9 kB\u001b[0m \u001b[31m13.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h"
          ]
        }
      ],
      "source": [
        "!pip -q install accelerate bitsandbytes datasets peft transformers trl"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pBOE-h8sbrNK",
        "outputId": "d1015e4a-43ce-47b2-f6e5-b6081f477dc7"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/trl/trainer/ppo_config.py:141: UserWarning: The `optimize_cuda_cache` arguement will be deprecated soon, please use `optimize_device_cache` instead.\n",
            "  warnings.warn(\n"
          ]
        }
      ],
      "source": [
        "import torch\n",
        "import gc\n",
        "\n",
        "from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig, DataCollatorForLanguageModeling, TrainingArguments\n",
        "from trl import SFTTrainer\n",
        "from peft import LoraConfig\n",
        "from datasets import load_dataset\n",
        "\n",
        "from matplotlib.pylab import plt"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "vziwd2UuCYGl"
      },
      "outputs": [],
      "source": [
        "#MODEL      = \"bigscience/bloom-560m\"\n",
        "#MODEL      = \"bigscience/bloom-1b1\"\n",
        "#MODEL      = \"bloom-1b7\"\n",
        "MODEL      = \"bigscience/bloom-3b\"\n",
        "#MODEL      = \"bigscience/bloom-7b1\"\n",
        "\n",
        "DATASET    = \"databricks/databricks-dolly-15k\"\n",
        "\n",
        "TOKENS     = 20\n",
        "\n",
        "EPOCHS     = 10\n",
        "\n",
        "DEVICE_MAP = \"auto\"\n",
        "\n",
        "BATCH_SIZE = 8"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "3h_ydWGf6EAd"
      },
      "outputs": [],
      "source": [
        "bnb_config = BitsAndBytesConfig(\n",
        "                                    load_in_4bit=True,\n",
        "                                    bnb_4bit_use_double_quant=True,\n",
        "                                    bnb_4bit_quant_type=\"nf4\",\n",
        "                                    bnb_4bit_compute_dtype=torch.bfloat16\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 81,
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          ]
        },
        "id": "W2EZhNQ66EAd",
        "outputId": "162b780e-8c98-47c9-e782-a41498dfe778"
      },
      "outputs": [
        {
          "data": {
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              "model_id": "3106123e9e9545fcb3b16cb472dcbf01",
              "version_major": 2,
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            "text/plain": [
              "config.json:   0%|          | 0.00/693 [00:00<?, ?B/s]"
            ]
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        {
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              "model.safetensors:   0%|          | 0.00/6.01G [00:00<?, ?B/s]"
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          },
          "metadata": {},
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      ],
      "source": [
        "pretrained_model = AutoModelForCausalLM.from_pretrained(\n",
        "                                                            MODEL,\n",
        "                                                            quantization_config=bnb_config,\n",
        "                                                            device_map=DEVICE_MAP,\n",
        "                                                            use_cache=False\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WWgahTxOcXEM",
        "outputId": "e13c180f-7ab5-4056-9a8a-572809fde46b"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Mon Dec  4 10:58:20 2023       \n",
            "+-----------------------------------------------------------------------------+\n",
            "| NVIDIA-SMI 525.105.17   Driver Version: 525.105.17   CUDA Version: 12.0     |\n",
            "|-------------------------------+----------------------+----------------------+\n",
            "| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |\n",
            "| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |\n",
            "|                               |                      |               MIG M. |\n",
            "|===============================+======================+======================|\n",
            "|   0  Tesla T4            Off  | 00000000:00:04.0 Off |                    0 |\n",
            "| N/A   51C    P0    28W /  70W |   3289MiB / 15360MiB |      0%      Default |\n",
            "|                               |                      |                  N/A |\n",
            "+-------------------------------+----------------------+----------------------+\n",
            "                                                                               \n",
            "+-----------------------------------------------------------------------------+\n",
            "| Processes:                                                                  |\n",
            "|  GPU   GI   CI        PID   Type   Process name                  GPU Memory |\n",
            "|        ID   ID                                                   Usage      |\n",
            "|=============================================================================|\n",
            "+-----------------------------------------------------------------------------+\n"
          ]
        }
      ],
      "source": [
        "!nvidia-smi"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
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        },
        "id": "aU0awofs84q7",
        "outputId": "f57088c9-b668-40e5-fa2b-0e69417ddc55"
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        {
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              "model_id": "2601eea026a84f779f41f2d68410c603",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "tokenizer_config.json:   0%|          | 0.00/222 [00:00<?, ?B/s]"
            ]
          },
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          "output_type": "display_data"
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        {
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            "text/plain": [
              "tokenizer.json:   0%|          | 0.00/14.5M [00:00<?, ?B/s]"
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          },
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        {
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            "text/plain": [
              "special_tokens_map.json:   0%|          | 0.00/85.0 [00:00<?, ?B/s]"
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          "metadata": {},
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      ],
      "source": [
        "tokenizer = AutoTokenizer.from_pretrained(MODEL)\n",
        "\n",
        "tokenizer.pad_token = tokenizer.eos_token"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FL5L_DcR9ggA"
      },
      "source": [
        "## Prepare Dataset"
      ]
    },
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              "version_minor": 0
            },
            "text/plain": [
              "Generating train split: 0 examples [00:00, ? examples/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "dataset = load_dataset(DATASET)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "id": "fMIsutTL5yA1"
      },
      "outputs": [],
      "source": [
        "def format_prompt(sample):\n",
        "    instruction = f\"### Instruction\\n{sample['instruction']}\"\n",
        "    context = f\"### Context\\n{sample['context']}\" if len(sample[\"context\"]) > 0 else None\n",
        "    response = f\"### Answer\\n{sample['response']}\"\n",
        "    prompt = \"\\n\\n\".join([i for i in [instruction, context, response] if i is not None])\n",
        "    sample[\"prompt\"] = prompt\n",
        "    return sample"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49,
          "referenced_widgets": [
            "c00d7d06b833468096047a580582f8d3",
            "1aabb6c502b84cf9bc5f0221aae0af5f",
            "fad2a4fe62f145dab02e82fc892e0ddd",
            "ca8bc2c6fc5c486e919f408af183692b",
            "10ed4bef32984997888e804da7a7ac15",
            "a6fb17bacdce47d7a974881c840a92f1",
            "9d9671b803cf4eac9aaa9f3c674e9685",
            "89038012af754fb39f6c238796443f32",
            "8ab36fc23b3b47e4ac07ca8947566d1c",
            "5d9dbc9b71bc4a95aac54a29d5bbd042",
            "641444ba036e4854a522c1e905bc4109"
          ]
        },
        "id": "uUfcEn9F5yA1",
        "outputId": "55026f17-7fbc-4442-eaff-954278e15be9"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "c00d7d06b833468096047a580582f8d3",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Map:   0%|          | 0/15011 [00:00<?, ? examples/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "dataset = dataset.map(format_prompt)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "id": "YyXhqPDX5yA2"
      },
      "outputs": [],
      "source": [
        "dataset = dataset.remove_columns(['instruction', 'context', 'response', 'category'])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "id": "QSarK1g25yA2"
      },
      "outputs": [],
      "source": [
        "train_samples = dataset[\"train\"].select(range(0,40))\n",
        "eval_samples  = dataset[\"train\"].select(range(40,50))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gmlZY3fk_9fm",
        "outputId": "24610e1a-30eb-4be5-a625-5413b4aca65d"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "{'prompt': [\"### Instruction\\nWhen did Virgin Australia start operating?\\n\\n### Context\\nVirgin Australia, the trading name of Virgin Australia Airlines Pty Ltd, is an Australian-based airline. It is the largest airline by fleet size to use the Virgin brand. It commenced services on 31 August 2000 as Virgin Blue, with two aircraft on a single route. It suddenly found itself as a major airline in Australia's domestic market after the collapse of Ansett Australia in September 2001. The airline has since grown to directly serve 32 cities in Australia, from hubs in Brisbane, Melbourne and Sydney.\\n\\n### Answer\\nVirgin Australia commenced services on 31 August 2000 as Virgin Blue, with two aircraft on a single route.\"]}\n"
          ]
        }
      ],
      "source": [
        "print(train_samples[:1])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oVPAJsrUAHiJ"
      },
      "source": [
        "## Finetune BLOOM"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "id": "uCalslQFGL7K"
      },
      "outputs": [],
      "source": [
        "lora_config = LoraConfig(\n",
        "                            r=16,\n",
        "                            lora_alpha=16,\n",
        "                            target_modules=[\"query_key_value\"],\n",
        "                            lora_dropout=0.05,\n",
        "                            bias=\"none\",\n",
        "                            task_type=\"CAUSAL_LM\"\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "id": "ND0aJ-t6ARqD"
      },
      "outputs": [],
      "source": [
        "training_args = TrainingArguments(\n",
        "                                    output_dir='./work',\n",
        "                                    auto_find_batch_size=True,\n",
        "                                    learning_rate=2e-4,\n",
        "                                    num_train_epochs=EPOCHS,\n",
        "                                    per_device_train_batch_size=BATCH_SIZE,\n",
        "                                    per_device_eval_batch_size=BATCH_SIZE,\n",
        "                                    logging_strategy=\"epoch\",\n",
        "                                    evaluation_strategy=\"epoch\",\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 135,
          "referenced_widgets": [
            "08fd992afc28415ea9ca2300069f58e8",
            "e3013dd772d04ad79f0fc250276b7e75",
            "e30b88d46f734888be4aafd27fde163b",
            "c113e351b77b492c899f4e34a27c121b",
            "322a2304690d4fc9b165364f692e7156",
            "23cce49e1c0542ce918b69e76f9ee630",
            "3e5d0e9973fa46319b5e05906b67ef95",
            "f2271408774349fbab680d343310431a",
            "4428d664aa9d4a78a4430fd2398b70e9",
            "45491fb4c4ad4cc784df90d146f060bc",
            "b29cde6e66cb4beabc4262d784256362",
            "8b1ca7e7d1f641dd8464be32af89c85c",
            "29ab5a146f044de6b4fad22bde60ae24",
            "081f607761664952ac2718ebb133288a",
            "adc5cd55deb8418d810026eca7454bb5",
            "764d2b4b46d24dcc8da0813f7f929b36",
            "ec0d6e3cf4b14fe880b5e0acef26f04f",
            "2db1ae956c994ba2865a2e7fa02acc8f",
            "bc3a102f52394f8b9aec9913e18c4f67",
            "01e2cc5acdbe429fac3603c02678fe68",
            "133692ce46234ab794f64002924648e8",
            "29657ae179be4c6f9d0b6d22d5780394"
          ]
        },
        "id": "z5NYHqBnGZyF",
        "outputId": "7f64eb1d-d29a-4a75-874a-b57694aa5711"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "08fd992afc28415ea9ca2300069f58e8",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Map:   0%|          | 0/40 [00:00<?, ? examples/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "8b1ca7e7d1f641dd8464be32af89c85c",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Map:   0%|          | 0/10 [00:00<?, ? examples/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/trl/trainer/sft_trainer.py:247: UserWarning: You passed a tokenizer with `padding_side` not equal to `right` to the SFTTrainer. This might lead to some unexpected behaviour due to overflow issues when training a model in half-precision. You might consider adding `tokenizer.padding_side = 'right'` to your code.\n",
            "  warnings.warn(\n"
          ]
        }
      ],
      "source": [
        "trainer = SFTTrainer(\n",
        "                        model=pretrained_model,\n",
        "                        args=training_args,\n",
        "                        eval_dataset=eval_samples,\n",
        "                        train_dataset=train_samples,\n",
        "                        peft_config=lora_config,\n",
        "                        dataset_text_field=\"prompt\",\n",
        "                        tokenizer=tokenizer,\n",
        "                        max_seq_length=256,\n",
        "                        data_collator=DataCollatorForLanguageModeling(tokenizer, mlm=False),\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 456
        },
        "id": "PgEChhbdk1R8",
        "outputId": "31339f42-716a-4279-ee43-693c36d0ba25"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "You're using a BloomTokenizerFast tokenizer. Please note that with a fast tokenizer, using the `__call__` method is faster than using a method to encode the text followed by a call to the `pad` method to get a padded encoding.\n"
          ]
        },
        {
          "data": {
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='100' max='100' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [100/100 06:35, Epoch 10/10]\n",
              "    </div>\n",
              "    <table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              " <tr style=\"text-align: left;\">\n",
              "      <th>Epoch</th>\n",
              "      <th>Training Loss</th>\n",
              "      <th>Validation Loss</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>1</td>\n",
              "      <td>2.749900</td>\n",
              "      <td>2.505706</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2</td>\n",
              "      <td>2.453700</td>\n",
              "      <td>2.351375</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3</td>\n",
              "      <td>2.197800</td>\n",
              "      <td>2.312591</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4</td>\n",
              "      <td>2.089800</td>\n",
              "      <td>2.318103</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>5</td>\n",
              "      <td>2.061200</td>\n",
              "      <td>2.315568</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>6</td>\n",
              "      <td>1.969500</td>\n",
              "      <td>2.334358</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>7</td>\n",
              "      <td>1.914100</td>\n",
              "      <td>2.360816</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>8</td>\n",
              "      <td>1.827100</td>\n",
              "      <td>2.384735</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>9</td>\n",
              "      <td>1.823900</td>\n",
              "      <td>2.404668</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>10</td>\n",
              "      <td>1.759000</td>\n",
              "      <td>2.409641</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "text/plain": [
              "TrainOutput(global_step=100, training_loss=2.0845944023132326, metrics={'train_runtime': 399.4261, 'train_samples_per_second': 1.001, 'train_steps_per_second': 0.25, 'total_flos': 1257975284981760.0, 'train_loss': 2.0845944023132326, 'epoch': 10.0})"
            ]
          },
          "execution_count": 17,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "trainer.train()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "id": "B2vXZWM0ElXk"
      },
      "outputs": [],
      "source": [
        "log_history = trainer.state.log_history"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "id": "FDgvmTAVEm46"
      },
      "outputs": [],
      "source": [
        "training_losses   = [log[\"loss\"]      for log in log_history if 'loss' in log.keys()]\n",
        "validation_losses = [log[\"eval_loss\"] for log in log_history if 'eval_loss' in log.keys()]\n",
        "epochs            = [log[\"epoch\"]     for log in log_history if 'loss' in log.keys()]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 795
        },
        "id": "iItwtsqlEs9h",
        "outputId": "bc27d361-72fe-4d51-f9dd-b625dedeeb71"
      },
      "outputs": [
        {
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            "text/plain": [
              "<Figure size 1600x900 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "plt.rcParams['figure.figsize'] = (16,9)\n",
        "\n",
        "fig, ax = plt.subplots(1)\n",
        "\n",
        "ax.plot(epochs, training_losses,   label='Training Loss')\n",
        "ax.plot(epochs, validation_losses, label='Validation Loss')\n",
        "\n",
        "ax.set_title('Losses')\n",
        "ax.set_xlabel('Epochs')\n",
        "ax.set_ylabel('Loss')\n",
        "\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "m3ZH8Qm6dyhk",
        "outputId": "d8194f0c-12ee-452e-8ee0-8ccf78c1a4af"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Mon Dec  4 11:05:13 2023       \n",
            "+-----------------------------------------------------------------------------+\n",
            "| NVIDIA-SMI 525.105.17   Driver Version: 525.105.17   CUDA Version: 12.0     |\n",
            "|-------------------------------+----------------------+----------------------+\n",
            "| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |\n",
            "| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |\n",
            "|                               |                      |               MIG M. |\n",
            "|===============================+======================+======================|\n",
            "|   0  Tesla T4            Off  | 00000000:00:04.0 Off |                    0 |\n",
            "| N/A   76C    P0    42W /  70W |  14141MiB / 15360MiB |      0%      Default |\n",
            "|                               |                      |                  N/A |\n",
            "+-------------------------------+----------------------+----------------------+\n",
            "                                                                               \n",
            "+-----------------------------------------------------------------------------+\n",
            "| Processes:                                                                  |\n",
            "|  GPU   GI   CI        PID   Type   Process name                  GPU Memory |\n",
            "|        ID   ID                                                   Usage      |\n",
            "|=============================================================================|\n",
            "+-----------------------------------------------------------------------------+\n"
          ]
        }
      ],
      "source": [
        "!nvidia-smi"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "id": "6HkuO9KynjEE"
      },
      "outputs": [],
      "source": [
        "finetuned_model = trainer.model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "glDE0Z4FOSe0",
        "outputId": "1dc8999c-c945-4171-db71-18873d1ddf34"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "50"
            ]
          },
          "execution_count": 23,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "del pretrained_model\n",
        "del trainer\n",
        "del train_samples\n",
        "del eval_samples\n",
        "\n",
        "torch.cuda.empty_cache()\n",
        "\n",
        "gc.collect()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LwfTXjgKcbYr",
        "outputId": "0267ae44-3d3a-44c0-dfd9-ab722facef20"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Mon Dec  4 11:05:14 2023       \n",
            "+-----------------------------------------------------------------------------+\n",
            "| NVIDIA-SMI 525.105.17   Driver Version: 525.105.17   CUDA Version: 12.0     |\n",
            "|-------------------------------+----------------------+----------------------+\n",
            "| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |\n",
            "| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |\n",
            "|                               |                      |               MIG M. |\n",
            "|===============================+======================+======================|\n",
            "|   0  Tesla T4            Off  | 00000000:00:04.0 Off |                    0 |\n",
            "| N/A   76C    P0    46W /  70W |   4783MiB / 15360MiB |     94%      Default |\n",
            "|                               |                      |                  N/A |\n",
            "+-------------------------------+----------------------+----------------------+\n",
            "                                                                               \n",
            "+-----------------------------------------------------------------------------+\n",
            "| Processes:                                                                  |\n",
            "|  GPU   GI   CI        PID   Type   Process name                  GPU Memory |\n",
            "|        ID   ID                                                   Usage      |\n",
            "|=============================================================================|\n",
            "+-----------------------------------------------------------------------------+\n"
          ]
        }
      ],
      "source": [
        "!nvidia-smi"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UOAqEg0mSjHW"
      },
      "source": [
        "## Inference with Finetuned BLOOM"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {
        "id": "Aot-Bq5Tdyhl"
      },
      "outputs": [],
      "source": [
        "def generate_output_tokens(model, input_tokens):\n",
        "    outputs = model.generate(\n",
        "        input_ids=input_tokens[\"input_ids\"],\n",
        "        attention_mask=input_tokens[\"attention_mask\"],\n",
        "        max_new_tokens=TOKENS,\n",
        "        repetition_penalty=1.5,\n",
        "        early_stopping=False,\n",
        "        eos_token_id=tokenizer.eos_token_id,\n",
        "    )\n",
        "    return outputs"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "I_27uvJudf03",
        "outputId": "def55ddb-e1ea-4bbe-b2ce-6fd274423cc9"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "['What is the meaning of life? What does it mean to be human, what makes us unique and why do we exist?\\nThe answer']\n"
          ]
        }
      ],
      "source": [
        "input_words = \"What is the meaning of life?\"\n",
        "\n",
        "input_tokens = tokenizer(input_words, return_tensors=\"pt\").to('cuda')\n",
        "\n",
        "finetuned_output_tokens = generate_output_tokens(finetuned_model, input_tokens)\n",
        "\n",
        "finetuned_output_words = tokenizer.batch_decode(finetuned_output_tokens, skip_special_tokens=True)\n",
        "\n",
        "print(finetuned_output_words)"
      ]
    }
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