{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WE5GJ6s7y0Xo"
      },
      "source": [
        "## Fine-tune large models using 🤗 [`peft`](https://github.com/huggingface/peft) adapters, [`transformers`](https://github.com/huggingface/transformers) & [`bitsandbytes`](https://github.com/TimDettmers/bitsandbytes)\n",
        "\n",
        "In this tutorial we will cover how we can fine-tune large language models using the very recent `peft` library and `bitsandbytes` for loading large models in **4-bit**.\n",
        "The fine-tuning method will rely on a recent method called \"Low Rank Adapters\" (LoRA), instead of fine-tuning the entire model you just have to fine-tune these adapters and load them properly inside the model. We will be using the 4-bit implementation first described in the [QLoRA](https://arxiv.org/abs/2305.14314) paper. \n",
        "After fine-tuning the model you can also share your adapters on the 🤗 Hub and load them very easily. Let's get started!"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TfBzP8gWzkpv"
      },
      "source": [
        "### Install requirements\n",
        "\n",
        "First, run the cells below to install the requirements:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "otj46qRbtpnd",
        "outputId": "7065daf4-22f7-43f0-c5a3-b6e375d0f1bf"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m92.2/92.2 MB\u001b[0m \u001b[31m17.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m474.6/474.6 kB\u001b[0m \u001b[31m35.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m42.2/42.2 kB\u001b[0m \u001b[31m4.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m110.5/110.5 kB\u001b[0m \u001b[31m10.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m212.5/212.5 kB\u001b[0m \u001b[31m9.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m134.3/134.3 kB\u001b[0m \u001b[31m13.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.0/1.0 MB\u001b[0m \u001b[31m38.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m224.5/224.5 kB\u001b[0m \u001b[31m19.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m114.5/114.5 kB\u001b[0m \u001b[31m5.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m268.8/268.8 kB\u001b[0m \u001b[31m1.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m149.6/149.6 kB\u001b[0m \u001b[31m13.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
            "  Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n",
            "  Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m7.8/7.8 MB\u001b[0m \u001b[31m61.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.3/1.3 MB\u001b[0m \u001b[31m58.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Building wheel for transformers (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
            "  Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
            "  Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n",
            "  Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m219.1/219.1 kB\u001b[0m \u001b[31m5.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Building wheel for peft (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
            "  Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
            "  Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n",
            "  Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
            "  Building wheel for accelerate (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n"
          ]
        }
      ],
      "source": [
        "!pip install -q -U bitsandbytes datasets loralib einops\n",
        "!pip install -q -U git+https://github.com/huggingface/transformers.git \n",
        "!pip install -q -U git+https://github.com/huggingface/peft.git\n",
        "!pip install -q -U git+https://github.com/huggingface/accelerate.git\n",
        "!pip install -q datasets"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 145,
          "referenced_widgets": [
            "22ce1d2c79eb47a189584725b171c62c",
            "0d27c9550f384c9fa20117d0ba8b159e",
            "eb12cdf103a8434fbe37e96012a5a122",
            "966a2afe088e4b159f1a727b3aba3484",
            "5551556a5716425ba9866c575e12bd0f",
            "979f1751945c4968b7149a4077617a22",
            "d7eb83c15a0745678a47197e5085cd0b",
            "ea446d518e174324bd18f76735e1d810",
            "da92bf5f939342baa80aaf28fa547535",
            "609bae05cda149198f64c60afff41912",
            "4a34139b612d497b8371b2d8a2dffd0c",
            "7d48e376e30f47fa926ce70c2358aea3",
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            "2e00d4270baf47bdb48985326d13df6e",
            "eabfc0efe41242c99ec08be98c675b3d",
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            "6f66af9cb14e41478b7f389581fcdaac",
            "fcfb8619197142b3be81e5f909657e12",
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            "6e99969ee00a44ffabdc350d5ba03978",
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            "ff5cf47fd0354cf18351074f75a17ffe",
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            "3facb3b445c349f6bce0696b54448d63",
            "5840257e9cf44e95be9e6a9d90f07ed0"
          ]
        },
        "id": "rTdXkcWecz3s",
        "outputId": "12ae8495-baa2-4aff-9a40-d32217c54764"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "22ce1d2c79eb47a189584725b171c62c",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "VBox(children=(HTML(value='<center> <img\\nsrc=https://huggingface.co/front/assets/huggingface_logo-noborder.sv…"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "from huggingface_hub import notebook_login\n",
        "\n",
        "notebook_login()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "I2-xmZCnVDg_",
        "outputId": "b853fc8e-69ce-4f04-dd40-90e7a1ed6025"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Tue May 30 04:38:26 2023       \n",
            "+-----------------------------------------------------------------------------+\n",
            "| NVIDIA-SMI 525.85.12    Driver Version: 525.85.12    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  NVIDIA A100-SXM...  Off  | 00000000:00:04.0 Off |                    0 |\n",
            "| N/A   35C    P0    47W / 400W |      0MiB / 40960MiB |      0%      Default |\n",
            "|                               |                      |             Disabled |\n",
            "+-------------------------------+----------------------+----------------------+\n",
            "                                                                               \n",
            "+-----------------------------------------------------------------------------+\n",
            "| Processes:                                                                  |\n",
            "|  GPU   GI   CI        PID   Type   Process name                  GPU Memory |\n",
            "|        ID   ID                                                   Usage      |\n",
            "|=============================================================================|\n",
            "|  No running processes found                                                 |\n",
            "+-----------------------------------------------------------------------------+\n"
          ]
        }
      ],
      "source": [
        "!nvidia-smi"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "koRYhzEOSyb0"
      },
      "outputs": [],
      "source": [
        "import os\n",
        "\n",
        "os.environ[\"CUDA_VISIBLE_DEVICES\"] = \"0\"\n",
        "\n",
        "import torch\n",
        "import torch.nn as nn\n",
        "import bitsandbytes as bnb\n",
        "from transformers import AutoTokenizer, AutoConfig, AutoModelForCausalLM, BitsAndBytesConfig"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "p8vHtolPVZJj",
        "outputId": "275a43be-ce6c-4009-84a5-6747592769f4"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "{0: '37GB'}"
            ]
          },
          "execution_count": 6,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "free_in_GB = int(torch.cuda.mem_get_info()[0] / 1024**3)\n",
        "max_memory = f\"{free_in_GB-2}GB\"\n",
        "\n",
        "n_gpus = torch.cuda.device_count()\n",
        "max_memory = {i: max_memory for i in range(n_gpus)}\n",
        "max_memory"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FOtwYRI3zzXI"
      },
      "source": [
        "### Model loading"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 752,
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        "id": "cg3fiQOvmI3Q",
        "outputId": "a9dfda1a-c2ef-4e4f-b4fe-2997d603b8f1"
      },
      "outputs": [
        {
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              "model_id": "50c3a02c18cc4df4aaaa54640c0dfdd0",
              "version_major": 2,
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        {
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          "text": [
            "A new version of the following files was downloaded from https://huggingface.co/tiiuae/falcon-40b:\n",
            "- configuration_RW.py\n",
            ". Make sure to double-check they do not contain any added malicious code. To avoid downloading new versions of the code file, you can pin a revision.\n"
          ]
        },
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        },
        {
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          "text": [
            "A new version of the following files was downloaded from https://huggingface.co/tiiuae/falcon-40b:\n",
            "- modelling_RW.py\n",
            ". Make sure to double-check they do not contain any added malicious code. To avoid downloading new versions of the code file, you can pin a revision.\n"
          ]
        },
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              "model_id": "d6879fcc8a3049d8afd1446b15136170",
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          },
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        },
        {
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          },
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        },
        {
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              "model_id": "0411a08bb87748c0a59224d70f473da6",
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            ]
          },
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        },
        {
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      ],
      "source": [
        "model_id = \"tiiuae/falcon-40b\"\n",
        "\n",
        "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",
        ")\n",
        "\n",
        "model = AutoModelForCausalLM.from_pretrained(\n",
        "    model_id,\n",
        "    quantization_config=bnb_config,\n",
        "    device_map={\"\":0},\n",
        "    trust_remote_code=True,\n",
        ")\n",
        "\n",
        "tokenizer = AutoTokenizer.from_pretrained(model_id)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9fTSZntA1iUG"
      },
      "source": [
        "### Prepare model for training\n",
        "\n",
        "Some pre-processing needs to be done before training such an int8 model using `peft`, therefore let's import an utiliy function `prepare_model_for_kbit_training` that will: \n",
        "- Casts all the non `nf4` modules to full precision (`fp32`) for stability\n",
        "- Add a `forward_hook` to the input embedding layer to enable gradient computation of the input hidden states\n",
        "- Enable gradient checkpointing for more memory-efficient training"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "id": "T-gy-LxM0yAi"
      },
      "outputs": [],
      "source": [
        "from peft import prepare_model_for_kbit_training\n",
        "\n",
        "model.gradient_checkpointing_enable()\n",
        "model = prepare_model_for_kbit_training(model)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KwOTr7B3NlM3"
      },
      "source": [
        "### Apply LoRA\n",
        "\n",
        "Here comes the magic with `peft`! Let's load a `PeftModel` and specify that we are going to use low-rank adapters (LoRA) using `get_peft_model` utility function from `peft`."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "id": "4W1j6lxaNnxC"
      },
      "outputs": [],
      "source": [
        "def print_trainable_parameters(model):\n",
        "    \"\"\"\n",
        "    Prints the number of trainable parameters in the model.\n",
        "    \"\"\"\n",
        "    trainable_params = 0\n",
        "    all_param = 0\n",
        "    for _, param in model.named_parameters():\n",
        "        all_param += param.numel()\n",
        "        if param.requires_grad:\n",
        "            trainable_params += param.numel()\n",
        "    print(\n",
        "        f\"trainable params: {trainable_params} || all params: {all_param} || trainable%: {100 * trainable_params / all_param}\"\n",
        "    )"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4iwHGzKBN6wk",
        "outputId": "bcf8ff27-3446-464c-a278-5021642d0e91"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "trainable params: 8355840 || all params: 20927332352 || trainable%: 0.03992787928940901\n"
          ]
        }
      ],
      "source": [
        "from peft import LoraConfig, get_peft_model\n",
        "\n",
        "config = LoraConfig(\n",
        "    r=8, \n",
        "    lora_alpha=32, \n",
        "    target_modules=[\"query_key_value\"], \n",
        "    lora_dropout=0.05, \n",
        "    bias=\"none\", \n",
        "    task_type=\"CAUSAL_LM\"\n",
        ")\n",
        "\n",
        "model = get_peft_model(model, config)\n",
        "print_trainable_parameters(model)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zdnQamMZTEXq",
        "outputId": "4bed9b00-afc6-4237-ae55-0447026c6e0e"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "torch.float32 543014912 0.02594764124095849\n",
            "torch.uint8 20384317440 0.9740523587590415\n"
          ]
        }
      ],
      "source": [
        "# Verifying the datatypes.\n",
        "dtypes = {}\n",
        "for _, p in model.named_parameters():\n",
        "    dtype = p.dtype\n",
        "    if dtype not in dtypes:\n",
        "        dtypes[dtype] = 0\n",
        "    dtypes[dtype] += p.numel()\n",
        "total = 0\n",
        "for k, v in dtypes.items():\n",
        "    total += v\n",
        "for k, v in dtypes.items():\n",
        "    print(k, v, v / total)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QdjWif4CVXR6"
      },
      "source": [
        "### Training"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "id": "AwYPCVsRTO-Y"
      },
      "outputs": [],
      "source": [
        "import transformers\n",
        "from datasets import load_dataset, Dataset\n",
        "import pandas as pd\n",
        "import numpy as np"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "id": "-wNSUnrBGquS"
      },
      "outputs": [],
      "source": [
        "# extract, load, and transform OpenAssistant/oasst1 chatbot dataset\n",
        "\n",
        "# set some pandas options to make the output more readable\n",
        "pd.set_option(\"display.max_rows\", 500)\n",
        "pd.set_option(\"display.max_columns\", 500)\n",
        "pd.set_option(\"display.width\", 1000)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
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        },
        "id": "2BiQEL-uHK69",
        "outputId": "6fb23806-bb2b-4a75-b95f-0783baf39a9a"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "5efe5e8835734993b374b6e10ac9e909",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Downloading readme:   0%|          | 0.00/10.2k [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Downloading and preparing dataset None/None to /root/.cache/huggingface/datasets/OpenAssistant___parquet/OpenAssistant--oasst1-2960c57d7e52ab15/0.0.0/2a3b91fbd88a2c90d1dbbb32b460cf621d31bd5b05b934492fdef7d8d6f236ec...\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "f710ae297f074ff695c6632c3727216e",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Downloading data files:   0%|          | 0/2 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "df07861155954e5f993300af1ac91e46",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Downloading data:   0%|          | 0.00/39.5M [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "cdc1dacd383941da943d7849e8fa033b",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Downloading data:   0%|          | 0.00/2.08M [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "524712a3690a4854a6696fae37eefa87",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Extracting data files:   0%|          | 0/2 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "fedb7ec754ba4485b6adf6b721b97580",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Generating train split:   0%|          | 0/84437 [00:00<?, ? examples/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "6f101f9188504ffe9463534548f1eabe",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Generating validation split:   0%|          | 0/4401 [00:00<?, ? examples/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Dataset parquet downloaded and prepared to /root/.cache/huggingface/datasets/OpenAssistant___parquet/OpenAssistant--oasst1-2960c57d7e52ab15/0.0.0/2a3b91fbd88a2c90d1dbbb32b460cf621d31bd5b05b934492fdef7d8d6f236ec. Subsequent calls will reuse this data.\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "7f1dc810576844bdaa97ff7aa1f2bfde",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "  0%|          | 0/2 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# load dataset from huggingface datasets\n",
        "ds = load_dataset(\"OpenAssistant/oasst1\")\n",
        "# lets convert the train dataset to a pandas df\n",
        "df = ds[\"train\"].to_pandas()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "id": "0DhbpL1Ffkko"
      },
      "outputs": [],
      "source": [
        "# lets grab the message trees to train on \n",
        "message_tree_ids = np.unique(np.array(df[\"message_tree_id\"]))\n",
        "messages = {}\n",
        "messages['message_tree_id'] = []\n",
        "messages['message_tree_text'] = []\n",
        "\n",
        "for message_tree_id in message_tree_ids:\n",
        "    try:\n",
        "        # look at all data for this message tree\n",
        "        one_message_tree = df.query(f\"message_tree_id == '{message_tree_id}'\").sort_values(\"created_date\")\n",
        "        text = \"\"\n",
        "    \n",
        "        # root message\n",
        "        text += \"<human>: \" + one_message_tree.iloc[0].text\n",
        "        # find root message's children\n",
        "        children = one_message_tree[one_message_tree.parent_id == one_message_tree.iloc[0].message_id]\n",
        "        # find root message's top ranked child:\n",
        "        child = children[children['rank'] == 0.0]\n",
        "        text += '\\n' + \"<bot>: \" + child.iloc[0].text\n",
        "    \n",
        "        # proceed through rest of the above message tree until completion\n",
        "        flag=True\n",
        "        while flag:\n",
        "            try:\n",
        "                # find next prompt\n",
        "                children = one_message_tree[one_message_tree.parent_id == child.message_id.iloc[0]]\n",
        "                children.index\n",
        "                one_message_tree.loc[children.index].iloc[0].role\n",
        "                text += '\\n' + \"<human>: \" + one_message_tree.loc[children.index].iloc[0].text\n",
        "    \n",
        "                # find next children\n",
        "                children = one_message_tree[one_message_tree.parent_id == one_message_tree.loc[children.index].iloc[0].message_id]\n",
        "                children\n",
        "                # find top ranked child:\n",
        "                child = children[children['rank'] == 0.0]\n",
        "                text += '\\n' + \"<bot>: \" + child.iloc[0].text\n",
        "            except:\n",
        "                flag=False\n",
        "    \n",
        "        messages['message_tree_id'].append(message_tree_id)\n",
        "        messages['message_tree_text'].append(text)\n",
        "\n",
        "    except IndexError:\n",
        "        pass\n",
        "\n",
        "message_df = pd.DataFrame.from_dict(messages)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dCBMejldfknP",
        "outputId": "4503129e-3f14-4c8a-f974-69896943b9ac"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "<human>: What is the capital of Japan?\n",
            "<bot>: Tokyo is the capital of Japan.\n",
            "<human>: And what is the capital of the Moon?\n",
            "<bot>: Since the Moon is not currently inhabited by humans, there is currently no capital of the Moon.\n",
            "\n",
            "\n",
            "message_tree_id                   01505c0f-2d68-4206-acfc-7ab39033c75a\n",
            "message_tree_text    <human>: What is the capital of Japan?\\n<bot>:...\n",
            "Name: 41, dtype: object\n"
          ]
        }
      ],
      "source": [
        "# check some random messages\n",
        "\n",
        "i=41\n",
        "print(message_df.message_tree_text.iloc[i])\n",
        "print('\\n')\n",
        "print(message_df.iloc[i])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "b1gqwFxZUtbo",
        "outputId": "b8d00339-2b01-49e6-bcc6-0dbd9006c208"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "Dataset({\n",
              "    features: ['message_tree_id', 'message_tree_text'],\n",
              "    num_rows: 9823\n",
              "})"
            ]
          },
          "execution_count": 19,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# convert back to HF datasets format\n",
        "\n",
        "data = Dataset.from_pandas(message_df)\n",
        "data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 88,
          "referenced_widgets": [
            "58b9a27175944cf9bab8c7de50059185",
            "1d3238ce51e2474fbe3607e6e6709c74",
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            "afa9cf9b8b9c4be7b63b1e7f0d6f6e4d"
          ]
        },
        "id": "oaBHh5yWTkQm",
        "outputId": "764a1bbe-6d2f-44ae-d484-fb15686c1f2b"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "58b9a27175944cf9bab8c7de50059185",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Map:   0%|          | 0/9823 [00:00<?, ? examples/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "text/plain": [
              "Dataset({\n",
              "    features: ['message_tree_id', 'message_tree_text', 'input_ids', 'token_type_ids', 'attention_mask'],\n",
              "    num_rows: 9823\n",
              "})"
            ]
          },
          "execution_count": 20,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "tokenizer.pad_token = tokenizer.eos_token\n",
        "\n",
        "data = data.map(lambda samples: tokenizer(samples[\"message_tree_text\"], padding=True, truncation=True,), batched=True)\n",
        "data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "AQ_HCYruWIHU",
        "outputId": "4acbc736-3602-4084-de58-982aa978d967"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "You're using a PreTrainedTokenizerFast tokenizer. Please note that with a fast tokenizer, using the `__call__` method is faster than using a method to encode the text followed by a call to the `pad` method to get a padded encoding.\n"
          ]
        },
        {
          "data": {
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='1052' max='1228' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [1052/1228 9:08:18 < 1:31:54, 0.03 it/s, Epoch 0.86/1]\n",
              "    </div>\n",
              "    <table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              " <tr style=\"text-align: left;\">\n",
              "      <th>Step</th>\n",
              "      <th>Training Loss</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>25</td>\n",
              "      <td>1.370500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>50</td>\n",
              "      <td>1.267300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>75</td>\n",
              "      <td>1.148100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>100</td>\n",
              "      <td>1.147200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>125</td>\n",
              "      <td>1.238700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>150</td>\n",
              "      <td>1.097000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>175</td>\n",
              "      <td>1.073900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>200</td>\n",
              "      <td>1.084300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>225</td>\n",
              "      <td>1.087400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>250</td>\n",
              "      <td>1.078400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>275</td>\n",
              "      <td>1.163900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>300</td>\n",
              "      <td>1.156400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>325</td>\n",
              "      <td>1.049400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>350</td>\n",
              "      <td>1.116700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>375</td>\n",
              "      <td>1.099400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>400</td>\n",
              "      <td>1.090400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>425</td>\n",
              "      <td>1.150900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>450</td>\n",
              "      <td>1.106200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>475</td>\n",
              "      <td>1.086200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>500</td>\n",
              "      <td>1.055100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>525</td>\n",
              "      <td>1.123800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>550</td>\n",
              "      <td>1.041500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>575</td>\n",
              "      <td>1.082700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>600</td>\n",
              "      <td>1.046000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>625</td>\n",
              "      <td>1.064200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>650</td>\n",
              "      <td>1.097300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>675</td>\n",
              "      <td>1.096000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>700</td>\n",
              "      <td>1.031900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>725</td>\n",
              "      <td>1.101600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>750</td>\n",
              "      <td>1.075600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>775</td>\n",
              "      <td>1.021200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>800</td>\n",
              "      <td>1.065200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>825</td>\n",
              "      <td>1.071500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>850</td>\n",
              "      <td>1.073700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>875</td>\n",
              "      <td>1.061300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>900</td>\n",
              "      <td>1.047800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>925</td>\n",
              "      <td>1.108700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>950</td>\n",
              "      <td>1.074400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>975</td>\n",
              "      <td>1.096200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1000</td>\n",
              "      <td>1.051100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1025</td>\n",
              "      <td>1.097900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1050</td>\n",
              "      <td>1.078400</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>"
            ],
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              "<IPython.core.display.HTML object>"
            ]
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            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='1101' max='1228' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [1101/1228 9:33:52 < 1:06:19, 0.03 it/s, Epoch 0.90/1]\n",
              "    </div>\n",
              "    <table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              " <tr style=\"text-align: left;\">\n",
              "      <th>Step</th>\n",
              "      <th>Training Loss</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>25</td>\n",
              "      <td>1.370500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>50</td>\n",
              "      <td>1.267300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>75</td>\n",
              "      <td>1.148100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>100</td>\n",
              "      <td>1.147200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>125</td>\n",
              "      <td>1.238700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>150</td>\n",
              "      <td>1.097000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>175</td>\n",
              "      <td>1.073900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>200</td>\n",
              "      <td>1.084300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>225</td>\n",
              "      <td>1.087400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>250</td>\n",
              "      <td>1.078400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>275</td>\n",
              "      <td>1.163900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>300</td>\n",
              "      <td>1.156400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>325</td>\n",
              "      <td>1.049400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>350</td>\n",
              "      <td>1.116700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>375</td>\n",
              "      <td>1.099400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>400</td>\n",
              "      <td>1.090400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>425</td>\n",
              "      <td>1.150900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>450</td>\n",
              "      <td>1.106200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>475</td>\n",
              "      <td>1.086200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>500</td>\n",
              "      <td>1.055100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>525</td>\n",
              "      <td>1.123800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>550</td>\n",
              "      <td>1.041500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>575</td>\n",
              "      <td>1.082700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>600</td>\n",
              "      <td>1.046000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>625</td>\n",
              "      <td>1.064200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>650</td>\n",
              "      <td>1.097300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>675</td>\n",
              "      <td>1.096000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>700</td>\n",
              "      <td>1.031900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>725</td>\n",
              "      <td>1.101600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>750</td>\n",
              "      <td>1.075600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>775</td>\n",
              "      <td>1.021200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>800</td>\n",
              "      <td>1.065200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>825</td>\n",
              "      <td>1.071500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>850</td>\n",
              "      <td>1.073700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>875</td>\n",
              "      <td>1.061300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>900</td>\n",
              "      <td>1.047800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>925</td>\n",
              "      <td>1.108700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>950</td>\n",
              "      <td>1.074400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>975</td>\n",
              "      <td>1.096200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1000</td>\n",
              "      <td>1.051100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1025</td>\n",
              "      <td>1.097900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1050</td>\n",
              "      <td>1.078400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1075</td>\n",
              "      <td>1.039800</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>"
            ],
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              "<IPython.core.display.HTML object>"
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            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #800000; text-decoration-color: #800000\">╭─────────────────────────────── </span><span style=\"color: #800000; text-decoration-color: #800000; font-weight: bold\">Traceback </span><span style=\"color: #bf7f7f; text-decoration-color: #bf7f7f; font-weight: bold\">(most recent call last)</span><span style=\"color: #800000; text-decoration-color: #800000\"> ────────────────────────────────╮</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span> in <span style=\"color: #00ff00; text-decoration-color: #00ff00\">&lt;cell line: 23&gt;</span>:<span style=\"color: #0000ff; text-decoration-color: #0000ff\">23</span>                                                                            <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>                                                                                                  <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span> <span style=\"color: #bfbf7f; text-decoration-color: #bfbf7f\">/usr/local/lib/python3.10/dist-packages/transformers/</span><span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\">trainer.py</span>:<span style=\"color: #0000ff; text-decoration-color: #0000ff\">1696</span> in <span style=\"color: #00ff00; text-decoration-color: #00ff00\">train</span>                    <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>                                                                                                  <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">1693 │   │   </span>inner_training_loop = find_executable_batch_size(                                 <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">1694 │   │   │   </span><span style=\"color: #00ffff; text-decoration-color: #00ffff\">self</span>._inner_training_loop, <span style=\"color: #00ffff; text-decoration-color: #00ffff\">self</span>._train_batch_size, args.auto_find_batch_size  <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">1695 │   │   </span>)                                                                                 <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span> <span style=\"color: #800000; text-decoration-color: #800000\">❱ </span>1696 <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   </span><span style=\"color: #0000ff; text-decoration-color: #0000ff\">return</span> inner_training_loop(                                                       <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">1697 │   │   │   </span>args=args,                                                                    <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">1698 │   │   │   </span>resume_from_checkpoint=resume_from_checkpoint,                                <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">1699 │   │   │   </span>trial=trial,                                                                  <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>                                                                                                  <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span> <span style=\"color: #bfbf7f; text-decoration-color: #bfbf7f\">/usr/local/lib/python3.10/dist-packages/accelerate/utils/</span><span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\">memory.py</span>:<span style=\"color: #0000ff; text-decoration-color: #0000ff\">132</span> in <span style=\"color: #00ff00; text-decoration-color: #00ff00\">decorator</span>              <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>                                                                                                  <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">129 │   │   │   </span><span style=\"color: #0000ff; text-decoration-color: #0000ff\">if</span> batch_size == <span style=\"color: #0000ff; text-decoration-color: #0000ff\">0</span>:                                                            <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">130 │   │   │   │   </span><span style=\"color: #0000ff; text-decoration-color: #0000ff\">raise</span> <span style=\"color: #00ffff; text-decoration-color: #00ffff\">RuntimeError</span>(<span style=\"color: #808000; text-decoration-color: #808000\">\"No executable batch size found, reached zero.\"</span>)        <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">131 │   │   │   </span><span style=\"color: #0000ff; text-decoration-color: #0000ff\">try</span>:                                                                           <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span> <span style=\"color: #800000; text-decoration-color: #800000\">❱ </span>132 <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   │   │   </span><span style=\"color: #0000ff; text-decoration-color: #0000ff\">return</span> function(batch_size, *args, **kwargs)                               <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">133 │   │   │   </span><span style=\"color: #0000ff; text-decoration-color: #0000ff\">except</span> <span style=\"color: #00ffff; text-decoration-color: #00ffff\">Exception</span> <span style=\"color: #0000ff; text-decoration-color: #0000ff\">as</span> e:                                                         <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">134 │   │   │   │   </span><span style=\"color: #0000ff; text-decoration-color: #0000ff\">if</span> should_reduce_batch_size(e):                                            <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">135 │   │   │   │   │   </span>gc.collect()                                                           <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>                                                                                                  <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span> <span style=\"color: #bfbf7f; text-decoration-color: #bfbf7f\">/usr/local/lib/python3.10/dist-packages/transformers/</span><span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\">trainer.py</span>:<span style=\"color: #0000ff; text-decoration-color: #0000ff\">2052</span> in <span style=\"color: #00ff00; text-decoration-color: #00ff00\">_inner_training_loop</span>     <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>                                                                                                  <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">2049 │   │   │   │   │   </span><span style=\"color: #00ffff; text-decoration-color: #00ffff\">self</span>.state.epoch = epoch + (step + <span style=\"color: #0000ff; text-decoration-color: #0000ff\">1</span> + steps_skipped) / steps_in_epo  <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">2050 │   │   │   │   │   </span><span style=\"color: #00ffff; text-decoration-color: #00ffff\">self</span>.control = <span style=\"color: #00ffff; text-decoration-color: #00ffff\">self</span>.callback_handler.on_step_end(args, <span style=\"color: #00ffff; text-decoration-color: #00ffff\">self</span>.state, <span style=\"color: #00ffff; text-decoration-color: #00ffff\">s</span>  <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">2051 │   │   │   │   │   </span>                                                                      <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span> <span style=\"color: #800000; text-decoration-color: #800000\">❱ </span>2052 <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   │   │   │   </span><span style=\"color: #00ffff; text-decoration-color: #00ffff\">self</span>._maybe_log_save_evaluate(tr_loss, model, trial, epoch, ignore_k  <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">2053 │   │   │   │   </span><span style=\"color: #0000ff; text-decoration-color: #0000ff\">else</span>:                                                                     <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">2054 │   │   │   │   │   </span><span style=\"color: #00ffff; text-decoration-color: #00ffff\">self</span>.control = <span style=\"color: #00ffff; text-decoration-color: #00ffff\">self</span>.callback_handler.on_substep_end(args, <span style=\"color: #00ffff; text-decoration-color: #00ffff\">self</span>.state  <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">2055 </span>                                                                                          <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>                                                                                                  <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span> <span style=\"color: #bfbf7f; text-decoration-color: #bfbf7f\">/usr/local/lib/python3.10/dist-packages/transformers/</span><span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\">trainer.py</span>:<span style=\"color: #0000ff; text-decoration-color: #0000ff\">2338</span> in <span style=\"color: #00ff00; text-decoration-color: #00ff00\">_maybe_log_save_evaluate</span> <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>                                                                                                  <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">2335 │   │   │   </span><span style=\"color: #00ffff; text-decoration-color: #00ffff\">self</span>._globalstep_last_logged = <span style=\"color: #00ffff; text-decoration-color: #00ffff\">self</span>.state.global_step                         <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">2336 │   │   │   </span><span style=\"color: #00ffff; text-decoration-color: #00ffff\">self</span>.store_flos()                                                             <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">2337 │   │   │   </span>                                                                              <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span> <span style=\"color: #800000; text-decoration-color: #800000\">❱ </span>2338 <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   │   </span><span style=\"color: #00ffff; text-decoration-color: #00ffff\">self</span>.log(logs)                                                                <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">2339 │   │   </span>                                                                                  <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">2340 │   │   </span>metrics = <span style=\"color: #0000ff; text-decoration-color: #0000ff\">None</span>                                                                    <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">2341 │   │   </span><span style=\"color: #0000ff; text-decoration-color: #0000ff\">if</span> <span style=\"color: #00ffff; text-decoration-color: #00ffff\">self</span>.control.should_evaluate:                                                  <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>                                                                                                  <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span> <span style=\"color: #bfbf7f; text-decoration-color: #bfbf7f\">/usr/local/lib/python3.10/dist-packages/transformers/</span><span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\">trainer.py</span>:<span style=\"color: #0000ff; text-decoration-color: #0000ff\">2700</span> in <span style=\"color: #00ff00; text-decoration-color: #00ff00\">log</span>                      <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>                                                                                                  <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">2697 │   │   </span>                                                                                  <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">2698 │   │   </span>output = {**logs, **{<span style=\"color: #808000; text-decoration-color: #808000\">\"step\"</span>: <span style=\"color: #00ffff; text-decoration-color: #00ffff\">self</span>.state.global_step}}                             <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">2699 │   │   </span><span style=\"color: #00ffff; text-decoration-color: #00ffff\">self</span>.state.log_history.append(output)                                             <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span> <span style=\"color: #800000; text-decoration-color: #800000\">❱ </span>2700 <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   </span><span style=\"color: #00ffff; text-decoration-color: #00ffff\">self</span>.control = <span style=\"color: #00ffff; text-decoration-color: #00ffff\">self</span>.callback_handler.on_log(<span style=\"color: #00ffff; text-decoration-color: #00ffff\">self</span>.args, <span style=\"color: #00ffff; text-decoration-color: #00ffff\">self</span>.state, <span style=\"color: #00ffff; text-decoration-color: #00ffff\">self</span>.control,  <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">2701 │   </span>                                                                                      <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">2702 │   </span><span style=\"color: #0000ff; text-decoration-color: #0000ff\">def</span> <span style=\"color: #00ff00; text-decoration-color: #00ff00\">_prepare_input</span>(<span style=\"color: #00ffff; text-decoration-color: #00ffff\">self</span>, data: Union[torch.Tensor, Any]) -&gt; Union[torch.Tensor, Any]  <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">2703 </span><span style=\"color: #bfbfbf; text-decoration-color: #bfbfbf\">│   │   </span><span style=\"color: #808000; text-decoration-color: #808000\">\"\"\"</span>                                                                               <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>                                                                                                  <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span> <span style=\"color: #bfbf7f; text-decoration-color: #bfbf7f\">/usr/local/lib/python3.10/dist-packages/transformers/</span><span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\">trainer_callback.py</span>:<span style=\"color: #0000ff; text-decoration-color: #0000ff\">390</span> in <span style=\"color: #00ff00; text-decoration-color: #00ff00\">on_log</span>           <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>                                                                                                  <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">387 │   </span>                                                                                       <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">388 │   </span><span style=\"color: #0000ff; text-decoration-color: #0000ff\">def</span> <span style=\"color: #00ff00; text-decoration-color: #00ff00\">on_log</span>(<span style=\"color: #00ffff; text-decoration-color: #00ffff\">self</span>, args: TrainingArguments, state: TrainerState, control: TrainerContr   <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">389 │   │   </span>control.should_log = <span style=\"color: #0000ff; text-decoration-color: #0000ff\">False</span>                                                         <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span> <span style=\"color: #800000; text-decoration-color: #800000\">❱ </span>390 <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   </span><span style=\"color: #0000ff; text-decoration-color: #0000ff\">return</span> <span style=\"color: #00ffff; text-decoration-color: #00ffff\">self</span>.call_event(<span style=\"color: #808000; text-decoration-color: #808000\">\"on_log\"</span>, args, state, control, logs=logs)                  <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">391 │   </span>                                                                                       <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">392 │   </span><span style=\"color: #0000ff; text-decoration-color: #0000ff\">def</span> <span style=\"color: #00ff00; text-decoration-color: #00ff00\">on_prediction_step</span>(<span style=\"color: #00ffff; text-decoration-color: #00ffff\">self</span>, args: TrainingArguments, state: TrainerState, control:    <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">393 │   │   </span><span style=\"color: #0000ff; text-decoration-color: #0000ff\">return</span> <span style=\"color: #00ffff; text-decoration-color: #00ffff\">self</span>.call_event(<span style=\"color: #808000; text-decoration-color: #808000\">\"on_prediction_step\"</span>, args, state, control)                 <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>                                                                                                  <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span> <span style=\"color: #bfbf7f; text-decoration-color: #bfbf7f\">/usr/local/lib/python3.10/dist-packages/transformers/</span><span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\">trainer_callback.py</span>:<span style=\"color: #0000ff; text-decoration-color: #0000ff\">397</span> in <span style=\"color: #00ff00; text-decoration-color: #00ff00\">call_event</span>       <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>                                                                                                  <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">394 │   </span>                                                                                       <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">395 │   </span><span style=\"color: #0000ff; text-decoration-color: #0000ff\">def</span> <span style=\"color: #00ff00; text-decoration-color: #00ff00\">call_event</span>(<span style=\"color: #00ffff; text-decoration-color: #00ffff\">self</span>, event, args, state, control, **kwargs):                           <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">396 │   │   </span><span style=\"color: #0000ff; text-decoration-color: #0000ff\">for</span> callback <span style=\"color: #ff00ff; text-decoration-color: #ff00ff\">in</span> <span style=\"color: #00ffff; text-decoration-color: #00ffff\">self</span>.callbacks:                                                    <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span> <span style=\"color: #800000; text-decoration-color: #800000\">❱ </span>397 <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   │   </span>result = <span style=\"color: #00ffff; text-decoration-color: #00ffff\">getattr</span>(callback, event)(                                             <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">398 │   │   │   │   </span>args,                                                                      <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">399 │   │   │   │   </span>state,                                                                     <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">400 │   │   │   │   </span>control,                                                                   <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>                                                                                                  <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span> <span style=\"color: #bfbf7f; text-decoration-color: #bfbf7f\">/usr/local/lib/python3.10/dist-packages/transformers/</span><span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\">integrations.py</span>:<span style=\"color: #0000ff; text-decoration-color: #0000ff\">655</span> in <span style=\"color: #00ff00; text-decoration-color: #00ff00\">on_log</span>               <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>                                                                                                  <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\"> 652 │   │   │   │   │   │   </span><span style=\"color: #808000; text-decoration-color: #808000\">\"This invocation of Tensorboard's writer.add_scalar() \"</span>           <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\"> 653 │   │   │   │   │   │   </span><span style=\"color: #808000; text-decoration-color: #808000\">\"is incorrect so we dropped this attribute.\"</span>                      <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\"> 654 │   │   │   │   │   </span>)                                                                     <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span> <span style=\"color: #800000; text-decoration-color: #800000\">❱ </span> 655 <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   │   </span><span style=\"color: #00ffff; text-decoration-color: #00ffff\">self</span>.tb_writer.flush()                                                        <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\"> 656 │   </span>                                                                                      <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\"> 657 │   </span><span style=\"color: #0000ff; text-decoration-color: #0000ff\">def</span> <span style=\"color: #00ff00; text-decoration-color: #00ff00\">on_train_end</span>(<span style=\"color: #00ffff; text-decoration-color: #00ffff\">self</span>, args, state, control, **kwargs):                               <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\"> 658 │   │   </span><span style=\"color: #0000ff; text-decoration-color: #0000ff\">if</span> <span style=\"color: #00ffff; text-decoration-color: #00ffff\">self</span>.tb_writer:                                                                <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>                                                                                                  <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span> <span style=\"color: #bfbf7f; text-decoration-color: #bfbf7f\">/usr/local/lib/python3.10/dist-packages/torch/utils/tensorboard/</span><span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\">writer.py</span>:<span style=\"color: #0000ff; text-decoration-color: #0000ff\">1200</span> in <span style=\"color: #00ff00; text-decoration-color: #00ff00\">flush</span>          <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>                                                                                                  <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">1197 │   │   </span><span style=\"color: #0000ff; text-decoration-color: #0000ff\">if</span> <span style=\"color: #00ffff; text-decoration-color: #00ffff\">self</span>.all_writers <span style=\"color: #ff00ff; text-decoration-color: #ff00ff\">is</span> <span style=\"color: #0000ff; text-decoration-color: #0000ff\">None</span>:                                                      <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">1198 │   │   │   </span><span style=\"color: #0000ff; text-decoration-color: #0000ff\">return</span>                                                                        <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">1199 │   │   </span><span style=\"color: #0000ff; text-decoration-color: #0000ff\">for</span> writer <span style=\"color: #ff00ff; text-decoration-color: #ff00ff\">in</span> <span style=\"color: #00ffff; text-decoration-color: #00ffff\">self</span>.all_writers.values():                                          <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span> <span style=\"color: #800000; text-decoration-color: #800000\">❱ </span>1200 <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   │   </span>writer.flush()                                                                <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">1201 │   </span>                                                                                      <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">1202 │   </span><span style=\"color: #0000ff; text-decoration-color: #0000ff\">def</span> <span style=\"color: #00ff00; text-decoration-color: #00ff00\">close</span>(<span style=\"color: #00ffff; text-decoration-color: #00ffff\">self</span>):                                                                      <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">1203 │   │   </span><span style=\"color: #0000ff; text-decoration-color: #0000ff\">if</span> <span style=\"color: #00ffff; text-decoration-color: #00ffff\">self</span>.all_writers <span style=\"color: #ff00ff; text-decoration-color: #ff00ff\">is</span> <span style=\"color: #0000ff; text-decoration-color: #0000ff\">None</span>:                                                      <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>                                                                                                  <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span> <span style=\"color: #bfbf7f; text-decoration-color: #bfbf7f\">/usr/local/lib/python3.10/dist-packages/torch/utils/tensorboard/</span><span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\">writer.py</span>:<span style=\"color: #0000ff; text-decoration-color: #0000ff\">150</span> in <span style=\"color: #00ff00; text-decoration-color: #00ff00\">flush</span>           <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>                                                                                                  <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\"> 147 </span><span style=\"color: #bfbf7f; text-decoration-color: #bfbf7f\">│   │   </span><span style=\"color: #808000; text-decoration-color: #808000\">Call this method to make sure that all pending events have been written to</span>        <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\"> 148 </span><span style=\"color: #bfbf7f; text-decoration-color: #bfbf7f\">│   │   </span><span style=\"color: #808000; text-decoration-color: #808000\">disk.</span>                                                                             <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\"> 149 </span><span style=\"color: #bfbf7f; text-decoration-color: #bfbf7f\">│   │   </span><span style=\"color: #808000; text-decoration-color: #808000\">\"\"\"</span>                                                                               <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span> <span style=\"color: #800000; text-decoration-color: #800000\">❱ </span> 150 <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   </span><span style=\"color: #00ffff; text-decoration-color: #00ffff\">self</span>.event_writer.flush()                                                         <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\"> 151 │   </span>                                                                                      <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\"> 152 │   </span><span style=\"color: #0000ff; text-decoration-color: #0000ff\">def</span> <span style=\"color: #00ff00; text-decoration-color: #00ff00\">close</span>(<span style=\"color: #00ffff; text-decoration-color: #00ffff\">self</span>):                                                                      <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\"> 153 </span><span style=\"color: #bfbfbf; text-decoration-color: #bfbfbf\">│   │   </span><span style=\"color: #808000; text-decoration-color: #808000\">\"\"\"Flushes the event file to disk and close the file.</span>                             <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>                                                                                                  <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span> <span style=\"color: #bfbf7f; text-decoration-color: #bfbf7f\">/usr/local/lib/python3.10/dist-packages/tensorboard/summary/writer/</span><span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\">event_file_writer.py</span>:<span style=\"color: #0000ff; text-decoration-color: #0000ff\">125</span> in   <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span> <span style=\"color: #00ff00; text-decoration-color: #00ff00\">flush</span>                                                                                            <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>                                                                                                  <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">122 </span><span style=\"color: #bfbf7f; text-decoration-color: #bfbf7f\">│   │   </span><span style=\"color: #808000; text-decoration-color: #808000\">Call this method to make sure that all pending events have been</span>                    <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">123 </span><span style=\"color: #bfbf7f; text-decoration-color: #bfbf7f\">│   │   </span><span style=\"color: #808000; text-decoration-color: #808000\">written to disk.</span>                                                                   <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">124 </span><span style=\"color: #bfbf7f; text-decoration-color: #bfbf7f\">│   │   </span><span style=\"color: #808000; text-decoration-color: #808000\">\"\"\"</span>                                                                                <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span> <span style=\"color: #800000; text-decoration-color: #800000\">❱ </span>125 <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   </span><span style=\"color: #00ffff; text-decoration-color: #00ffff\">self</span>._async_writer.flush()                                                         <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">126 │   </span>                                                                                       <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">127 │   </span><span style=\"color: #0000ff; text-decoration-color: #0000ff\">def</span> <span style=\"color: #00ff00; text-decoration-color: #00ff00\">close</span>(<span style=\"color: #00ffff; text-decoration-color: #00ffff\">self</span>):                                                                       <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">128 </span><span style=\"color: #bfbfbf; text-decoration-color: #bfbfbf\">│   │   </span><span style=\"color: #808000; text-decoration-color: #808000\">\"\"\"Performs a final flush of the event file to disk, stops the</span>                     <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>                                                                                                  <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span> <span style=\"color: #bfbf7f; text-decoration-color: #bfbf7f\">/usr/local/lib/python3.10/dist-packages/tensorboard/summary/writer/</span><span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\">event_file_writer.py</span>:<span style=\"color: #0000ff; text-decoration-color: #0000ff\">190</span> in   <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span> <span style=\"color: #00ff00; text-decoration-color: #00ff00\">flush</span>                                                                                            <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>                                                                                                  <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">187 │   │   │   </span><span style=\"color: #0000ff; text-decoration-color: #0000ff\">if</span> <span style=\"color: #00ffff; text-decoration-color: #00ffff\">self</span>._closed:                                                               <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">188 │   │   │   │   </span><span style=\"color: #0000ff; text-decoration-color: #0000ff\">raise</span> <span style=\"color: #00ffff; text-decoration-color: #00ffff\">IOError</span>(<span style=\"color: #808000; text-decoration-color: #808000\">\"Writer is closed\"</span>)                                          <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">189 │   │   │   </span><span style=\"color: #00ffff; text-decoration-color: #00ffff\">self</span>._byte_queue.join()                                                        <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span> <span style=\"color: #800000; text-decoration-color: #800000\">❱ </span>190 <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   │   </span><span style=\"color: #00ffff; text-decoration-color: #00ffff\">self</span>._writer.flush()                                                           <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">191 │   │   │   # Check the status again in case the background worker thread has</span>              <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">192 │   │   │   # failed in the meantime to avoid waiting until the next call to</span>               <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">193 │   │   │   # surface the error.</span>                                                           <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>                                                                                                  <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span> <span style=\"color: #bfbf7f; text-decoration-color: #bfbf7f\">/usr/local/lib/python3.10/dist-packages/tensorboard/summary/writer/</span><span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\">record_writer.py</span>:<span style=\"color: #0000ff; text-decoration-color: #0000ff\">43</span> in <span style=\"color: #00ff00; text-decoration-color: #00ff00\">flush</span>  <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>                                                                                                  <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">40 │   │   </span><span style=\"color: #00ffff; text-decoration-color: #00ffff\">self</span>._writer.write(header + header_crc + data + footer_crc)                         <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">41 │   </span>                                                                                        <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">42 │   </span><span style=\"color: #0000ff; text-decoration-color: #0000ff\">def</span> <span style=\"color: #00ff00; text-decoration-color: #00ff00\">flush</span>(<span style=\"color: #00ffff; text-decoration-color: #00ffff\">self</span>):                                                                        <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span> <span style=\"color: #800000; text-decoration-color: #800000\">❱ </span>43 <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   </span><span style=\"color: #00ffff; text-decoration-color: #00ffff\">self</span>._writer.flush()                                                                <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">44 │   </span>                                                                                        <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">45 │   </span><span style=\"color: #0000ff; text-decoration-color: #0000ff\">def</span> <span style=\"color: #00ff00; text-decoration-color: #00ff00\">close</span>(<span style=\"color: #00ffff; text-decoration-color: #00ffff\">self</span>):                                                                        <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">46 │   │   </span><span style=\"color: #00ffff; text-decoration-color: #00ffff\">self</span>._writer.close()                                                                <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>                                                                                                  <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span> <span style=\"color: #bfbf7f; text-decoration-color: #bfbf7f\">/usr/local/lib/python3.10/dist-packages/tensorflow/python/lib/io/</span><span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\">file_io.py</span>:<span style=\"color: #0000ff; text-decoration-color: #0000ff\">221</span> in <span style=\"color: #00ff00; text-decoration-color: #00ff00\">flush</span>         <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>                                                                                                  <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\"> 218 </span><span style=\"color: #bfbf7f; text-decoration-color: #bfbf7f\">│   </span><span style=\"color: #808000; text-decoration-color: #808000\">data would survive an application crash but not necessarily an OS crash.</span>              <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\"> 219 </span><span style=\"color: #bfbf7f; text-decoration-color: #bfbf7f\">│   </span><span style=\"color: #808000; text-decoration-color: #808000\">\"\"\"</span>                                                                                   <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\"> 220 │   </span><span style=\"color: #0000ff; text-decoration-color: #0000ff\">if</span> <span style=\"color: #00ffff; text-decoration-color: #00ffff\">self</span>._writable_file:                                                               <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span> <span style=\"color: #800000; text-decoration-color: #800000\">❱ </span> 221 <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│     </span><span style=\"color: #00ffff; text-decoration-color: #00ffff\">self</span>._writable_file.flush()                                                         <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\"> 222   </span>                                                                                        <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\"> 223   </span><span style=\"color: #0000ff; text-decoration-color: #0000ff\">def</span> <span style=\"color: #00ff00; text-decoration-color: #00ff00\">close</span>(<span style=\"color: #00ffff; text-decoration-color: #00ffff\">self</span>):                                                                        <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\"> 224 </span><span style=\"color: #bfbfbf; text-decoration-color: #bfbfbf\">│   </span><span style=\"color: #808000; text-decoration-color: #808000\">r\"\"\"Closes the file.</span>                                                                  <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">╰──────────────────────────────────────────────────────────────────────────────────────────────────╯</span>\n",
              "<span style=\"color: #ff0000; text-decoration-color: #ff0000; font-weight: bold\">FailedPreconditionError: </span><span style=\"color: #800080; text-decoration-color: #800080\">/content/drive/MyDrive/</span><span style=\"color: #ff00ff; text-decoration-color: #ff00ff\">Colab</span> \n",
              "Files/WM/falcon-chat-40b/runs/May30_05-<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">06</span>-25_3e61552a1fc1/events.out.tfevents.<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1685423187.</span>3e61552a1fc1.<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">231.0</span>; \n",
              "Transport endpoint is not connected\n",
              "</pre>\n"
            ],
            "text/plain": [
              "\u001b[31m╭─\u001b[0m\u001b[31m──────────────────────────────\u001b[0m\u001b[31m \u001b[0m\u001b[1;31mTraceback \u001b[0m\u001b[1;2;31m(most recent call last)\u001b[0m\u001b[31m \u001b[0m\u001b[31m───────────────────────────────\u001b[0m\u001b[31m─╮\u001b[0m\n",
              "\u001b[31m│\u001b[0m in \u001b[92m<cell line: 23>\u001b[0m:\u001b[94m23\u001b[0m                                                                            \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m                                                                                                  \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m \u001b[2;33m/usr/local/lib/python3.10/dist-packages/transformers/\u001b[0m\u001b[1;33mtrainer.py\u001b[0m:\u001b[94m1696\u001b[0m in \u001b[92mtrain\u001b[0m                    \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m                                                                                                  \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m   \u001b[2m1693 \u001b[0m\u001b[2m│   │   \u001b[0minner_training_loop = find_executable_batch_size(                                 \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m   \u001b[2m1694 \u001b[0m\u001b[2m│   │   │   \u001b[0m\u001b[96mself\u001b[0m._inner_training_loop, \u001b[96mself\u001b[0m._train_batch_size, args.auto_find_batch_size  \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m   \u001b[2m1695 \u001b[0m\u001b[2m│   │   \u001b[0m)                                                                                 \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m \u001b[31m❱ \u001b[0m1696 \u001b[2m│   │   \u001b[0m\u001b[94mreturn\u001b[0m inner_training_loop(                                                       \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m   \u001b[2m1697 \u001b[0m\u001b[2m│   │   │   \u001b[0margs=args,                                                                    \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m   \u001b[2m1698 \u001b[0m\u001b[2m│   │   │   \u001b[0mresume_from_checkpoint=resume_from_checkpoint,                                \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m   \u001b[2m1699 \u001b[0m\u001b[2m│   │   │   \u001b[0mtrial=trial,                                                                  \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m                                                                                                  \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m \u001b[2;33m/usr/local/lib/python3.10/dist-packages/accelerate/utils/\u001b[0m\u001b[1;33mmemory.py\u001b[0m:\u001b[94m132\u001b[0m in \u001b[92mdecorator\u001b[0m              \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m                                                                                                  \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m   \u001b[2m129 \u001b[0m\u001b[2m│   │   │   \u001b[0m\u001b[94mif\u001b[0m batch_size == \u001b[94m0\u001b[0m:                                                            \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m   \u001b[2m130 \u001b[0m\u001b[2m│   │   │   │   \u001b[0m\u001b[94mraise\u001b[0m \u001b[96mRuntimeError\u001b[0m(\u001b[33m\"\u001b[0m\u001b[33mNo executable batch size found, reached zero.\u001b[0m\u001b[33m\"\u001b[0m)        \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m   \u001b[2m131 \u001b[0m\u001b[2m│   │   │   \u001b[0m\u001b[94mtry\u001b[0m:                                                                           \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m \u001b[31m❱ \u001b[0m132 \u001b[2m│   │   │   │   \u001b[0m\u001b[94mreturn\u001b[0m function(batch_size, *args, **kwargs)                               \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m   \u001b[2m133 \u001b[0m\u001b[2m│   │   │   \u001b[0m\u001b[94mexcept\u001b[0m \u001b[96mException\u001b[0m \u001b[94mas\u001b[0m e:                                                         \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m   \u001b[2m134 \u001b[0m\u001b[2m│   │   │   │   \u001b[0m\u001b[94mif\u001b[0m should_reduce_batch_size(e):                                            \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m   \u001b[2m135 \u001b[0m\u001b[2m│   │   │   │   │   \u001b[0mgc.collect()                                                           \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m                                                                                                  \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m \u001b[2;33m/usr/local/lib/python3.10/dist-packages/transformers/\u001b[0m\u001b[1;33mtrainer.py\u001b[0m:\u001b[94m2052\u001b[0m in \u001b[92m_inner_training_loop\u001b[0m     \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m                                                                                                  \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m   \u001b[2m2049 \u001b[0m\u001b[2m│   │   │   │   │   \u001b[0m\u001b[96mself\u001b[0m.state.epoch = epoch + (step + \u001b[94m1\u001b[0m + steps_skipped) / steps_in_epo  \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m   \u001b[2m2050 \u001b[0m\u001b[2m│   │   │   │   │   \u001b[0m\u001b[96mself\u001b[0m.control = \u001b[96mself\u001b[0m.callback_handler.on_step_end(args, \u001b[96mself\u001b[0m.state, \u001b[96ms\u001b[0m  \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m   \u001b[2m2051 \u001b[0m\u001b[2m│   │   │   │   │   \u001b[0m                                                                      \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m \u001b[31m❱ \u001b[0m2052 \u001b[2m│   │   │   │   │   \u001b[0m\u001b[96mself\u001b[0m._maybe_log_save_evaluate(tr_loss, model, trial, epoch, ignore_k  \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m   \u001b[2m2053 \u001b[0m\u001b[2m│   │   │   │   \u001b[0m\u001b[94melse\u001b[0m:                                                                     \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m   \u001b[2m2054 \u001b[0m\u001b[2m│   │   │   │   │   \u001b[0m\u001b[96mself\u001b[0m.control = \u001b[96mself\u001b[0m.callback_handler.on_substep_end(args, \u001b[96mself\u001b[0m.state  \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m   \u001b[2m2055 \u001b[0m                                                                                          \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m                                                                                                  \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m \u001b[2;33m/usr/local/lib/python3.10/dist-packages/transformers/\u001b[0m\u001b[1;33mtrainer.py\u001b[0m:\u001b[94m2338\u001b[0m in \u001b[92m_maybe_log_save_evaluate\u001b[0m \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m                                                                                                  \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m   \u001b[2m2335 \u001b[0m\u001b[2m│   │   │   \u001b[0m\u001b[96mself\u001b[0m._globalstep_last_logged = \u001b[96mself\u001b[0m.state.global_step                         \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m   \u001b[2m2336 \u001b[0m\u001b[2m│   │   │   \u001b[0m\u001b[96mself\u001b[0m.store_flos()                                                             \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m   \u001b[2m2337 \u001b[0m\u001b[2m│   │   │   \u001b[0m                                                                              \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m \u001b[31m❱ \u001b[0m2338 \u001b[2m│   │   │   \u001b[0m\u001b[96mself\u001b[0m.log(logs)                                                                \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m   \u001b[2m2339 \u001b[0m\u001b[2m│   │   \u001b[0m                                                                                  \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m   \u001b[2m2340 \u001b[0m\u001b[2m│   │   \u001b[0mmetrics = \u001b[94mNone\u001b[0m                                                                    \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m   \u001b[2m2341 \u001b[0m\u001b[2m│   │   \u001b[0m\u001b[94mif\u001b[0m \u001b[96mself\u001b[0m.control.should_evaluate:                                                  \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m                                                                                                  \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m \u001b[2;33m/usr/local/lib/python3.10/dist-packages/transformers/\u001b[0m\u001b[1;33mtrainer.py\u001b[0m:\u001b[94m2700\u001b[0m in \u001b[92mlog\u001b[0m                      \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m                                                                                                  \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m   \u001b[2m2697 \u001b[0m\u001b[2m│   │   \u001b[0m                                                                                  \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m   \u001b[2m2698 \u001b[0m\u001b[2m│   │   \u001b[0moutput = {**logs, **{\u001b[33m\"\u001b[0m\u001b[33mstep\u001b[0m\u001b[33m\"\u001b[0m: \u001b[96mself\u001b[0m.state.global_step}}                             \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m   \u001b[2m2699 \u001b[0m\u001b[2m│   │   \u001b[0m\u001b[96mself\u001b[0m.state.log_history.append(output)                                             \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m \u001b[31m❱ \u001b[0m2700 \u001b[2m│   │   \u001b[0m\u001b[96mself\u001b[0m.control = \u001b[96mself\u001b[0m.callback_handler.on_log(\u001b[96mself\u001b[0m.args, \u001b[96mself\u001b[0m.state, \u001b[96mself\u001b[0m.control,  \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m   \u001b[2m2701 \u001b[0m\u001b[2m│   \u001b[0m                                                                                      \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m   \u001b[2m2702 \u001b[0m\u001b[2m│   \u001b[0m\u001b[94mdef\u001b[0m \u001b[92m_prepare_input\u001b[0m(\u001b[96mself\u001b[0m, data: Union[torch.Tensor, Any]) -> Union[torch.Tensor, Any]  \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m   \u001b[2m2703 \u001b[0m\u001b[2;90m│   │   \u001b[0m\u001b[33m\"\"\"\u001b[0m                                                                               \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m                                                                                                  \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m \u001b[2;33m/usr/local/lib/python3.10/dist-packages/transformers/\u001b[0m\u001b[1;33mtrainer_callback.py\u001b[0m:\u001b[94m390\u001b[0m in \u001b[92mon_log\u001b[0m           \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m                                                                                                  \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m   \u001b[2m387 \u001b[0m\u001b[2m│   \u001b[0m                                                                                       \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m   \u001b[2m388 \u001b[0m\u001b[2m│   \u001b[0m\u001b[94mdef\u001b[0m \u001b[92mon_log\u001b[0m(\u001b[96mself\u001b[0m, args: TrainingArguments, state: TrainerState, control: TrainerContr   \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m   \u001b[2m389 \u001b[0m\u001b[2m│   │   \u001b[0mcontrol.should_log = \u001b[94mFalse\u001b[0m                                                         \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m \u001b[31m❱ \u001b[0m390 \u001b[2m│   │   \u001b[0m\u001b[94mreturn\u001b[0m \u001b[96mself\u001b[0m.call_event(\u001b[33m\"\u001b[0m\u001b[33mon_log\u001b[0m\u001b[33m\"\u001b[0m, args, state, control, logs=logs)                  \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m   \u001b[2m391 \u001b[0m\u001b[2m│   \u001b[0m                                                                                       \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m   \u001b[2m392 \u001b[0m\u001b[2m│   \u001b[0m\u001b[94mdef\u001b[0m \u001b[92mon_prediction_step\u001b[0m(\u001b[96mself\u001b[0m, args: TrainingArguments, state: TrainerState, control:    \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m   \u001b[2m393 \u001b[0m\u001b[2m│   │   \u001b[0m\u001b[94mreturn\u001b[0m \u001b[96mself\u001b[0m.call_event(\u001b[33m\"\u001b[0m\u001b[33mon_prediction_step\u001b[0m\u001b[33m\"\u001b[0m, args, state, control)                 \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m                                                                                                  \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m \u001b[2;33m/usr/local/lib/python3.10/dist-packages/transformers/\u001b[0m\u001b[1;33mtrainer_callback.py\u001b[0m:\u001b[94m397\u001b[0m in \u001b[92mcall_event\u001b[0m       \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m                                                                                                  \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m   \u001b[2m394 \u001b[0m\u001b[2m│   \u001b[0m                                                                                       \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m   \u001b[2m395 \u001b[0m\u001b[2m│   \u001b[0m\u001b[94mdef\u001b[0m \u001b[92mcall_event\u001b[0m(\u001b[96mself\u001b[0m, event, args, state, control, **kwargs):                           \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m   \u001b[2m396 \u001b[0m\u001b[2m│   │   \u001b[0m\u001b[94mfor\u001b[0m callback \u001b[95min\u001b[0m \u001b[96mself\u001b[0m.callbacks:                                                    \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m \u001b[31m❱ \u001b[0m397 \u001b[2m│   │   │   \u001b[0mresult = \u001b[96mgetattr\u001b[0m(callback, event)(                                             \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m   \u001b[2m398 \u001b[0m\u001b[2m│   │   │   │   \u001b[0margs,                                                                      \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m   \u001b[2m399 \u001b[0m\u001b[2m│   │   │   │   \u001b[0mstate,                                                                     \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m   \u001b[2m400 \u001b[0m\u001b[2m│   │   │   │   \u001b[0mcontrol,                                                                   \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m                                                                                                  \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m \u001b[2;33m/usr/local/lib/python3.10/dist-packages/transformers/\u001b[0m\u001b[1;33mintegrations.py\u001b[0m:\u001b[94m655\u001b[0m in \u001b[92mon_log\u001b[0m               \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m                                                                                                  \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m   \u001b[2m 652 \u001b[0m\u001b[2m│   │   │   │   │   │   \u001b[0m\u001b[33m\"\u001b[0m\u001b[33mThis invocation of Tensorboard\u001b[0m\u001b[33m'\u001b[0m\u001b[33ms writer.add_scalar() \u001b[0m\u001b[33m\"\u001b[0m           \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m   \u001b[2m 653 \u001b[0m\u001b[2m│   │   │   │   │   │   \u001b[0m\u001b[33m\"\u001b[0m\u001b[33mis incorrect so we dropped this attribute.\u001b[0m\u001b[33m\"\u001b[0m                      \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m   \u001b[2m 654 \u001b[0m\u001b[2m│   │   │   │   │   \u001b[0m)                                                                     \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m \u001b[31m❱ \u001b[0m 655 \u001b[2m│   │   │   \u001b[0m\u001b[96mself\u001b[0m.tb_writer.flush()                                                        \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m   \u001b[2m 656 \u001b[0m\u001b[2m│   \u001b[0m                                                                                      \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m   \u001b[2m 657 \u001b[0m\u001b[2m│   \u001b[0m\u001b[94mdef\u001b[0m \u001b[92mon_train_end\u001b[0m(\u001b[96mself\u001b[0m, args, state, control, **kwargs):                               \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m   \u001b[2m 658 \u001b[0m\u001b[2m│   │   \u001b[0m\u001b[94mif\u001b[0m \u001b[96mself\u001b[0m.tb_writer:                                                                \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m                                                                                                  \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m \u001b[2;33m/usr/local/lib/python3.10/dist-packages/torch/utils/tensorboard/\u001b[0m\u001b[1;33mwriter.py\u001b[0m:\u001b[94m1200\u001b[0m in \u001b[92mflush\u001b[0m          \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m                                                                                                  \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m   \u001b[2m1197 \u001b[0m\u001b[2m│   │   \u001b[0m\u001b[94mif\u001b[0m \u001b[96mself\u001b[0m.all_writers \u001b[95mis\u001b[0m \u001b[94mNone\u001b[0m:                                                      \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m   \u001b[2m1198 \u001b[0m\u001b[2m│   │   │   \u001b[0m\u001b[94mreturn\u001b[0m                                                                        \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m   \u001b[2m1199 \u001b[0m\u001b[2m│   │   \u001b[0m\u001b[94mfor\u001b[0m writer \u001b[95min\u001b[0m \u001b[96mself\u001b[0m.all_writers.values():                                          \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m \u001b[31m❱ \u001b[0m1200 \u001b[2m│   │   │   \u001b[0mwriter.flush()                                                                \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m   \u001b[2m1201 \u001b[0m\u001b[2m│   \u001b[0m                                                                                      \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m   \u001b[2m1202 \u001b[0m\u001b[2m│   \u001b[0m\u001b[94mdef\u001b[0m \u001b[92mclose\u001b[0m(\u001b[96mself\u001b[0m):                                                                      \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m   \u001b[2m1203 \u001b[0m\u001b[2m│   │   \u001b[0m\u001b[94mif\u001b[0m \u001b[96mself\u001b[0m.all_writers \u001b[95mis\u001b[0m \u001b[94mNone\u001b[0m:                                                      \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m                                                                                                  \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m \u001b[2;33m/usr/local/lib/python3.10/dist-packages/torch/utils/tensorboard/\u001b[0m\u001b[1;33mwriter.py\u001b[0m:\u001b[94m150\u001b[0m in \u001b[92mflush\u001b[0m           \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m                                                                                                  \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m   \u001b[2m 147 \u001b[0m\u001b[2;33m│   │   \u001b[0m\u001b[33mCall this method to make sure that all pending events have been written to\u001b[0m        \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m   \u001b[2m 148 \u001b[0m\u001b[2;33m│   │   \u001b[0m\u001b[33mdisk.\u001b[0m                                                                             \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m   \u001b[2m 149 \u001b[0m\u001b[2;33m│   │   \u001b[0m\u001b[33m\"\"\"\u001b[0m                                                                               \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m \u001b[31m❱ \u001b[0m 150 \u001b[2m│   │   \u001b[0m\u001b[96mself\u001b[0m.event_writer.flush()                                                         \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m   \u001b[2m 151 \u001b[0m\u001b[2m│   \u001b[0m                                                                                      \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m   \u001b[2m 152 \u001b[0m\u001b[2m│   \u001b[0m\u001b[94mdef\u001b[0m \u001b[92mclose\u001b[0m(\u001b[96mself\u001b[0m):                                                                      \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m   \u001b[2m 153 \u001b[0m\u001b[2;90m│   │   \u001b[0m\u001b[33m\"\"\"Flushes the event file to disk and close the file.\u001b[0m                             \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m                                                                                                  \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m \u001b[2;33m/usr/local/lib/python3.10/dist-packages/tensorboard/summary/writer/\u001b[0m\u001b[1;33mevent_file_writer.py\u001b[0m:\u001b[94m125\u001b[0m in   \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m \u001b[92mflush\u001b[0m                                                                                            \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m                                                                                                  \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m   \u001b[2m122 \u001b[0m\u001b[2;33m│   │   \u001b[0m\u001b[33mCall this method to make sure that all pending events have been\u001b[0m                    \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m   \u001b[2m123 \u001b[0m\u001b[2;33m│   │   \u001b[0m\u001b[33mwritten to disk.\u001b[0m                                                                   \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m   \u001b[2m124 \u001b[0m\u001b[2;33m│   │   \u001b[0m\u001b[33m\"\"\"\u001b[0m                                                                                \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m \u001b[31m❱ \u001b[0m125 \u001b[2m│   │   \u001b[0m\u001b[96mself\u001b[0m._async_writer.flush()                                                         \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m   \u001b[2m126 \u001b[0m\u001b[2m│   \u001b[0m                                                                                       \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m   \u001b[2m127 \u001b[0m\u001b[2m│   \u001b[0m\u001b[94mdef\u001b[0m \u001b[92mclose\u001b[0m(\u001b[96mself\u001b[0m):                                                                       \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m   \u001b[2m128 \u001b[0m\u001b[2;90m│   │   \u001b[0m\u001b[33m\"\"\"Performs a final flush of the event file to disk, stops the\u001b[0m                     \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m                                                                                                  \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m \u001b[2;33m/usr/local/lib/python3.10/dist-packages/tensorboard/summary/writer/\u001b[0m\u001b[1;33mevent_file_writer.py\u001b[0m:\u001b[94m190\u001b[0m in   \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m \u001b[92mflush\u001b[0m                                                                                            \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m                                                                                                  \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m   \u001b[2m187 \u001b[0m\u001b[2m│   │   │   \u001b[0m\u001b[94mif\u001b[0m \u001b[96mself\u001b[0m._closed:                                                               \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m   \u001b[2m188 \u001b[0m\u001b[2m│   │   │   │   \u001b[0m\u001b[94mraise\u001b[0m \u001b[96mIOError\u001b[0m(\u001b[33m\"\u001b[0m\u001b[33mWriter is closed\u001b[0m\u001b[33m\"\u001b[0m)                                          \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m   \u001b[2m189 \u001b[0m\u001b[2m│   │   │   \u001b[0m\u001b[96mself\u001b[0m._byte_queue.join()                                                        \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m \u001b[31m❱ \u001b[0m190 \u001b[2m│   │   │   \u001b[0m\u001b[96mself\u001b[0m._writer.flush()                                                           \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m   \u001b[2m191 \u001b[0m\u001b[2m│   │   │   \u001b[0m\u001b[2m# Check the status again in case the background worker thread has\u001b[0m              \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m   \u001b[2m192 \u001b[0m\u001b[2m│   │   │   \u001b[0m\u001b[2m# failed in the meantime to avoid waiting until the next call to\u001b[0m               \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m   \u001b[2m193 \u001b[0m\u001b[2m│   │   │   \u001b[0m\u001b[2m# surface the error.\u001b[0m                                                           \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m                                                                                                  \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m \u001b[2;33m/usr/local/lib/python3.10/dist-packages/tensorboard/summary/writer/\u001b[0m\u001b[1;33mrecord_writer.py\u001b[0m:\u001b[94m43\u001b[0m in \u001b[92mflush\u001b[0m  \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m                                                                                                  \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m   \u001b[2m40 \u001b[0m\u001b[2m│   │   \u001b[0m\u001b[96mself\u001b[0m._writer.write(header + header_crc + data + footer_crc)                         \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m   \u001b[2m41 \u001b[0m\u001b[2m│   \u001b[0m                                                                                        \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m   \u001b[2m42 \u001b[0m\u001b[2m│   \u001b[0m\u001b[94mdef\u001b[0m \u001b[92mflush\u001b[0m(\u001b[96mself\u001b[0m):                                                                        \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m \u001b[31m❱ \u001b[0m43 \u001b[2m│   │   \u001b[0m\u001b[96mself\u001b[0m._writer.flush()                                                                \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m   \u001b[2m44 \u001b[0m\u001b[2m│   \u001b[0m                                                                                        \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m   \u001b[2m45 \u001b[0m\u001b[2m│   \u001b[0m\u001b[94mdef\u001b[0m \u001b[92mclose\u001b[0m(\u001b[96mself\u001b[0m):                                                                        \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m   \u001b[2m46 \u001b[0m\u001b[2m│   │   \u001b[0m\u001b[96mself\u001b[0m._writer.close()                                                                \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m                                                                                                  \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m \u001b[2;33m/usr/local/lib/python3.10/dist-packages/tensorflow/python/lib/io/\u001b[0m\u001b[1;33mfile_io.py\u001b[0m:\u001b[94m221\u001b[0m in \u001b[92mflush\u001b[0m         \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m                                                                                                  \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m   \u001b[2m 218 \u001b[0m\u001b[2;33m│   \u001b[0m\u001b[33mdata would survive an application crash but not necessarily an OS crash.\u001b[0m              \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m   \u001b[2m 219 \u001b[0m\u001b[2;33m│   \u001b[0m\u001b[33m\"\"\"\u001b[0m                                                                                   \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m   \u001b[2m 220 \u001b[0m\u001b[2m│   \u001b[0m\u001b[94mif\u001b[0m \u001b[96mself\u001b[0m._writable_file:                                                               \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m \u001b[31m❱ \u001b[0m 221 \u001b[2m│     \u001b[0m\u001b[96mself\u001b[0m._writable_file.flush()                                                         \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m   \u001b[2m 222 \u001b[0m\u001b[2m  \u001b[0m                                                                                        \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m   \u001b[2m 223 \u001b[0m\u001b[2m  \u001b[0m\u001b[94mdef\u001b[0m \u001b[92mclose\u001b[0m(\u001b[96mself\u001b[0m):                                                                        \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m   \u001b[2m 224 \u001b[0m\u001b[2;90m│   \u001b[0m\u001b[33mr\u001b[0m\u001b[33m\"\"\"Closes the file.\u001b[0m                                                                  \u001b[31m│\u001b[0m\n",
              "\u001b[31m╰──────────────────────────────────────────────────────────────────────────────────────────────────╯\u001b[0m\n",
              "\u001b[1;91mFailedPreconditionError: \u001b[0m\u001b[35m/content/drive/MyDrive/\u001b[0m\u001b[95mColab\u001b[0m \n",
              "Files/WM/falcon-chat-40b/runs/May30_05-\u001b[1;36m06\u001b[0m-25_3e61552a1fc1/events.out.tfevents.\u001b[1;36m1685423187.\u001b[0m3e61552a1fc1.\u001b[1;36m231.0\u001b[0m; \n",
              "Transport endpoint is not connected\n"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "training_args = transformers.TrainingArguments(\n",
        "    auto_find_batch_size=True,\n",
        "    gradient_accumulation_steps=4,\n",
        "    num_train_epochs=1,\n",
        "    learning_rate=2e-5,\n",
        "    fp16=True,\n",
        "    save_total_limit=4,\n",
        "    logging_steps=25,\n",
        "    output_dir=\"./outputs\",\n",
        "    save_strategy='epoch',\n",
        "    optim=\"paged_adamw_8bit\",\n",
        "    lr_scheduler_type = 'cosine',\n",
        "    warmup_ratio = 0.05,\n",
        ")\n",
        "\n",
        "trainer = transformers.Trainer(\n",
        "    model=model,\n",
        "    train_dataset=data,\n",
        "    args=training_args,\n",
        "    data_collator=transformers.DataCollatorForLanguageModeling(tokenizer, mlm=False),\n",
        ")\n",
        "model.config.use_cache = False  # silence the warnings. Please re-enable for inference!\n",
        "trainer.train()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Duak7T_B3VpJ"
      },
      "source": [
        "## Share adapters on the 🤗 Hub"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 135,
          "referenced_widgets": [
            "ca22fdc5543b40a695476c352965e587",
            "00a07ac6f14c470992340780e79d8931",
            "eb5e065658b3429397036f83b700aba2",
            "12c9008efaa549a9b10e3a225f240379",
            "33d47d486b474205b5e8b3cacd03ad2f",
            "f3ac14e439b34fc180dee3ce13f6c218",
            "71a2bb5be0014d7ba33371552a329e8a",
            "32ef619ccd784666b40296edb4294b44",
            "c17abfbff6644ecf9ec2f21d10495588",
            "bd60144a6ba2489a95e30d6e3f2aab61",
            "2fc0d85d5f194613bb6cb238afeefb57",
            "873e2d07dd6b4e1d86d9fd560948e8b3",
            "670796e8f1ac4102b401040d8cd1a9f4",
            "501ed7d1cb9a4603974428ecf992ce12",
            "f95b824d2f08420fb907de8b2c529c1a",
            "bcd8c57594f44936975c07f7848aa078",
            "c5d0534c10364104b89b98e70ef1346a",
            "86dfd30f10554f8aa12c7076860fc361",
            "5400eb206c5f49c4aa4e57c612d8fde4",
            "087a82e337554cfa8642dbdd97624f30",
            "e20dcca2167048f4a41275680d4e9d51",
            "1b834967bc8e4779ad58687fbcbd527b"
          ]
        },
        "id": "VxB6UV5XAvvP",
        "outputId": "72fcdaad-8183-4544-9025-3973324ea427"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "ca22fdc5543b40a695476c352965e587",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Upload 1 LFS files:   0%|          | 0/1 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "873e2d07dd6b4e1d86d9fd560948e8b3",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "adapter_model.bin:   0%|          | 0.00/33.5M [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "text/plain": [
              "CommitInfo(commit_url='https://huggingface.co/dfurman/falcon-40b-chat-oasst1/commit/ac771b1cd698d5d2c7ce270eb15924cd2ea366aa', commit_message='Upload model', commit_description='', oid='ac771b1cd698d5d2c7ce270eb15924cd2ea366aa', pr_url=None, pr_revision=None, pr_num=None)"
            ]
          },
          "execution_count": 22,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "model.push_to_hub(\"dfurman/falcon-40b-chat-oasst1\", use_auth_token=True)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "S65GcxNGA9kz"
      },
      "source": [
        "## Load adapters from the Hub\n",
        "\n",
        "You can also directly load adapters from the Hub using the commands below:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 637,
          "referenced_widgets": [
            "af8ed9f488774e158860da31a070ebdb",
            "012fd769dabc4b6ab285cf0e838632a4",
            "dc159821927042f18262b00c441f7ca9",
            "78398c61b7fd43e7844673da022352b4",
            "59555f5d6a3e4afeb4a7880a72eff86a",
            "1107772094454155b9ad182f64babd11",
            "04bd56ac81aa45a5a6a1cf3f194cf92f",
            "3965fc9a677742939964ccc99eb7efd6",
            "ed983edf3c994c748947c432022d5810",
            "b8a8c40f22954fc9a0d662af7bb658c8",
            "9b0efcca54ed4d84add516affdd0a54e",
            "1884cdbd84e84e46a55747747e139b2c",
            "c4257b19ccee4aa4b6986e7e2ea366a3",
            "8284aee324944895b7da43554615dfc4",
            "3450ab02b1714805a9b54b27e93d2e0d",
            "34f7e1a0f48e4c488d60a75ddf321d63",
            "711dfc9060594030935dbe58ad688d9d",
            "fe9c5af5775646f39f9ad1b3cfc7bbec",
            "8cb14ad1398847b090456990ea35e5ca",
            "f6cf360f5caf4ec782aa7461ce6bf1c1",
            "f76f28be57274973acb9ea7a9cea5d2a",
            "e8d11ba7bcb449a3a31da63551938957"
          ]
        },
        "id": "hsD1VKqeA62Z",
        "outputId": "43c39595-6f30-451e-9ff1-9e8f4afc5c63"
      },
      "outputs": [],
      "source": [
        "import torch\n",
        "from peft import PeftModel, PeftConfig\n",
        "from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig\n",
        "\n",
        "peft_model_id = \"dfurman/falcon-40b-chat-oasst1\"\n",
        "config = PeftConfig.from_pretrained(peft_model_id)\n",
        "\n",
        "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",
        ")\n",
        "\n",
        "model = AutoModelForCausalLM.from_pretrained(\n",
        "    config.base_model_name_or_path,\n",
        "    quantization_config=bnb_config,\n",
        "    device_map={\"\":0},\n",
        "    trust_remote_code=True,\n",
        ")\n",
        "tokenizer = AutoTokenizer.from_pretrained(config.base_model_name_or_path)\n",
        "tokenizer.pad_token = tokenizer.eos_token\n",
        "\n",
        "# Load the Lora model\n",
        "model = PeftModel.from_pretrained(model, peft_model_id)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "J_xz1Cc-Kx4c"
      },
      "source": [
        "## Inference\n",
        "\n",
        "You can then directly use the trained model or the model that you have loaded from the 🤗 Hub for inference as you would do it usually in `transformers`."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 53
        },
        "id": "jlpAYdyVGoyo",
        "outputId": "6fa1758f-4ffa-4a0d-8c8d-0b3d7f497641"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'<human>: My name is Daniel. Write a short email to my closest friends inviting them to come to my home on Friday for a dinner party, I will make the food but tell them to BYOB. \\n<bot>:'"
            ]
          },
          "execution_count": 3,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "prompt = \"\"\"<human>: My name is Daniel. Write a short email to my closest friends inviting them to come to my home on Friday for a dinner party, I will make the food but tell them to BYOB. \n",
        "<bot>:\"\"\"\n",
        "\n",
        "prompt"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5UpKIshHJQmm",
        "outputId": "c5bcd426-f94f-4ecc-b99c-3d7b74617d5c"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "{'input_ids': tensor([[   39, 15564, 48190,  1814,  1536,   304,  8156,    25, 14687,   241,\n",
              "          1866,  2572,   271,   491, 14710,  2153, 19549,   612,   271,  1239,\n",
              "           271,   491,  1081,   313,  4201,   312,   241,  5947,  3054,    23,\n",
              "           295,   451,   717,   248,  1655,   480,  1705,   612,   271, 15528,\n",
              "         18791,    25,  4610,    39, 13359, 48190]], device='cuda:0'), 'token_type_ids': tensor([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
              "         0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]],\n",
              "       device='cuda:0'), 'attention_mask': tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "         1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]],\n",
              "       device='cuda:0')}"
            ]
          },
          "execution_count": 4,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "batch = tokenizer(\n",
        "    prompt,\n",
        "    padding=True,\n",
        "    truncation=True,\n",
        "    return_tensors='pt'\n",
        ")\n",
        "batch = batch.to('cuda:0')\n",
        "batch"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FuUMu4tzKx4e",
        "outputId": "66da0ee0-48e9-441d-a6fb-09ea4529103c"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/transformers/generation/utils.py:1255: UserWarning: You have modified the pretrained model configuration to control generation. This is a deprecated strategy to control generation and will be removed soon, in a future version. Please use a generation configuration file (see https://huggingface.co/docs/transformers/main_classes/text_generation)\n",
            "  warnings.warn(\n"
          ]
        }
      ],
      "source": [
        "with torch.cuda.amp.autocast():\n",
        "    output_tokens = model.generate(\n",
        "        input_ids = batch.input_ids, \n",
        "        max_new_tokens=200,\n",
        "        temperature=0.7,\n",
        "        top_p=0.7,\n",
        "        num_return_sequences=1,\n",
        "        pad_token_id=tokenizer.eos_token_id,\n",
        "        eos_token_id=tokenizer.eos_token_id,\n",
        "    )"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RCe7ly02JVis",
        "outputId": "e5a2633a-2ea5-494f-9376-93e555383f77"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Dear Friends,\n",
            "\n",
            "I'm excited to invite you to a dinner party at my home on Friday, [date]. I will be making the food, but please bring your own beverages. I'm looking forward to seeing you and catching up!\n",
            "\n",
            "Sincerely,\n",
            "Daniel\n",
            "\n"
          ]
        }
      ],
      "source": [
        "generated_text = tokenizer.decode(output_tokens[0], skip_special_tokens=True)\n",
        "# Inspect message response in the outputs\n",
        "print(generated_text.split(\"<human>: \")[1].split(\"<bot>: \")[-1])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "ErwLWPPMCjmg",
        "outputId": "e5f56cfa-e55d-4504-e814-03bcd1f487ab"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'<human>: Create a list of four things to do in San Francisco. \\n<bot>:'"
            ]
          },
          "execution_count": 7,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "prompt = \"\"\"<human>: Create a list of four things to do in San Francisco. \n",
        "<bot>:\"\"\"\n",
        "\n",
        "prompt"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gTfZmK37Cjmg",
        "outputId": "40f240cf-d9f3-47c7-ec76-aa092a020ae6"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "{'input_ids': tensor([[   39, 15564, 48190,  8715,   241,  1175,   275,  1855,  1209,   271,\n",
              "           441,   272,  3787,  8863,    25,  4610,    39, 13359, 48190]],\n",
              "       device='cuda:0'), 'token_type_ids': tensor([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]],\n",
              "       device='cuda:0'), 'attention_mask': tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]],\n",
              "       device='cuda:0')}"
            ]
          },
          "execution_count": 8,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "batch = tokenizer(\n",
        "    prompt,\n",
        "    padding=True,\n",
        "    truncation=True,\n",
        "    return_tensors='pt'\n",
        ")\n",
        "batch = batch.to('cuda:0')\n",
        "batch"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "id": "3j55z1AhCjmh"
      },
      "outputs": [],
      "source": [
        "with torch.cuda.amp.autocast():\n",
        "    output_tokens = model.generate(\n",
        "        input_ids = batch.input_ids, \n",
        "        max_new_tokens=300,\n",
        "        temperature=0.7,\n",
        "        top_p=0.7,\n",
        "        num_return_sequences=1,\n",
        "        pad_token_id=tokenizer.eos_token_id,\n",
        "        eos_token_id=tokenizer.eos_token_id,\n",
        "    )"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6oTaI6m6Cjmh",
        "outputId": "1a55f5fe-3dda-4121-9076-c0496416d0dd"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Here are four things to do in San Francisco:\n",
            "\n",
            "1. Visit the Golden Gate Bridge: The Golden Gate Bridge is one of the most iconic landmarks in San Francisco and a must-see for any visitor. You can walk or bike across the bridge, or take a ferry to get a closer look.\n",
            "\n",
            "2. Explore the Fisherman's Wharf: The Fisherman's Wharf is a popular tourist destination with a variety of restaurants, shops, and attractions. You can try some fresh seafood, take a boat tour, or visit the Aquarium of the Bay.\n",
            "\n",
            "3. Visit the Alcatraz Island: Alcatraz Island is a former federal prison that is now a national park and a popular tourist destination. You can take a ferry to the island and explore the prison, learn about its history, and enjoy the beautiful views of the city.\n",
            "\n",
            "4. Take a cable car ride: San Francisco's cable cars are a unique and fun way to get around the city. You can ride the cable cars up and down the city's steep hills and enjoy the views along the way.\n",
            "\n",
            "These are just a few of the many things to do in San Francisco. There are also museums, parks, and other attractions to explore.\n",
            "\n"
          ]
        }
      ],
      "source": [
        "generated_text = tokenizer.decode(output_tokens[0], skip_special_tokens=True)\n",
        "# Inspect message response in the outputs\n",
        "print(generated_text.split(\"<human>: \")[1].split(\"<bot>: \")[-1])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "aNX2bTr3NZCM"
      },
      "outputs": [],
      "source": []
    }
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