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  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# Finetune Your Chatbot on a Single Node Xeon SPR"
      ],
      "metadata": {
        "id": "ElgN7-Qwk9FM"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "NeuralChat is a customizable chat framework designed to create user own chatbot within few minutes on multiple architectures. This notebook will introduce how to finetune your chatbot on the customized data on a single node Xeon SPR."
      ],
      "metadata": {
        "id": "vB8OZ4QilBqr"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Prepare Environment"
      ],
      "metadata": {
        "id": "r-q0OQ5HlHhh"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Install intel extension for transformers:"
      ],
      "metadata": {
        "id": "zuDt1O0nlGZi"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Jp-6OH6RT2ll",
        "outputId": "e0fc7495-34bf-4965-c190-bd1c8938fce4"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting intel-extension-for-transformers\n",
            "  Downloading intel_extension_for_transformers-1.4.2-cp310-cp310-manylinux_2_28_x86_64.whl (45.3 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m45.3/45.3 MB\u001b[0m \u001b[31m12.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: packaging in /usr/local/lib/python3.10/dist-packages (from intel-extension-for-transformers) (24.1)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.10/dist-packages (from intel-extension-for-transformers) (1.25.2)\n",
            "Collecting schema (from intel-extension-for-transformers)\n",
            "  Downloading schema-0.7.7-py2.py3-none-any.whl (18 kB)\n",
            "Requirement already satisfied: pyyaml in /usr/local/lib/python3.10/dist-packages (from intel-extension-for-transformers) (6.0.1)\n",
            "Collecting neural-compressor (from intel-extension-for-transformers)\n",
            "  Downloading neural_compressor-2.6-py3-none-any.whl (1.5 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.5/1.5 MB\u001b[0m \u001b[31m60.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: transformers in /usr/local/lib/python3.10/dist-packages (from intel-extension-for-transformers) (4.41.2)\n",
            "Collecting deprecated>=1.2.13 (from neural-compressor->intel-extension-for-transformers)\n",
            "  Downloading Deprecated-1.2.14-py2.py3-none-any.whl (9.6 kB)\n",
            "Requirement already satisfied: opencv-python-headless in /usr/local/lib/python3.10/dist-packages (from neural-compressor->intel-extension-for-transformers) (4.10.0.84)\n",
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            "Requirement already satisfied: py-cpuinfo in /usr/local/lib/python3.10/dist-packages (from neural-compressor->intel-extension-for-transformers) (9.0.0)\n",
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            "Requirement already satisfied: scikit-learn in /usr/local/lib/python3.10/dist-packages (from neural-compressor->intel-extension-for-transformers) (1.2.2)\n",
            "Requirement already satisfied: pycocotools in /usr/local/lib/python3.10/dist-packages (from neural-compressor->intel-extension-for-transformers) (2.0.8)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from transformers->intel-extension-for-transformers) (3.15.4)\n",
            "Requirement already satisfied: huggingface-hub<1.0,>=0.23.0 in /usr/local/lib/python3.10/dist-packages (from transformers->intel-extension-for-transformers) (0.23.4)\n",
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            "Requirement already satisfied: wrapt<2,>=1.10 in /usr/local/lib/python3.10/dist-packages (from deprecated>=1.2.13->neural-compressor->intel-extension-for-transformers) (1.14.1)\n",
            "Requirement already satisfied: fsspec>=2023.5.0 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub<1.0,>=0.23.0->transformers->intel-extension-for-transformers) (2023.6.0)\n",
            "Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub<1.0,>=0.23.0->transformers->intel-extension-for-transformers) (4.12.2)\n",
            "Requirement already satisfied: python-dateutil>=2.8.2 in /usr/local/lib/python3.10/dist-packages (from pandas->neural-compressor->intel-extension-for-transformers) (2.8.2)\n",
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            "Requirement already satisfied: wcwidth in /usr/local/lib/python3.10/dist-packages (from prettytable->neural-compressor->intel-extension-for-transformers) (0.2.13)\n",
            "Requirement already satisfied: matplotlib>=2.1.0 in /usr/local/lib/python3.10/dist-packages (from pycocotools->neural-compressor->intel-extension-for-transformers) (3.7.1)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->neural-compressor->intel-extension-for-transformers) (3.3.2)\n",
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            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests->neural-compressor->intel-extension-for-transformers) (2.0.7)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->neural-compressor->intel-extension-for-transformers) (2024.7.4)\n",
            "Requirement already satisfied: scipy>=1.3.2 in /usr/local/lib/python3.10/dist-packages (from scikit-learn->neural-compressor->intel-extension-for-transformers) (1.11.4)\n",
            "Requirement already satisfied: joblib>=1.1.1 in /usr/local/lib/python3.10/dist-packages (from scikit-learn->neural-compressor->intel-extension-for-transformers) (1.4.2)\n",
            "Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.10/dist-packages (from scikit-learn->neural-compressor->intel-extension-for-transformers) (3.5.0)\n",
            "Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=2.1.0->pycocotools->neural-compressor->intel-extension-for-transformers) (1.2.1)\n",
            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=2.1.0->pycocotools->neural-compressor->intel-extension-for-transformers) (0.12.1)\n",
            "Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=2.1.0->pycocotools->neural-compressor->intel-extension-for-transformers) (4.53.1)\n",
            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=2.1.0->pycocotools->neural-compressor->intel-extension-for-transformers) (1.4.5)\n",
            "Requirement already satisfied: pyparsing>=2.3.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=2.1.0->pycocotools->neural-compressor->intel-extension-for-transformers) (3.1.2)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.8.2->pandas->neural-compressor->intel-extension-for-transformers) (1.16.0)\n",
            "Installing collected packages: schema, deprecated, neural-compressor, intel-extension-for-transformers\n",
            "Successfully installed deprecated-1.2.14 intel-extension-for-transformers-1.4.2 neural-compressor-2.6 schema-0.7.7\n"
          ]
        }
      ],
      "source": [
        "!pip install intel-extension-for-transformers"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Install Requirements:"
      ],
      "metadata": {
        "id": "kMh7r3ajlYDS"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!git clone https://github.com/intel/intel-extension-for-transformers.git"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ojnMjYX8ULsl",
        "outputId": "28993856-b836-4434-dba8-616ef160e73c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Cloning into 'intel-extension-for-transformers'...\n",
            "remote: Enumerating objects: 1681988, done.\u001b[K\n",
            "remote: Counting objects: 100% (116638/116638), done.\u001b[K\n",
            "remote: Compressing objects: 100% (12340/12340), done.\u001b[K\n",
            "remote: Total 1681988 (delta 63056), reused 114702 (delta 61456), pack-reused 1565350\u001b[K\n",
            "Receiving objects: 100% (1681988/1681988), 594.70 MiB | 26.83 MiB/s, done.\n",
            "Resolving deltas: 100% (898963/898963), done.\n",
            "Updating files: 100% (3217/3217), done.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "%cd ./intel-extension-for-transformers/intel_extension_for_transformers/neural_chat/\n",
        "!pip install -r requirements.txt\n",
        "%cd ../../../"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "w0qR5TXsUcuU",
        "outputId": "27902b98-015b-4938-d913-db0993cbf5c0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content/intel-extension-for-transformers/intel_extension_for_transformers/neural_chat\n",
            "Collecting accelerate (from -r requirements.txt (line 1))\n",
            "  Downloading accelerate-0.32.1-py3-none-any.whl (314 kB)\n",
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            "\u001b[?25h  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
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            "Building wheels for collected packages: cchardet, rouge_score, transformers_stream_generator, sqlitedict, word2number, quantile-python, wavedrom\n",
            "  Building wheel for cchardet (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
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            "  Created wheel for sqlitedict: filename=sqlitedict-2.1.0-py3-none-any.whl size=16862 sha256=577239ed6b4cecb692c937232d0808725f3521f983b3d268946bbf960bfe6ea4\n",
            "  Stored in directory: /root/.cache/pip/wheels/79/d6/e7/304e0e6cb2221022c26d8161f7c23cd4f259a9e41e8bbcfabd\n",
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            "  Created wheel for quantile-python: filename=quantile_python-1.1-py3-none-any.whl size=3443 sha256=69109a367fe78e3ba0565f1c98c114c7baaa2d0d9cf1b3247fb3bc4b95989c8d\n",
            "  Stored in directory: /root/.cache/pip/wheels/6d/f4/0a/0e7d01548a005f9f3fa23101f071d248da052f2a9bf2fe11c6\n",
            "  Building wheel for wavedrom (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for wavedrom: filename=wavedrom-2.0.3.post3-py2.py3-none-any.whl size=30055 sha256=b7489a9acc41e9bf00fb6b35a032870760752e501d0bd7f3bdf907f8a5ae21fd\n",
            "  Stored in directory: /root/.cache/pip/wheels/9c/52/8c/38b454b42f712f325e26f633287484c7dc1ad469e1580c5954\n",
            "Successfully built cchardet rouge_score transformers_stream_generator sqlitedict word2number quantile-python wavedrom\n",
            "Installing collected packages: word2number, sqlitedict, quantile-python, ninja, nh3, cchardet, zstandard, yacs, xxhash, websockets, uvloop, ujson, tcolorpy, svgwrite, shortuuid, sacremoses, requests, python-multipart, python-dotenv, pydantic, pybind11, portalocker, pathvalidate, orjson, nvidia-nvtx-cu12, nvidia-nvjitlink-cu12, nvidia-nccl-cu12, nvidia-curand-cu12, nvidia-cufft-cu12, nvidia-cuda-runtime-cu12, nvidia-cuda-nvrtc-cu12, nvidia-cuda-cupti-cu12, nvidia-cublas-cu12, numpy, neural_speed, mbstrdecoder, markdown2, latex2mathml, jsonlines, humanfriendly, httptools, h11, einops, dnspython, dill, colorama, wavedrom, watchfiles, uvicorn, typepy, tqdm-multiprocess, tiktoken, starlette, sacrebleu, rouge_score, pyarrow, onnx, nvidia-cusparse-cu12, nvidia-cudnn-cu12, multiprocess, intel_extension_for_pytorch, httpcore, email_validator, coloredlogs, aioprometheus, nvidia-cusolver-cu12, httpx, ray, fastapi-cli, datasets, DataProperty, xformers, transformers_stream_generator, tabledata, fastapi, evaluate, accelerate, vllm, pytablewriter, peft, optimum, fschat, optimum-intel, lm-eval\n",
            "  Attempting uninstall: requests\n",
            "    Found existing installation: requests 2.31.0\n",
            "    Uninstalling requests-2.31.0:\n",
            "      Successfully uninstalled requests-2.31.0\n",
            "  Attempting uninstall: pydantic\n",
            "    Found existing installation: pydantic 2.8.2\n",
            "    Uninstalling pydantic-2.8.2:\n",
            "      Successfully uninstalled pydantic-2.8.2\n",
            "  Attempting uninstall: numpy\n",
            "    Found existing installation: numpy 1.25.2\n",
            "    Uninstalling numpy-1.25.2:\n",
            "      Successfully uninstalled numpy-1.25.2\n",
            "  Attempting uninstall: pyarrow\n",
            "    Found existing installation: pyarrow 14.0.2\n",
            "    Uninstalling pyarrow-14.0.2:\n",
            "      Successfully uninstalled pyarrow-14.0.2\n",
            "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
            "chex 0.1.86 requires numpy>=1.24.1, but you have numpy 1.23.5 which is incompatible.\n",
            "cudf-cu12 24.4.1 requires pyarrow<15.0.0a0,>=14.0.1, but you have pyarrow 16.1.0 which is incompatible.\n",
            "google-colab 1.0.0 requires requests==2.31.0, but you have requests 2.32.3 which is incompatible.\n",
            "ibis-framework 8.0.0 requires pyarrow<16,>=2, but you have pyarrow 16.1.0 which is incompatible.\n",
            "pandas-stubs 2.0.3.230814 requires numpy>=1.25.0; python_version >= \"3.9\", but you have numpy 1.23.5 which is incompatible.\u001b[0m\u001b[31m\n",
            "\u001b[0mSuccessfully installed DataProperty-1.0.1 accelerate-0.32.1 aioprometheus-23.12.0 cchardet-2.1.7 colorama-0.4.6 coloredlogs-15.0.1 datasets-2.20.0 dill-0.3.8 dnspython-2.6.1 einops-0.8.0 email_validator-2.2.0 evaluate-0.4.2 fastapi-0.111.0 fastapi-cli-0.0.4 fschat-0.2.35 h11-0.14.0 httpcore-1.0.5 httptools-0.6.1 httpx-0.27.0 humanfriendly-10.0 intel_extension_for_pytorch-2.3.0 jsonlines-4.0.0 latex2mathml-3.77.0 lm-eval-0.4.3 markdown2-2.5.0 mbstrdecoder-1.1.3 multiprocess-0.70.16 neural_speed-1.0a0 nh3-0.2.18 ninja-1.11.1.1 numpy-1.23.5 nvidia-cublas-cu12-12.1.3.1 nvidia-cuda-cupti-cu12-12.1.105 nvidia-cuda-nvrtc-cu12-12.1.105 nvidia-cuda-runtime-cu12-12.1.105 nvidia-cudnn-cu12-8.9.2.26 nvidia-cufft-cu12-11.0.2.54 nvidia-curand-cu12-10.3.2.106 nvidia-cusolver-cu12-11.4.5.107 nvidia-cusparse-cu12-12.1.0.106 nvidia-nccl-cu12-2.20.5 nvidia-nvjitlink-cu12-12.5.82 nvidia-nvtx-cu12-12.1.105 onnx-1.16.1 optimum-1.21.2 optimum-intel-1.18.1 orjson-3.10.6 pathvalidate-3.2.0 peft-0.6.2 portalocker-2.10.1 pyarrow-16.1.0 pybind11-2.13.1 pydantic-1.10.13 pytablewriter-1.2.0 python-dotenv-1.0.1 python-multipart-0.0.9 quantile-python-1.1 ray-2.32.0 requests-2.32.3 rouge_score-0.1.2 sacrebleu-2.4.2 sacremoses-0.1.1 shortuuid-1.0.13 sqlitedict-2.1.0 starlette-0.37.2 svgwrite-1.4.3 tabledata-1.3.3 tcolorpy-0.1.6 tiktoken-0.7.0 tqdm-multiprocess-0.0.11 transformers_stream_generator-0.0.5 typepy-1.3.2 ujson-5.10.0 uvicorn-0.30.1 uvloop-0.19.0 vllm-0.2.5 watchfiles-0.22.0 wavedrom-2.0.3.post3 websockets-12.0 word2number-1.1 xformers-0.0.26.post1 xxhash-3.4.1 yacs-0.1.8 zstandard-0.22.0\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.colab-display-data+json": {
              "pip_warning": {
                "packages": [
                  "numpy"
                ]
              },
              "id": "24fead7791f84efdb8da6318136bfaee"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Prepare the Dataset\n",
        "\n",
        "Text Generation (General domain instruction): We use the [Alpaca dataset](https://github.com/tatsu-lab/stanford_alpaca) from Stanford University as the general domain dataset to fine-tune the model. This dataset is provided in the form of a JSON file, [alpaca_data.json](https://github.com/tatsu-lab/stanford_alpaca/blob/main/alpaca_data.json). In Alpaca, researchers have manually crafted 175 seed tasks to guide `text-davinci-003` in generating 52K instruction data for diverse tasks."
      ],
      "metadata": {
        "id": "V6XaANCdkCo5"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Finetune Your Chatbot\n",
        "\n",
        "We employ the [LoRA approach](https://arxiv.org/pdf/2106.09685.pdf) to finetune the LLM efficiently."
      ],
      "metadata": {
        "id": "AdyykZ5bj1PR"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Finetune the model on Alpaca-format dataset to conduct text generation:"
      ],
      "metadata": {
        "id": "JOyi-HZLjn2s"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import TrainingArguments\n",
        "from intel_extension_for_transformers.neural_chat.config import (\n",
        "    ModelArguments,\n",
        "    DataArguments,\n",
        "    FinetuningArguments,\n",
        "    TextGenerationFinetuningConfig,\n",
        ")\n",
        "from intel_extension_for_transformers.neural_chat.chatbot import finetune_model\n",
        "model_args = ModelArguments(model_name_or_path=\"TinyLlama/TinyLlama_v1.1\")\n",
        "data_args = DataArguments(train_file=\"alpaca_data.json\", validation_split_percentage=1)\n",
        "training_args = TrainingArguments(\n",
        "    output_dir='./tmp',\n",
        "    do_train=True,\n",
        "    do_eval=True,\n",
        "    num_train_epochs=3,\n",
        "    overwrite_output_dir=True,\n",
        "    per_device_train_batch_size=4,\n",
        "    per_device_eval_batch_size=4,\n",
        "    gradient_accumulation_steps=2,\n",
        "    save_strategy=\"no\",\n",
        "    log_level=\"info\",\n",
        "    save_total_limit=2,\n",
        "    bf16=True,\n",
        ")\n",
        "finetune_args = FinetuningArguments()\n",
        "finetune_cfg = TextGenerationFinetuningConfig(\n",
        "            model_args=model_args,\n",
        "            data_args=data_args,\n",
        "            training_args=training_args,\n",
        "            finetune_args=finetune_args,\n",
        "        )\n",
        "finetune_model(finetune_cfg)"
      ],
      "metadata": {
        "colab": {
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        "id": "WKh0qLmkVmc4",
        "outputId": "77ebc4b9-6edb-43ff-db02-667a7bf11262"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:intel_extension_for_transformers.transformers.llm.finetuning.finetuning:Process rank: 0, device: cuda:0\n",
            "distributed training: True, 16-bits training: True\n",
            "INFO:intel_extension_for_transformers.transformers.llm.finetuning.finetuning:Training/evaluation parameters TrainingArguments(\n",
            "_n_gpu=1,\n",
            "accelerator_config={'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None},\n",
            "adafactor=False,\n",
            "adam_beta1=0.9,\n",
            "adam_beta2=0.999,\n",
            "adam_epsilon=1e-08,\n",
            "auto_find_batch_size=False,\n",
            "batch_eval_metrics=False,\n",
            "bf16=True,\n",
            "bf16_full_eval=False,\n",
            "data_seed=None,\n",
            "dataloader_drop_last=False,\n",
            "dataloader_num_workers=0,\n",
            "dataloader_persistent_workers=False,\n",
            "dataloader_pin_memory=True,\n",
            "dataloader_prefetch_factor=None,\n",
            "ddp_backend=None,\n",
            "ddp_broadcast_buffers=None,\n",
            "ddp_bucket_cap_mb=None,\n",
            "ddp_find_unused_parameters=None,\n",
            "ddp_timeout=1800,\n",
            "debug=[],\n",
            "deepspeed=None,\n",
            "disable_tqdm=False,\n",
            "dispatch_batches=None,\n",
            "do_eval=True,\n",
            "do_predict=False,\n",
            "do_train=True,\n",
            "eval_accumulation_steps=None,\n",
            "eval_delay=0,\n",
            "eval_do_concat_batches=True,\n",
            "eval_steps=None,\n",
            "eval_strategy=no,\n",
            "evaluation_strategy=None,\n",
            "fp16=False,\n",
            "fp16_backend=auto,\n",
            "fp16_full_eval=False,\n",
            "fp16_opt_level=O1,\n",
            "fsdp=[],\n",
            "fsdp_config={'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False},\n",
            "fsdp_min_num_params=0,\n",
            "fsdp_transformer_layer_cls_to_wrap=None,\n",
            "full_determinism=False,\n",
            "gradient_accumulation_steps=2,\n",
            "gradient_checkpointing=False,\n",
            "gradient_checkpointing_kwargs=None,\n",
            "greater_is_better=None,\n",
            "group_by_length=False,\n",
            "half_precision_backend=auto,\n",
            "hub_always_push=False,\n",
            "hub_model_id=None,\n",
            "hub_private_repo=False,\n",
            "hub_strategy=every_save,\n",
            "hub_token=<HUB_TOKEN>,\n",
            "ignore_data_skip=False,\n",
            "include_inputs_for_metrics=False,\n",
            "include_num_input_tokens_seen=False,\n",
            "include_tokens_per_second=False,\n",
            "jit_mode_eval=False,\n",
            "label_names=None,\n",
            "label_smoothing_factor=0.0,\n",
            "learning_rate=5e-05,\n",
            "length_column_name=length,\n",
            "load_best_model_at_end=False,\n",
            "local_rank=0,\n",
            "log_level=info,\n",
            "log_level_replica=warning,\n",
            "log_on_each_node=True,\n",
            "logging_dir=./tmp/runs/Jul14_08-24-34_2931100ef06b,\n",
            "logging_first_step=False,\n",
            "logging_nan_inf_filter=True,\n",
            "logging_steps=500,\n",
            "logging_strategy=steps,\n",
            "lr_scheduler_kwargs={},\n",
            "lr_scheduler_type=linear,\n",
            "max_grad_norm=1.0,\n",
            "max_steps=-1,\n",
            "metric_for_best_model=None,\n",
            "mp_parameters=,\n",
            "neftune_noise_alpha=None,\n",
            "no_cuda=False,\n",
            "num_train_epochs=3,\n",
            "optim=adamw_torch,\n",
            "optim_args=None,\n",
            "optim_target_modules=None,\n",
            "output_dir=./tmp,\n",
            "overwrite_output_dir=True,\n",
            "past_index=-1,\n",
            "per_device_eval_batch_size=4,\n",
            "per_device_train_batch_size=4,\n",
            "prediction_loss_only=False,\n",
            "push_to_hub=False,\n",
            "push_to_hub_model_id=None,\n",
            "push_to_hub_organization=None,\n",
            "push_to_hub_token=<PUSH_TO_HUB_TOKEN>,\n",
            "ray_scope=last,\n",
            "remove_unused_columns=True,\n",
            "report_to=['tensorboard'],\n",
            "restore_callback_states_from_checkpoint=False,\n",
            "resume_from_checkpoint=None,\n",
            "run_name=./tmp,\n",
            "save_on_each_node=False,\n",
            "save_only_model=False,\n",
            "save_safetensors=True,\n",
            "save_steps=500,\n",
            "save_strategy=no,\n",
            "save_total_limit=2,\n",
            "seed=42,\n",
            "skip_memory_metrics=True,\n",
            "split_batches=None,\n",
            "tf32=None,\n",
            "torch_compile=False,\n",
            "torch_compile_backend=None,\n",
            "torch_compile_mode=None,\n",
            "torchdynamo=None,\n",
            "tpu_metrics_debug=False,\n",
            "tpu_num_cores=None,\n",
            "use_cpu=False,\n",
            "use_ipex=False,\n",
            "use_legacy_prediction_loop=False,\n",
            "use_mps_device=False,\n",
            "warmup_ratio=0.0,\n",
            "warmup_steps=0,\n",
            "weight_decay=0.0,\n",
            ")\n",
            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "58644e1496df4219887edaf32ab81aa0"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[INFO|configuration_utils.py:733] 2024-07-14 08:24:45,166 >> loading configuration file config.json from cache at /root/.cache/huggingface/hub/models--TinyLlama--TinyLlama_v1.1/snapshots/ff3c701f2424c7625fdefb9dd470f45ef18b02d6/config.json\n",
            "[INFO|configuration_utils.py:796] 2024-07-14 08:24:45,177 >> Model config LlamaConfig {\n",
            "  \"_name_or_path\": \"TinyLlama/TinyLlama_v1.1\",\n",
            "  \"architectures\": [\n",
            "    \"LlamaForCausalLM\"\n",
            "  ],\n",
            "  \"attention_bias\": false,\n",
            "  \"attention_dropout\": 0.0,\n",
            "  \"bos_token_id\": 1,\n",
            "  \"eos_token_id\": 2,\n",
            "  \"hidden_act\": \"silu\",\n",
            "  \"hidden_size\": 2048,\n",
            "  \"initializer_range\": 0.02,\n",
            "  \"intermediate_size\": 5632,\n",
            "  \"max_position_embeddings\": 2048,\n",
            "  \"mlp_bias\": false,\n",
            "  \"model_type\": \"llama\",\n",
            "  \"num_attention_heads\": 32,\n",
            "  \"num_hidden_layers\": 22,\n",
            "  \"num_key_value_heads\": 4,\n",
            "  \"pretraining_tp\": 1,\n",
            "  \"rms_norm_eps\": 1e-05,\n",
            "  \"rope_scaling\": null,\n",
            "  \"rope_theta\": 10000.0,\n",
            "  \"tie_word_embeddings\": false,\n",
            "  \"torch_dtype\": \"float32\",\n",
            "  \"transformers_version\": \"4.41.2\",\n",
            "  \"use_cache\": true,\n",
            "  \"vocab_size\": 32000\n",
            "}\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "a8d5cf05ff5941f0bf60d5203a911252"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "d05faedc3d1e450799e602913cf49555"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "a0c735bb4c3346b58ec8f99e77da55d2"
            }
          },
          "metadata": {}
        },
        {
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "353e5fc0fe1f42d8a9c965cd6cce1453"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[INFO|tokenization_utils_base.py:2108] 2024-07-14 08:24:46,603 >> loading file tokenizer.model from cache at /root/.cache/huggingface/hub/models--TinyLlama--TinyLlama_v1.1/snapshots/ff3c701f2424c7625fdefb9dd470f45ef18b02d6/tokenizer.model\n",
            "[INFO|tokenization_utils_base.py:2108] 2024-07-14 08:24:46,605 >> loading file added_tokens.json from cache at None\n",
            "[INFO|tokenization_utils_base.py:2108] 2024-07-14 08:24:46,607 >> loading file special_tokens_map.json from cache at /root/.cache/huggingface/hub/models--TinyLlama--TinyLlama_v1.1/snapshots/ff3c701f2424c7625fdefb9dd470f45ef18b02d6/special_tokens_map.json\n",
            "[INFO|tokenization_utils_base.py:2108] 2024-07-14 08:24:46,608 >> loading file tokenizer_config.json from cache at /root/.cache/huggingface/hub/models--TinyLlama--TinyLlama_v1.1/snapshots/ff3c701f2424c7625fdefb9dd470f45ef18b02d6/tokenizer_config.json\n",
            "[INFO|tokenization_utils_base.py:2108] 2024-07-14 08:24:46,609 >> loading file tokenizer.json from cache at /root/.cache/huggingface/hub/models--TinyLlama--TinyLlama_v1.1/snapshots/ff3c701f2424c7625fdefb9dd470f45ef18b02d6/tokenizer.json\n",
            "Using custom data configuration default-0a7894dfd4d2d325\n",
            "INFO:datasets.builder:Using custom data configuration default-0a7894dfd4d2d325\n",
            "Loading Dataset Infos from /usr/local/lib/python3.10/dist-packages/datasets/packaged_modules/json\n",
            "INFO:datasets.info:Loading Dataset Infos from /usr/local/lib/python3.10/dist-packages/datasets/packaged_modules/json\n",
            "Generating dataset json (/root/.cache/huggingface/datasets/json/default-0a7894dfd4d2d325/0.0.0/7483f22a71512872c377524b97484f6d20c275799bb9e7cd8fb3198178d8220a)\n",
            "INFO:datasets.builder:Generating dataset json (/root/.cache/huggingface/datasets/json/default-0a7894dfd4d2d325/0.0.0/7483f22a71512872c377524b97484f6d20c275799bb9e7cd8fb3198178d8220a)\n",
            "Downloading and preparing dataset json/default to /root/.cache/huggingface/datasets/json/default-0a7894dfd4d2d325/0.0.0/7483f22a71512872c377524b97484f6d20c275799bb9e7cd8fb3198178d8220a...\n",
            "INFO:datasets.builder:Downloading and preparing dataset json/default to /root/.cache/huggingface/datasets/json/default-0a7894dfd4d2d325/0.0.0/7483f22a71512872c377524b97484f6d20c275799bb9e7cd8fb3198178d8220a...\n",
            "Downloading took 0.0 min\n",
            "INFO:datasets.download.download_manager:Downloading took 0.0 min\n",
            "Checksum Computation took 0.0 min\n",
            "INFO:datasets.download.download_manager:Checksum Computation took 0.0 min\n",
            "Generating train split\n",
            "INFO:datasets.builder:Generating train split\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating train split: 0 examples [00:00, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "30c07ccfbdf64e09bc61c33e03c6fa9d"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Unable to verify splits sizes.\n",
            "INFO:datasets.utils.info_utils:Unable to verify splits sizes.\n",
            "Dataset json downloaded and prepared to /root/.cache/huggingface/datasets/json/default-0a7894dfd4d2d325/0.0.0/7483f22a71512872c377524b97484f6d20c275799bb9e7cd8fb3198178d8220a. Subsequent calls will reuse this data.\n",
            "INFO:datasets.builder:Dataset json downloaded and prepared to /root/.cache/huggingface/datasets/json/default-0a7894dfd4d2d325/0.0.0/7483f22a71512872c377524b97484f6d20c275799bb9e7cd8fb3198178d8220a. Subsequent calls will reuse this data.\n",
            "Using custom data configuration default-0a7894dfd4d2d325\n",
            "INFO:datasets.builder:Using custom data configuration default-0a7894dfd4d2d325\n",
            "Loading Dataset Infos from /usr/local/lib/python3.10/dist-packages/datasets/packaged_modules/json\n",
            "INFO:datasets.info:Loading Dataset Infos from /usr/local/lib/python3.10/dist-packages/datasets/packaged_modules/json\n",
            "Overwrite dataset info from restored data version if exists.\n",
            "INFO:datasets.builder:Overwrite dataset info from restored data version if exists.\n",
            "Loading Dataset info from /root/.cache/huggingface/datasets/json/default-0a7894dfd4d2d325/0.0.0/7483f22a71512872c377524b97484f6d20c275799bb9e7cd8fb3198178d8220a\n",
            "INFO:datasets.info:Loading Dataset info from /root/.cache/huggingface/datasets/json/default-0a7894dfd4d2d325/0.0.0/7483f22a71512872c377524b97484f6d20c275799bb9e7cd8fb3198178d8220a\n",
            "Found cached dataset json (/root/.cache/huggingface/datasets/json/default-0a7894dfd4d2d325/0.0.0/7483f22a71512872c377524b97484f6d20c275799bb9e7cd8fb3198178d8220a)\n",
            "INFO:datasets.builder:Found cached dataset json (/root/.cache/huggingface/datasets/json/default-0a7894dfd4d2d325/0.0.0/7483f22a71512872c377524b97484f6d20c275799bb9e7cd8fb3198178d8220a)\n",
            "Loading Dataset info from /root/.cache/huggingface/datasets/json/default-0a7894dfd4d2d325/0.0.0/7483f22a71512872c377524b97484f6d20c275799bb9e7cd8fb3198178d8220a\n",
            "INFO:datasets.info:Loading Dataset info from /root/.cache/huggingface/datasets/json/default-0a7894dfd4d2d325/0.0.0/7483f22a71512872c377524b97484f6d20c275799bb9e7cd8fb3198178d8220a\n",
            "Using custom data configuration default-0a7894dfd4d2d325\n",
            "INFO:datasets.builder:Using custom data configuration default-0a7894dfd4d2d325\n",
            "Loading Dataset Infos from /usr/local/lib/python3.10/dist-packages/datasets/packaged_modules/json\n",
            "INFO:datasets.info:Loading Dataset Infos from /usr/local/lib/python3.10/dist-packages/datasets/packaged_modules/json\n",
            "Overwrite dataset info from restored data version if exists.\n",
            "INFO:datasets.builder:Overwrite dataset info from restored data version if exists.\n",
            "Loading Dataset info from /root/.cache/huggingface/datasets/json/default-0a7894dfd4d2d325/0.0.0/7483f22a71512872c377524b97484f6d20c275799bb9e7cd8fb3198178d8220a\n",
            "INFO:datasets.info:Loading Dataset info from /root/.cache/huggingface/datasets/json/default-0a7894dfd4d2d325/0.0.0/7483f22a71512872c377524b97484f6d20c275799bb9e7cd8fb3198178d8220a\n",
            "Found cached dataset json (/root/.cache/huggingface/datasets/json/default-0a7894dfd4d2d325/0.0.0/7483f22a71512872c377524b97484f6d20c275799bb9e7cd8fb3198178d8220a)\n",
            "INFO:datasets.builder:Found cached dataset json (/root/.cache/huggingface/datasets/json/default-0a7894dfd4d2d325/0.0.0/7483f22a71512872c377524b97484f6d20c275799bb9e7cd8fb3198178d8220a)\n",
            "Loading Dataset info from /root/.cache/huggingface/datasets/json/default-0a7894dfd4d2d325/0.0.0/7483f22a71512872c377524b97484f6d20c275799bb9e7cd8fb3198178d8220a\n",
            "INFO:datasets.info:Loading Dataset info from /root/.cache/huggingface/datasets/json/default-0a7894dfd4d2d325/0.0.0/7483f22a71512872c377524b97484f6d20c275799bb9e7cd8fb3198178d8220a\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "d0d2ea2b1ffd4ef89e037813c3e249fa"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[INFO|modeling_utils.py:3474] 2024-07-14 08:25:17,508 >> loading weights file pytorch_model.bin from cache at /root/.cache/huggingface/hub/models--TinyLlama--TinyLlama_v1.1/snapshots/ff3c701f2424c7625fdefb9dd470f45ef18b02d6/pytorch_model.bin\n",
            "[INFO|modeling_utils.py:1519] 2024-07-14 08:25:17,566 >> Instantiating LlamaForCausalLM model under default dtype torch.bfloat16.\n",
            "[INFO|configuration_utils.py:962] 2024-07-14 08:25:17,570 >> Generate config GenerationConfig {\n",
            "  \"bos_token_id\": 1,\n",
            "  \"eos_token_id\": 2\n",
            "}\n",
            "\n",
            "[INFO|modeling_utils.py:4280] 2024-07-14 08:25:30,181 >> All model checkpoint weights were used when initializing LlamaForCausalLM.\n",
            "\n",
            "[INFO|modeling_utils.py:4288] 2024-07-14 08:25:30,184 >> All the weights of LlamaForCausalLM were initialized from the model checkpoint at TinyLlama/TinyLlama_v1.1.\n",
            "If your task is similar to the task the model of the checkpoint was trained on, you can already use LlamaForCausalLM for predictions without further training.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "3016e5e714464ac6b2d7df0d7afaebc3"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[INFO|configuration_utils.py:917] 2024-07-14 08:25:30,401 >> loading configuration file generation_config.json from cache at /root/.cache/huggingface/hub/models--TinyLlama--TinyLlama_v1.1/snapshots/ff3c701f2424c7625fdefb9dd470f45ef18b02d6/generation_config.json\n",
            "[INFO|configuration_utils.py:962] 2024-07-14 08:25:30,403 >> Generate config GenerationConfig {\n",
            "  \"bos_token_id\": 1,\n",
            "  \"eos_token_id\": 2,\n",
            "  \"max_length\": 2048,\n",
            "  \"pad_token_id\": 0\n",
            "}\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "ef85047f92714e328c94b14cdd787323"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Caching processed dataset at /root/.cache/huggingface/datasets/json/default-0a7894dfd4d2d325/0.0.0/7483f22a71512872c377524b97484f6d20c275799bb9e7cd8fb3198178d8220a/cache-54b5bb5eca928a20.arrow\n",
            "INFO:datasets.arrow_dataset:Caching processed dataset at /root/.cache/huggingface/datasets/json/default-0a7894dfd4d2d325/0.0.0/7483f22a71512872c377524b97484f6d20c275799bb9e7cd8fb3198178d8220a/cache-54b5bb5eca928a20.arrow\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "091b8843d9be41198c839194ed8f6973"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Caching processed dataset at /root/.cache/huggingface/datasets/json/default-0a7894dfd4d2d325/0.0.0/7483f22a71512872c377524b97484f6d20c275799bb9e7cd8fb3198178d8220a/cache-81bedc7f6123cf5c.arrow\n",
            "INFO:datasets.arrow_dataset:Caching processed dataset at /root/.cache/huggingface/datasets/json/default-0a7894dfd4d2d325/0.0.0/7483f22a71512872c377524b97484f6d20c275799bb9e7cd8fb3198178d8220a/cache-81bedc7f6123cf5c.arrow\n",
            "INFO:intel_extension_for_transformers.transformers.llm.finetuning.finetuning:Using data collator of type DataCollatorForSeq2Seq\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "trainable params: 1,126,400 || all params: 1,101,174,784 || trainable%: 0.10229075496156657\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[INFO|trainer.py:642] 2024-07-14 09:09:36,984 >> Using auto half precision backend\n",
            "[INFO|trainer.py:2128] 2024-07-14 09:09:37,456 >> ***** Running training *****\n",
            "[INFO|trainer.py:2129] 2024-07-14 09:09:37,457 >>   Num examples = 51,482\n",
            "[INFO|trainer.py:2130] 2024-07-14 09:09:37,457 >>   Num Epochs = 3\n",
            "[INFO|trainer.py:2131] 2024-07-14 09:09:37,458 >>   Instantaneous batch size per device = 4\n",
            "[INFO|trainer.py:2134] 2024-07-14 09:09:37,459 >>   Total train batch size (w. parallel, distributed & accumulation) = 8\n",
            "[INFO|trainer.py:2135] 2024-07-14 09:09:37,460 >>   Gradient Accumulation steps = 2\n",
            "[INFO|trainer.py:2136] 2024-07-14 09:09:37,460 >>   Total optimization steps = 19,305\n",
            "[INFO|trainer.py:2137] 2024-07-14 09:09:37,464 >>   Number of trainable parameters = 1,126,400\n"
          ]
        },
                {
          "output_type": "display_data",
          "data": {
            "text/plain": "<IPython.core.display.HTML object>",
            "text/html": "\n    <div>\n      \n      <progress value='19305' max='19305' style='width:300px; height:20px; vertical-align: middle;'></progress>\n      [19305/19305 8:03:20, Epoch 2/3]\n    </div>\n    <table border=\"1\" class=\"dataframe\">\n  <thead>\n <tr style=\"text-align: left;\">\n      <th>Step</th>\n      <th>Training Loss</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <td>500</td>\n      <td>1.362100</td>\n    </tr>\n    <tr>\n      <td>1000</td>\n      <td>1.311200</td>\n    </tr>\n    <tr>\n      <td>1500</td>\n      <td>1.291400</td>\n    </tr>\n    <tr>\n      <td>2000</td>\n      <td>1.305900</td>\n    </tr>\n    <tr>\n      <td>2500</td>\n      <td>1.297400</td>\n    </tr>\n    <tr>\n      <td>3000</td>\n      <td>1.307700</td>\n    </tr>\n    <tr>\n      <td>3500</td>\n      <td>1.303200</td>\n    </tr>\n    <tr>\n      <td>4000</td>\n      <td>1.289000</td>\n    </tr>\n    <tr>\n      <td>4500</td>\n      <td>1.288000</td>\n    </tr>\n    <tr>\n      <td>5000</td>\n      <td>1.290300</td>\n    </tr>\n    <tr>\n      <td>5500</td>\n      <td>1.285500</td>\n    </tr>\n    <tr>\n      <td>6000</td>\n      <td>1.290600</td>\n    </tr>\n    <tr>\n      <td>6500</td>\n      <td>1.292900</td>\n    </tr>\n    <tr>\n      <td>7000</td>\n      <td>1.286600</td>\n    </tr>\n    <tr>\n      <td>7500</td>\n      <td>1.289800</td>\n    </tr>\n    <tr>\n      <td>8000</td>\n      <td>1.278600</td>\n    </tr>\n    <tr>\n      <td>8500</td>\n      <td>1.273900</td>\n    </tr>\n    <tr>\n      <td>9000</td>\n      <td>1.270700</td>\n    </tr>\n    <tr>\n      <td>9500</td>\n      <td>1.287000</td>\n    </tr>\n    <tr>\n      <td>10000</td>\n      <td>1.278500</td>\n    </tr>\n    <tr>\n      <td>10500</td>\n      <td>1.281300</td>\n    </tr>\n    <tr>\n      <td>11000</td>\n      <td>1.294800</td>\n    </tr>\n    <tr>\n      <td>11500</td>\n      <td>1.303800</td>\n    </tr>\n    <tr>\n      <td>12000</td>\n      <td>1.286300</td>\n    </tr>\n    <tr>\n      <td>12500</td>\n      <td>1.279800</td>\n    </tr>\n    <tr>\n      <td>13000</td>\n      <td>1.286300</td>\n    </tr>\n    <tr>\n      <td>13500</td>\n      <td>1.281800</td>\n    </tr>\n    <tr>\n      <td>14000</td>\n      <td>1.289300</td>\n    </tr>\n    <tr>\n      <td>14500</td>\n      <td>1.281300</td>\n    </tr>\n    <tr>\n      <td>15000</td>\n      <td>1.275600</td>\n    </tr>\n    <tr>\n      <td>15500</td>\n      <td>1.282300</td>\n    </tr>\n    <tr>\n      <td>16000</td>\n      <td>1.288200</td>\n    </tr>\n    <tr>\n      <td>16500</td>\n      <td>1.291100</td>\n    </tr>\n    <tr>\n      <td>17000</td>\n      <td>1.271300</td>\n    </tr>\n    <tr>\n      <td>17500</td>\n      <td>1.289600</td>\n    </tr>\n    <tr>\n      <td>18000</td>\n      <td>1.293200</td>\n    </tr>\n    <tr>\n      <td>18500</td>\n      <td>1.296000</td>\n    </tr>\n    <tr>\n      <td>19000</td>\n      <td>1.286500</td>\n    </tr>\n  </tbody>\n</table><p>"
          },
          "metadata": {}
        },
        {
          "name": "stderr",
          "text": "[INFO|trainer.py:2383] 2024-07-14 17:13:00,328 >> \n\nTraining completed. Do not forget to share your model on huggingface.co/models =)\n\n\n[INFO|trainer.py:3478] 2024-07-14 17:13:00,333 >> Saving model checkpoint to ./tmp\n[INFO|tokenization_utils_base.py:2574] 2024-07-14 17:13:00,376 >> tokenizer config file saved in ./tmp/tokenizer_config.json\n[INFO|tokenization_utils_base.py:2583] 2024-07-14 17:13:00,377 >> Special tokens file saved in ./tmp/special_tokens_map.json\n[INFO|trainer.py:3788] 2024-07-14 17:13:00,383 >> \n***** Running Evaluation *****\n[INFO|trainer.py:3790] 2024-07-14 17:13:00,384 >>   Num examples = 520\n[INFO|trainer.py:3793] 2024-07-14 17:13:00,384 >>   Batch size = 4\n",
          "output_type": "stream"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<IPython.core.display.HTML object>",
            "text/html": "\n    <div>\n      \n      <progress value='130' max='130' style='width:300px; height:20px; vertical-align: middle;'></progress>\n      [130/130 00:43]\n    </div>\n    "
          },
          "metadata": {}
        },
        {
          "name": "stdout",
          "text": "***** eval metrics *****\n  epoch                   =     2.9998\n  eval_loss               =     1.2866\n  eval_ppl                =     3.6205\n  eval_runtime            = 0:00:43.64\n  eval_samples            =        520\n  eval_samples_per_second =     11.914\n  eval_steps_per_second   =      2.978\n",
          "output_type": "stream"
        }
      ]
    }
  ]
}