CsmTrnslt.ipynb

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  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Andron00e/Fine-Tuning-project/blob/main/CsmTrnslt.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UJyxR59ZfiKq",
        "outputId": "e7b1e165-d1ba-4402-ce46-fc09bc19d263"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "  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",
            "  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",
            "Requirement already satisfied: accelerate in /usr/local/lib/python3.10/dist-packages (0.21.0)\n",
            "Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.10/dist-packages (from accelerate) (1.22.4)\n",
            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from accelerate) (23.1)\n",
            "Requirement already satisfied: psutil in /usr/local/lib/python3.10/dist-packages (from accelerate) (5.9.5)\n",
            "Requirement already satisfied: pyyaml in /usr/local/lib/python3.10/dist-packages (from accelerate) (6.0)\n",
            "Requirement already satisfied: torch>=1.10.0 in /usr/local/lib/python3.10/dist-packages (from accelerate) (2.0.1+cu118)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate) (3.12.2)\n",
            "Requirement already satisfied: typing-extensions in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate) (4.7.1)\n",
            "Requirement already satisfied: sympy in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate) (1.11.1)\n",
            "Requirement already satisfied: networkx in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate) (3.1)\n",
            "Requirement already satisfied: jinja2 in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate) (3.1.2)\n",
            "Requirement already satisfied: triton==2.0.0 in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate) (2.0.0)\n",
            "Requirement already satisfied: cmake in /usr/local/lib/python3.10/dist-packages (from triton==2.0.0->torch>=1.10.0->accelerate) (3.25.2)\n",
            "Requirement already satisfied: lit in /usr/local/lib/python3.10/dist-packages (from triton==2.0.0->torch>=1.10.0->accelerate) (16.0.6)\n",
            "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from jinja2->torch>=1.10.0->accelerate) (2.1.3)\n",
            "Requirement already satisfied: mpmath>=0.19 in /usr/local/lib/python3.10/dist-packages (from sympy->torch>=1.10.0->accelerate) (1.3.0)\n",
            "Requirement already satisfied: bitsandbytes in /usr/local/lib/python3.10/dist-packages (0.40.2)\n",
            "Requirement already satisfied: sentencepiece in /usr/local/lib/python3.10/dist-packages (0.1.99)\n",
            "Requirement already satisfied: datasets in /usr/local/lib/python3.10/dist-packages (2.13.1)\n",
            "Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.10/dist-packages (from datasets) (1.22.4)\n",
            "Requirement already satisfied: pyarrow>=8.0.0 in /usr/local/lib/python3.10/dist-packages (from datasets) (9.0.0)\n",
            "Requirement already satisfied: dill<0.3.7,>=0.3.0 in /usr/local/lib/python3.10/dist-packages (from datasets) (0.3.6)\n",
            "Requirement already satisfied: pandas in /usr/local/lib/python3.10/dist-packages (from datasets) (1.5.3)\n",
            "Requirement already satisfied: requests>=2.19.0 in /usr/local/lib/python3.10/dist-packages (from datasets) (2.27.1)\n",
            "Requirement already satisfied: tqdm>=4.62.1 in /usr/local/lib/python3.10/dist-packages (from datasets) (4.65.0)\n",
            "Requirement already satisfied: xxhash in /usr/local/lib/python3.10/dist-packages (from datasets) (3.2.0)\n",
            "Requirement already satisfied: multiprocess in /usr/local/lib/python3.10/dist-packages (from datasets) (0.70.14)\n",
            "Requirement already satisfied: fsspec[http]>=2021.11.1 in /usr/local/lib/python3.10/dist-packages (from datasets) (2023.6.0)\n",
            "Requirement already satisfied: aiohttp in /usr/local/lib/python3.10/dist-packages (from datasets) (3.8.4)\n",
            "Requirement already satisfied: huggingface-hub<1.0.0,>=0.11.0 in /usr/local/lib/python3.10/dist-packages (from datasets) (0.16.4)\n",
            "Requirement already satisfied: packaging in /usr/local/lib/python3.10/dist-packages (from datasets) (23.1)\n",
            "Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.10/dist-packages (from datasets) (6.0)\n",
            "Requirement already satisfied: attrs>=17.3.0 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets) (23.1.0)\n",
            "Requirement already satisfied: charset-normalizer<4.0,>=2.0 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets) (2.0.12)\n",
            "Requirement already satisfied: multidict<7.0,>=4.5 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets) (6.0.4)\n",
            "Requirement already satisfied: async-timeout<5.0,>=4.0.0a3 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets) (4.0.2)\n",
            "Requirement already satisfied: yarl<2.0,>=1.0 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets) (1.9.2)\n",
            "Requirement already satisfied: frozenlist>=1.1.1 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets) (1.4.0)\n",
            "Requirement already satisfied: aiosignal>=1.1.2 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets) (1.3.1)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from huggingface-hub<1.0.0,>=0.11.0->datasets) (3.12.2)\n",
            "Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub<1.0.0,>=0.11.0->datasets) (4.7.1)\n",
            "Requirement already satisfied: urllib3<1.27,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests>=2.19.0->datasets) (1.26.16)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests>=2.19.0->datasets) (2023.5.7)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests>=2.19.0->datasets) (3.4)\n",
            "Requirement already satisfied: python-dateutil>=2.8.1 in /usr/local/lib/python3.10/dist-packages (from pandas->datasets) (2.8.2)\n",
            "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas->datasets) (2022.7.1)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.8.1->pandas->datasets) (1.16.0)\n",
            "\u001b[33mWARNING: Skipping wandb as it is not installed.\u001b[0m\u001b[33m\n",
            "\u001b[0m"
          ]
        }
      ],
      "source": [
        "!pip install -q git+https://github.com/huggingface/peft.git git+https://github.com/huggingface/transformers.git\n",
        "!pip install accelerate\n",
        "!pip install bitsandbytes\n",
        "!pip install sentencepiece\n",
        "!pip install datasets\n",
        "!pip uninstall wandb"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import torch\n",
        "torch.cuda.is_available()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jxQJoUjKf-wU",
        "outputId": "830fe13d-5374-4669-926e-e692f7fd7b5c"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {},
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "torch.cuda.empty_cache()"
      ],
      "metadata": {
        "id": "a8WWyedX4PpC"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "os.environ[\"CUDA_VISIBLE_DEVICES\"]=\"0\"\n",
        "import torch.nn as nn\n",
        "import bitsandbytes as bnb\n",
        "from transformers import AutoTokenizer, AutoModelForCausalLM\n",
        "\n",
        "tokenizer = AutoTokenizer.from_pretrained(\"openlm-research/open_llama_3b\")\n",
        "tokenizer.pad_token_id = (\n",
        "    0  # unk. we want this to be different from the eos token\n",
        ")\n",
        "tokenizer.padding_side = \"left\"\n",
        "tokenizer"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fQhTyvaogDzh",
        "outputId": "86a907cb-c56e-4cb8-d337-4191acc6dcf9"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LlamaTokenizerFast(name_or_path='openlm-research/open_llama_3b', vocab_size=32000, model_max_length=2048, is_fast=True, padding_side='left', truncation_side='right', special_tokens={'bos_token': AddedToken(\"<s>\", rstrip=False, lstrip=False, single_word=False, normalized=True), 'eos_token': AddedToken(\"</s>\", rstrip=False, lstrip=False, single_word=False, normalized=True), 'unk_token': AddedToken(\"<unk>\", rstrip=False, lstrip=False, single_word=False, normalized=True), 'pad_token': '<unk>'}, clean_up_tokenization_spaces=False)"
            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model = AutoModelForCausalLM.from_pretrained(\n",
        "    \"openlm-research/open_llama_3b\",\n",
        "    torch_dtype=torch.float16,\n",
        "    load_in_8bit=True,\n",
        "    device_map='auto',\n",
        ")\n",
        "print(model)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 491,
          "referenced_widgets": [
            "e77097f6b4ac434cb6f9a9dfedb77307",
            "b6d1030523d5458196c58c6093bc092c",
            "d18f10a0c5d9465fa4bea722207d931c",
            "5741235c4eae4afeb928b683eed7ead2",
            "0dd4ab7fba7b4de08aa4c3d8cc371a24",
            "39e90b820dc64c5e80e301169f730880",
            "5c5042660d6749c98dae0942ae84ad98",
            "5c67d1ef74d749c8aa05819c7907df08",
            "0a13e76989594933ba8ee1bc952c3a5f",
            "a620a56d00c548e6a9154a2e67d1cfef",
            "e816ebfc09af4c45b53f025b06387412"
          ]
        },
        "id": "PZylgQxH6785",
        "outputId": "ab291e3b-bca8-46cc-f867-d05a8ac5c8b9"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)neration_config.json:   0%|          | 0.00/137 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "e77097f6b4ac434cb6f9a9dfedb77307"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "LlamaForCausalLM(\n",
            "  (model): LlamaModel(\n",
            "    (embed_tokens): Embedding(32000, 3200, padding_idx=0)\n",
            "    (layers): ModuleList(\n",
            "      (0-25): 26 x LlamaDecoderLayer(\n",
            "        (self_attn): LlamaAttention(\n",
            "          (q_proj): Linear8bitLt(in_features=3200, out_features=3200, bias=False)\n",
            "          (k_proj): Linear8bitLt(in_features=3200, out_features=3200, bias=False)\n",
            "          (v_proj): Linear8bitLt(in_features=3200, out_features=3200, bias=False)\n",
            "          (o_proj): Linear8bitLt(in_features=3200, out_features=3200, bias=False)\n",
            "          (rotary_emb): LlamaRotaryEmbedding()\n",
            "        )\n",
            "        (mlp): LlamaMLP(\n",
            "          (gate_proj): Linear8bitLt(in_features=3200, out_features=8640, bias=False)\n",
            "          (up_proj): Linear8bitLt(in_features=3200, out_features=8640, bias=False)\n",
            "          (down_proj): Linear8bitLt(in_features=8640, out_features=3200, bias=False)\n",
            "          (act_fn): SiLUActivation()\n",
            "        )\n",
            "        (input_layernorm): LlamaRMSNorm()\n",
            "        (post_attention_layernorm): LlamaRMSNorm()\n",
            "      )\n",
            "    )\n",
            "    (norm): LlamaRMSNorm()\n",
            "  )\n",
            "  (lm_head): Linear(in_features=3200, out_features=32000, bias=False)\n",
            ")\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "for param in model.parameters():\n",
        "  param.requires_grad = False  # freeze the model - train adapters later\n",
        "  if param.ndim == 1:\n",
        "    # cast the small parameters (e.g. layernorm) to fp32 for stability\n",
        "    param.data = param.data.to(torch.float32)\n",
        "\n",
        "model.gradient_checkpointing_enable()  # reduce number of stored activations\n",
        "model.enable_input_require_grads()\n",
        "\n",
        "class CastOutputToFloat(nn.Sequential):\n",
        "  def forward(self, x): return super().forward(x).to(torch.float32)\n",
        "model.lm_head = CastOutputToFloat(model.lm_head)"
      ],
      "metadata": {
        "id": "-7vBbzzbgFz5"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "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",
        "    )"
      ],
      "metadata": {
        "id": "8VCTyDsPgIp8"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from peft import LoraConfig, get_peft_model\n",
        "\n",
        "config = LoraConfig(\n",
        "    r=8,\n",
        "    lora_alpha=16,\n",
        "    target_modules=[\n",
        "    \"q_proj\",\n",
        "    \"v_proj\",\n",
        "],\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)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "m-iTEFnagLt3",
        "outputId": "1cac7e1e-ea64-4574-d687-824d9744f66f"
      },
      "execution_count": 98,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "trainable params: 2662400 || all params: 3429136000 || trainable%: 0.07764054852300988\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# My dataset"
      ],
      "metadata": {
        "id": "6yQdorf7Fa6S"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from datasets import load_dataset\n",
        "\n",
        "train_dataset = load_dataset(\"csv\", data_files=\"concatenated_trans.csv\", split = \"train\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 135,
          "referenced_widgets": [
            "9575af350f4949dba4c1b229719298d6",
            "a379c823c4a443748192ff350c8189ca",
            "cf7f1514d6f845e58d9228ed10c7967d",
            "4ae1d38d9c314aa3a6760101f1084b33",
            "cac8585cc5c24b7eaf3b92778daa6f20",
            "7973c682318b4297aedf0e9e8a38b909",
            "38f96819d1954b979204683282918a68",
            "15bb57583a504a74a3b9bf20a3e42358",
            "b78304842072468886541d95ea3190f2",
            "a7c680340a02487895be3a96e67c40a9",
            "69847b101a844bd9a3142f001c728284",
            "56b61d7eddee424598a5efab2c44f1bd",
            "7730e1acede84e82a0ca2bf2baaf667c",
            "e5fc0031ab5a450393b9c7910dead5aa",
            "6511931bb53a43ba9350d00c392b1fd8",
            "6c2f976e9494466aa24039a0cd761358",
            "2298da4771b24565b135f78d34181ff5",
            "6e74f78d226d4138a5473a80b8bda585",
            "87652a609ca449b094545965b25f7439",
            "5cfee844ea6a453d81a84b7b73691616",
            "86499d305f854702a55519087b5ac68b",
            "30eb6f8aa7e34562902dcc6f2f63ed90",
            "db99e95b8c75429fa43deabc1a107162",
            "0946df4cc0484e339fede91e4c0a7f86",
            "d6e0fd15810b4c9689ab65dff54669f7",
            "f6c05d36fc634edd97365b4dd4c69227",
            "ab4a76db5b944b7f8a624fbfbe6be337",
            "9404459ca9694a7a857d1af36b4005d8",
            "4611cb5f972840f98248195c2bb48243",
            "32365da7166d434a91961fb6c88b724e",
            "2ec4c000b500434eaae4aea5b9103fbd",
            "b553cc6f8cbc42edb319b2268c8e7b9d",
            "0fa2c1b4f8d14b20a4a75f8870a71248"
          ]
        },
        "id": "iQoYMK9ngN8w",
        "outputId": "0206d463-d6e2-4a1a-87ca-5860709b87ed"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading and preparing dataset csv/default to /root/.cache/huggingface/datasets/csv/default-0944c3b6d35127ec/0.0.0/eea64c71ca8b46dd3f537ed218fc9bf495d5707789152eb2764f5c78fa66d59d...\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading data files:   0%|          | 0/1 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "9575af350f4949dba4c1b229719298d6"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Extracting data files:   0%|          | 0/1 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "56b61d7eddee424598a5efab2c44f1bd"
            }
          },
          "metadata": {}
        },
        {
          "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": "db99e95b8c75429fa43deabc1a107162"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Dataset csv downloaded and prepared to /root/.cache/huggingface/datasets/csv/default-0944c3b6d35127ec/0.0.0/eea64c71ca8b46dd3f537ed218fc9bf495d5707789152eb2764f5c78fa66d59d. Subsequent calls will reuse this data.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_dataset"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eVINqTdHgXMs",
        "outputId": "6e77096d-bf26-468f-fa3e-21a08e09f6c2"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Dataset({\n",
              "    features: ['Unnamed: 0', 'system_prompt', 'question', 'response', 'tr question', 'tr response'],\n",
              "    num_rows: 16891\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_dataset = train_dataset.rename_column(\"tr question\", \"tr_question\")"
      ],
      "metadata": {
        "id": "XvCQQQZ1r1as"
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train_dataset = train_dataset.rename_column(\"tr response\", \"tr_response\")"
      ],
      "metadata": {
        "id": "QkAAMZFEr9sI"
      },
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train_dataset = train_dataset.remove_columns(\"Unnamed: 0\")"
      ],
      "metadata": {
        "id": "_0g-J-UTsTRY"
      },
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train_dataset"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Cz1Vf5yDsD99",
        "outputId": "b580b7fe-26b0-4254-b0b2-87d2d4e06c60"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Dataset({\n",
              "    features: ['system_prompt', 'question', 'response', 'tr_question', 'tr_response'],\n",
              "    num_rows: 16891\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "prompt"
      ],
      "metadata": {
        "id": "TaJYKz0wguqR"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def create_prompt(system_prompt: str, question: str, response: str, tr_question: str, tr_response: str):\n",
        "  if len(response) < 1:\n",
        "    response = \"Cannot Find Answer\"\n",
        "  else:\n",
        "    response = response\n",
        "  prompt_template = f\"### CONTEXT\\n{system_prompt}\\n\\n### QUESTION\\n{question}\\n\\n### RESPONSE\\n{response}\\n\\n### TRANSLATED QUESTION\\n{tr_question}\\n\\n### TRANSLATED RESPONSE\\n{tr_response}</s>\"\n",
        "  return prompt_template"
      ],
      "metadata": {
        "id": "km6W7MbugvkC"
      },
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def tokenize_function(samples):\n",
        "  return tokenizer(create_prompt(samples['system_prompt'],\n",
        "                                 samples['question'],\n",
        "                                 samples['response'],\n",
        "                                 samples['tr_question'],\n",
        "                                 samples['tr_response']),\n",
        "                                 padding='max_length',\n",
        "                                 truncation=True\n",
        "                   )\n",
        "\n",
        "\n",
        "\n",
        "mapped_train_dataset = train_dataset.map(tokenize_function)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17,
          "referenced_widgets": [
            "a083c5128a3c46249a6bb190d97af46d",
            "6a445adc441949598390d2d79a753f1a",
            "a5ff3a5a734a4392b45aba1ff05de2c4",
            "8a66cbacbc0a49d19367c1ed18c23233",
            "a2e6780116634c618db99e23e3ecfa0c",
            "00087d7ed62c4230a1e04be1371cca1c",
            "9d2b9778cb18495ea882f1015c0ed57b",
            "2462ea322e724e8aae1a7a0119b73e44",
            "47ebe850c59e4ed29a908cee9792dc4e",
            "705f0b64f46843698cc684b355a89049",
            "834f3c49ba1545eabe816071c2599e68"
          ]
        },
        "id": "_1GkqXHbgodX",
        "outputId": "3876afa5-0f63-48a0-93ad-ec6c0ccd3bff"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/16891 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "a083c5128a3c46249a6bb190d97af46d"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "mapped_train_dataset"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PySxYDAdgwnw",
        "outputId": "6534464f-2999-403e-adb4-a4cd2d7990b2"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Dataset({\n",
              "    features: ['system_prompt', 'question', 'response', 'tr_question', 'tr_response', 'input_ids', 'attention_mask'],\n",
              "    num_rows: 16891\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Dataset for translation"
      ],
      "metadata": {
        "id": "PsjEMTQxFe95"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from datasets import inspect_dataset, load_dataset_builder, load_dataset\n",
        "\n",
        "dataset = load_dataset(\"wmt19\", 'ru-en')\n",
        "\n",
        "'''\n",
        "inspect_dataset(\"wmt19\")\n",
        "builder = load_dataset_builder(\n",
        "    local_path = \"/content\",\n",
        "    language_pair=(\"en\", \"ru\"),\n",
        "    split = \"train\"\n",
        "    #subsets={\n",
        "    #    datasets.Split.TRAIN: [\"commoncrawl_frde\"],\n",
        "    #    datasets.Split.VALIDATION: [\"euelections_dev2019\"],\n",
        "    #},\n",
        "'''"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000,
          "referenced_widgets": [
            "08d1a005eb0143b5b02a4f1b98c766a4",
            "92ba31e4d3584c239bdb95f12dbdd249",
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            "036a1e521b224ffda148a37e337c1c83",
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            "0d02914d84ab465c8b6dfd84114ecb68",
            "1d3b986992df44fdab8c006960d32955",
            "16729beffea249528eec49c0f091ba80",
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            "b0a3743b9a4e479dad1b59d857581186",
            "b6253ef67cbe450ebea4b2c7f64f4b2e",
            "aa7f33560d1742758964ec3bdcaf3813",
            "d87517b772794c6f888dbaaa337f7433",
            "38b7e11b35084af38613ad29af486c60",
            "bf15bd2f38f14267832c3430920f37c7",
            "a0377db405fe43a28ad69cac9ae36279",
            "5a91337addff426cb4e1d07b8e9107c6",
            "27592ccc040543549c16d34d336c7623",
            "d9d90ca41aba49e0a3ec92ee2ad22431",
            "c10f1065a12545599cade0d6bdcaa512",
            "2b2652b5ea224c10a2dab5fb5362a35a",
            "f5e29c48f6c74a5882030340ab34485c",
            "c8329c9084c14424b1e35680e1d60781",
            "de68d4896c0b4e8788f7b0d47674cfba",
            "2c893ab5643946dc9adab1d59347602b",
            "2296acb3b6ab44408f35af8d3c5d95ec",
            "e5efde565da24add8c06047432833d91",
            "15c7733d8e274d239c6c9808b8909e68",
            "cbdfabe2470249aab933781eda9dfc8e",
            "9cdcda50c5e24d8fba01e1e81c4fec8d",
            "da9f24c8ef21404ca95e18e2970d96b8",
            "8fae44bc32464887b7f587162e60433d"
          ]
        },
        "id": "4Qr4vX3wFk-R",
        "outputId": "54c7638b-2f6e-4fb0-ccc2-4c0256712d30"
      },
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading and preparing dataset wmt19/ru-en to /root/.cache/huggingface/datasets/wmt19/ru-en/1.0.0/29e210fae5690e843cae5dc43b53db36c4e02f927db50cd5235a22ab42dde90a...\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading data files:   0%|          | 0/6 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "08d1a005eb0143b5b02a4f1b98c766a4"
            }
          },
          "metadata": {}
        },
        {
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          "data": {
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            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "1d3b986992df44fdab8c006960d32955"
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          },
          "metadata": {}
        },
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            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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              "model_id": "335f4d968ef24b72bfca7b3d4888cb94"
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          },
          "metadata": {}
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            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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              "model_id": "89bba4a761704ba7b3af70e46929d968"
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          },
          "metadata": {}
        },
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            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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              "model_id": "ad591f4a736c4933803419a48542226e"
            }
          },
          "metadata": {}
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            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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              "model_id": "40689524e576413ab289caa46ede4ff0"
            }
          },
          "metadata": {}
        },
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            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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              "model_id": "da98a927121d4360937bc584043ebef9"
            }
          },
          "metadata": {}
        },
        {
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          "data": {
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
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            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Extracting data files: 0it [00:00, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "86d2b603d2064235ae062ee4d900b5e3"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating train split:   0%|          | 0/37492126 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "340214edb2a04d82b5a760d467e4d53d"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:datasets_modules.datasets.wmt19.29e210fae5690e843cae5dc43b53db36c4e02f927db50cd5235a22ab42dde90a.wmt_utils:Skipping line 19415 in TSV (/root/.cache/huggingface/datasets/downloads/extracted/7f5739f7e234cd46b873c999a898c40b3f56b4f6fee0ac46401810a706da2720) with 3 != 2 columns.\n",
            "WARNING:datasets_modules.datasets.wmt19.29e210fae5690e843cae5dc43b53db36c4e02f927db50cd5235a22ab42dde90a.wmt_utils:Skipping line 19419 in TSV (/root/.cache/huggingface/datasets/downloads/extracted/7f5739f7e234cd46b873c999a898c40b3f56b4f6fee0ac46401810a706da2720) with 3 != 2 columns.\n",
            "WARNING:datasets_modules.datasets.wmt19.29e210fae5690e843cae5dc43b53db36c4e02f927db50cd5235a22ab42dde90a.wmt_utils:Skipping line 19434 in TSV (/root/.cache/huggingface/datasets/downloads/extracted/7f5739f7e234cd46b873c999a898c40b3f56b4f6fee0ac46401810a706da2720) with 3 != 2 columns.\n",
            "WARNING:datasets_modules.datasets.wmt19.29e210fae5690e843cae5dc43b53db36c4e02f927db50cd5235a22ab42dde90a.wmt_utils:Skipping line 39894 in TSV (/root/.cache/huggingface/datasets/downloads/extracted/7f5739f7e234cd46b873c999a898c40b3f56b4f6fee0ac46401810a706da2720) with 3 != 2 columns.\n",
            "WARNING:datasets_modules.datasets.wmt19.29e210fae5690e843cae5dc43b53db36c4e02f927db50cd5235a22ab42dde90a.wmt_utils:Skipping line 39899 in TSV (/root/.cache/huggingface/datasets/downloads/extracted/7f5739f7e234cd46b873c999a898c40b3f56b4f6fee0ac46401810a706da2720) with 3 != 2 columns.\n",
            "WARNING:datasets_modules.datasets.wmt19.29e210fae5690e843cae5dc43b53db36c4e02f927db50cd5235a22ab42dde90a.wmt_utils:Skipping line 39900 in TSV (/root/.cache/huggingface/datasets/downloads/extracted/7f5739f7e234cd46b873c999a898c40b3f56b4f6fee0ac46401810a706da2720) with 3 != 2 columns.\n",
            "WARNING:datasets_modules.datasets.wmt19.29e210fae5690e843cae5dc43b53db36c4e02f927db50cd5235a22ab42dde90a.wmt_utils:Skipping line 39915 in TSV (/root/.cache/huggingface/datasets/downloads/extracted/7f5739f7e234cd46b873c999a898c40b3f56b4f6fee0ac46401810a706da2720) with 3 != 2 columns.\n",
            "WARNING:datasets_modules.datasets.wmt19.29e210fae5690e843cae5dc43b53db36c4e02f927db50cd5235a22ab42dde90a.wmt_utils:Skipping line 39920 in TSV (/root/.cache/huggingface/datasets/downloads/extracted/7f5739f7e234cd46b873c999a898c40b3f56b4f6fee0ac46401810a706da2720) with 3 != 2 columns.\n",
            "WARNING:datasets_modules.datasets.wmt19.29e210fae5690e843cae5dc43b53db36c4e02f927db50cd5235a22ab42dde90a.wmt_utils:Skipping line 39923 in TSV (/root/.cache/huggingface/datasets/downloads/extracted/7f5739f7e234cd46b873c999a898c40b3f56b4f6fee0ac46401810a706da2720) with 3 != 2 columns.\n",
            "WARNING:datasets_modules.datasets.wmt19.29e210fae5690e843cae5dc43b53db36c4e02f927db50cd5235a22ab42dde90a.wmt_utils:Skipping line 50815 in TSV (/root/.cache/huggingface/datasets/downloads/extracted/7f5739f7e234cd46b873c999a898c40b3f56b4f6fee0ac46401810a706da2720) with 3 != 2 columns.\n",
            "WARNING:datasets_modules.datasets.wmt19.29e210fae5690e843cae5dc43b53db36c4e02f927db50cd5235a22ab42dde90a.wmt_utils:Skipping line 50818 in TSV (/root/.cache/huggingface/datasets/downloads/extracted/7f5739f7e234cd46b873c999a898c40b3f56b4f6fee0ac46401810a706da2720) with 3 != 2 columns.\n",
            "WARNING:datasets_modules.datasets.wmt19.29e210fae5690e843cae5dc43b53db36c4e02f927db50cd5235a22ab42dde90a.wmt_utils:Skipping line 50820 in TSV (/root/.cache/huggingface/datasets/downloads/extracted/7f5739f7e234cd46b873c999a898c40b3f56b4f6fee0ac46401810a706da2720) with 3 != 2 columns.\n",
            "WARNING:datasets_modules.datasets.wmt19.29e210fae5690e843cae5dc43b53db36c4e02f927db50cd5235a22ab42dde90a.wmt_utils:Skipping line 50829 in TSV (/root/.cache/huggingface/datasets/downloads/extracted/7f5739f7e234cd46b873c999a898c40b3f56b4f6fee0ac46401810a706da2720) with 3 != 2 columns.\n",
            "WARNING:datasets_modules.datasets.wmt19.29e210fae5690e843cae5dc43b53db36c4e02f927db50cd5235a22ab42dde90a.wmt_utils:Skipping line 50831 in TSV (/root/.cache/huggingface/datasets/downloads/extracted/7f5739f7e234cd46b873c999a898c40b3f56b4f6fee0ac46401810a706da2720) with 3 != 2 columns.\n",
            "WARNING:datasets_modules.datasets.wmt19.29e210fae5690e843cae5dc43b53db36c4e02f927db50cd5235a22ab42dde90a.wmt_utils:Skipping line 52209 in TSV (/root/.cache/huggingface/datasets/downloads/extracted/7f5739f7e234cd46b873c999a898c40b3f56b4f6fee0ac46401810a706da2720) with 3 != 2 columns.\n",
            "WARNING:datasets_modules.datasets.wmt19.29e210fae5690e843cae5dc43b53db36c4e02f927db50cd5235a22ab42dde90a.wmt_utils:Skipping line 52210 in TSV (/root/.cache/huggingface/datasets/downloads/extracted/7f5739f7e234cd46b873c999a898c40b3f56b4f6fee0ac46401810a706da2720) with 3 != 2 columns.\n",
            "WARNING:datasets_modules.datasets.wmt19.29e210fae5690e843cae5dc43b53db36c4e02f927db50cd5235a22ab42dde90a.wmt_utils:Skipping line 52214 in TSV (/root/.cache/huggingface/datasets/downloads/extracted/7f5739f7e234cd46b873c999a898c40b3f56b4f6fee0ac46401810a706da2720) with 3 != 2 columns.\n",
            "WARNING:datasets_modules.datasets.wmt19.29e210fae5690e843cae5dc43b53db36c4e02f927db50cd5235a22ab42dde90a.wmt_utils:Skipping line 52220 in TSV (/root/.cache/huggingface/datasets/downloads/extracted/7f5739f7e234cd46b873c999a898c40b3f56b4f6fee0ac46401810a706da2720) with 3 != 2 columns.\n",
            "WARNING:datasets_modules.datasets.wmt19.29e210fae5690e843cae5dc43b53db36c4e02f927db50cd5235a22ab42dde90a.wmt_utils:Skipping line 52224 in TSV (/root/.cache/huggingface/datasets/downloads/extracted/7f5739f7e234cd46b873c999a898c40b3f56b4f6fee0ac46401810a706da2720) with 3 != 2 columns.\n",
            "WARNING:datasets_modules.datasets.wmt19.29e210fae5690e843cae5dc43b53db36c4e02f927db50cd5235a22ab42dde90a.wmt_utils:Skipping line 52226 in TSV (/root/.cache/huggingface/datasets/downloads/extracted/7f5739f7e234cd46b873c999a898c40b3f56b4f6fee0ac46401810a706da2720) with 3 != 2 columns.\n",
            "WARNING:datasets_modules.datasets.wmt19.29e210fae5690e843cae5dc43b53db36c4e02f927db50cd5235a22ab42dde90a.wmt_utils:Skipping line 73469 in TSV (/root/.cache/huggingface/datasets/downloads/extracted/7f5739f7e234cd46b873c999a898c40b3f56b4f6fee0ac46401810a706da2720) with 3 != 2 columns.\n",
            "WARNING:datasets_modules.datasets.wmt19.29e210fae5690e843cae5dc43b53db36c4e02f927db50cd5235a22ab42dde90a.wmt_utils:Skipping line 73494 in TSV (/root/.cache/huggingface/datasets/downloads/extracted/7f5739f7e234cd46b873c999a898c40b3f56b4f6fee0ac46401810a706da2720) with 3 != 2 columns.\n",
            "WARNING:datasets_modules.datasets.wmt19.29e210fae5690e843cae5dc43b53db36c4e02f927db50cd5235a22ab42dde90a.wmt_utils:Skipping line 75844 in TSV (/root/.cache/huggingface/datasets/downloads/extracted/7f5739f7e234cd46b873c999a898c40b3f56b4f6fee0ac46401810a706da2720) with 3 != 2 columns.\n",
            "WARNING:datasets_modules.datasets.wmt19.29e210fae5690e843cae5dc43b53db36c4e02f927db50cd5235a22ab42dde90a.wmt_utils:Skipping line 76410 in TSV (/root/.cache/huggingface/datasets/downloads/extracted/7f5739f7e234cd46b873c999a898c40b3f56b4f6fee0ac46401810a706da2720) with 3 != 2 columns.\n",
            "WARNING:datasets_modules.datasets.wmt19.29e210fae5690e843cae5dc43b53db36c4e02f927db50cd5235a22ab42dde90a.wmt_utils:Skipping line 76412 in TSV (/root/.cache/huggingface/datasets/downloads/extracted/7f5739f7e234cd46b873c999a898c40b3f56b4f6fee0ac46401810a706da2720) with 3 != 2 columns.\n",
            "WARNING:datasets_modules.datasets.wmt19.29e210fae5690e843cae5dc43b53db36c4e02f927db50cd5235a22ab42dde90a.wmt_utils:Skipping line 76416 in TSV (/root/.cache/huggingface/datasets/downloads/extracted/7f5739f7e234cd46b873c999a898c40b3f56b4f6fee0ac46401810a706da2720) with 3 != 2 columns.\n",
            "WARNING:datasets_modules.datasets.wmt19.29e210fae5690e843cae5dc43b53db36c4e02f927db50cd5235a22ab42dde90a.wmt_utils:Skipping line 76417 in TSV (/root/.cache/huggingface/datasets/downloads/extracted/7f5739f7e234cd46b873c999a898c40b3f56b4f6fee0ac46401810a706da2720) with 3 != 2 columns.\n",
            "WARNING:datasets_modules.datasets.wmt19.29e210fae5690e843cae5dc43b53db36c4e02f927db50cd5235a22ab42dde90a.wmt_utils:Skipping line 79373 in TSV (/root/.cache/huggingface/datasets/downloads/extracted/7f5739f7e234cd46b873c999a898c40b3f56b4f6fee0ac46401810a706da2720) with 3 != 2 columns.\n",
            "WARNING:datasets_modules.datasets.wmt19.29e210fae5690e843cae5dc43b53db36c4e02f927db50cd5235a22ab42dde90a.wmt_utils:Skipping line 79377 in TSV (/root/.cache/huggingface/datasets/downloads/extracted/7f5739f7e234cd46b873c999a898c40b3f56b4f6fee0ac46401810a706da2720) with 3 != 2 columns.\n",
            "WARNING:datasets_modules.datasets.wmt19.29e210fae5690e843cae5dc43b53db36c4e02f927db50cd5235a22ab42dde90a.wmt_utils:Skipping line 79390 in TSV (/root/.cache/huggingface/datasets/downloads/extracted/7f5739f7e234cd46b873c999a898c40b3f56b4f6fee0ac46401810a706da2720) with 3 != 2 columns.\n",
            "WARNING:datasets_modules.datasets.wmt19.29e210fae5690e843cae5dc43b53db36c4e02f927db50cd5235a22ab42dde90a.wmt_utils:Skipping line 81046 in TSV (/root/.cache/huggingface/datasets/downloads/extracted/7f5739f7e234cd46b873c999a898c40b3f56b4f6fee0ac46401810a706da2720) with 4 != 2 columns.\n",
            "WARNING:datasets_modules.datasets.wmt19.29e210fae5690e843cae5dc43b53db36c4e02f927db50cd5235a22ab42dde90a.wmt_utils:Skipping line 81051 in TSV (/root/.cache/huggingface/datasets/downloads/extracted/7f5739f7e234cd46b873c999a898c40b3f56b4f6fee0ac46401810a706da2720) with 3 != 2 columns.\n",
            "WARNING:datasets_modules.datasets.wmt19.29e210fae5690e843cae5dc43b53db36c4e02f927db50cd5235a22ab42dde90a.wmt_utils:Skipping line 81052 in TSV (/root/.cache/huggingface/datasets/downloads/extracted/7f5739f7e234cd46b873c999a898c40b3f56b4f6fee0ac46401810a706da2720) with 3 != 2 columns.\n",
            "WARNING:datasets_modules.datasets.wmt19.29e210fae5690e843cae5dc43b53db36c4e02f927db50cd5235a22ab42dde90a.wmt_utils:Skipping line 81054 in TSV (/root/.cache/huggingface/datasets/downloads/extracted/7f5739f7e234cd46b873c999a898c40b3f56b4f6fee0ac46401810a706da2720) with 3 != 2 columns.\n",
            "WARNING:datasets_modules.datasets.wmt19.29e210fae5690e843cae5dc43b53db36c4e02f927db50cd5235a22ab42dde90a.wmt_utils:Skipping line 81058 in TSV (/root/.cache/huggingface/datasets/downloads/extracted/7f5739f7e234cd46b873c999a898c40b3f56b4f6fee0ac46401810a706da2720) with 4 != 2 columns.\n",
            "WARNING:datasets_modules.datasets.wmt19.29e210fae5690e843cae5dc43b53db36c4e02f927db50cd5235a22ab42dde90a.wmt_utils:Skipping line 81063 in TSV (/root/.cache/huggingface/datasets/downloads/extracted/7f5739f7e234cd46b873c999a898c40b3f56b4f6fee0ac46401810a706da2720) with 3 != 2 columns.\n",
            "WARNING:datasets_modules.datasets.wmt19.29e210fae5690e843cae5dc43b53db36c4e02f927db50cd5235a22ab42dde90a.wmt_utils:Skipping line 81754 in TSV (/root/.cache/huggingface/datasets/downloads/extracted/7f5739f7e234cd46b873c999a898c40b3f56b4f6fee0ac46401810a706da2720) with 4 != 2 columns.\n",
            "WARNING:datasets_modules.datasets.wmt19.29e210fae5690e843cae5dc43b53db36c4e02f927db50cd5235a22ab42dde90a.wmt_utils:Skipping line 81758 in TSV (/root/.cache/huggingface/datasets/downloads/extracted/7f5739f7e234cd46b873c999a898c40b3f56b4f6fee0ac46401810a706da2720) with 3 != 2 columns.\n",
            "WARNING:datasets_modules.datasets.wmt19.29e210fae5690e843cae5dc43b53db36c4e02f927db50cd5235a22ab42dde90a.wmt_utils:Skipping line 81759 in TSV (/root/.cache/huggingface/datasets/downloads/extracted/7f5739f7e234cd46b873c999a898c40b3f56b4f6fee0ac46401810a706da2720) with 3 != 2 columns.\n",
            "WARNING:datasets_modules.datasets.wmt19.29e210fae5690e843cae5dc43b53db36c4e02f927db50cd5235a22ab42dde90a.wmt_utils:Skipping line 81761 in TSV (/root/.cache/huggingface/datasets/downloads/extracted/7f5739f7e234cd46b873c999a898c40b3f56b4f6fee0ac46401810a706da2720) with 4 != 2 columns.\n",
            "WARNING:datasets_modules.datasets.wmt19.29e210fae5690e843cae5dc43b53db36c4e02f927db50cd5235a22ab42dde90a.wmt_utils:Skipping line 81763 in TSV (/root/.cache/huggingface/datasets/downloads/extracted/7f5739f7e234cd46b873c999a898c40b3f56b4f6fee0ac46401810a706da2720) with 4 != 2 columns.\n",
            "WARNING:datasets_modules.datasets.wmt19.29e210fae5690e843cae5dc43b53db36c4e02f927db50cd5235a22ab42dde90a.wmt_utils:Skipping line 81774 in TSV (/root/.cache/huggingface/datasets/downloads/extracted/7f5739f7e234cd46b873c999a898c40b3f56b4f6fee0ac46401810a706da2720) with 3 != 2 columns.\n",
            "WARNING:datasets_modules.datasets.wmt19.29e210fae5690e843cae5dc43b53db36c4e02f927db50cd5235a22ab42dde90a.wmt_utils:Skipping line 81781 in TSV (/root/.cache/huggingface/datasets/downloads/extracted/7f5739f7e234cd46b873c999a898c40b3f56b4f6fee0ac46401810a706da2720) with 4 != 2 columns.\n",
            "WARNING:datasets_modules.datasets.wmt19.29e210fae5690e843cae5dc43b53db36c4e02f927db50cd5235a22ab42dde90a.wmt_utils:Skipping line 81786 in TSV (/root/.cache/huggingface/datasets/downloads/extracted/7f5739f7e234cd46b873c999a898c40b3f56b4f6fee0ac46401810a706da2720) with 4 != 2 columns.\n",
            "WARNING:datasets_modules.datasets.wmt19.29e210fae5690e843cae5dc43b53db36c4e02f927db50cd5235a22ab42dde90a.wmt_utils:Skipping line 93910 in TSV (/root/.cache/huggingface/datasets/downloads/extracted/7f5739f7e234cd46b873c999a898c40b3f56b4f6fee0ac46401810a706da2720) with 3 != 2 columns.\n",
            "WARNING:datasets_modules.datasets.wmt19.29e210fae5690e843cae5dc43b53db36c4e02f927db50cd5235a22ab42dde90a.wmt_utils:Skipping line 93912 in TSV (/root/.cache/huggingface/datasets/downloads/extracted/7f5739f7e234cd46b873c999a898c40b3f56b4f6fee0ac46401810a706da2720) with 3 != 2 columns.\n",
            "WARNING:datasets_modules.datasets.wmt19.29e210fae5690e843cae5dc43b53db36c4e02f927db50cd5235a22ab42dde90a.wmt_utils:Skipping line 93930 in TSV (/root/.cache/huggingface/datasets/downloads/extracted/7f5739f7e234cd46b873c999a898c40b3f56b4f6fee0ac46401810a706da2720) with 3 != 2 columns.\n",
            "WARNING:datasets_modules.datasets.wmt19.29e210fae5690e843cae5dc43b53db36c4e02f927db50cd5235a22ab42dde90a.wmt_utils:Skipping line 94400 in TSV (/root/.cache/huggingface/datasets/downloads/extracted/7f5739f7e234cd46b873c999a898c40b3f56b4f6fee0ac46401810a706da2720) with 3 != 2 columns.\n",
            "WARNING:datasets_modules.datasets.wmt19.29e210fae5690e843cae5dc43b53db36c4e02f927db50cd5235a22ab42dde90a.wmt_utils:Skipping line 94403 in TSV (/root/.cache/huggingface/datasets/downloads/extracted/7f5739f7e234cd46b873c999a898c40b3f56b4f6fee0ac46401810a706da2720) with 3 != 2 columns.\n",
            "WARNING:datasets_modules.datasets.wmt19.29e210fae5690e843cae5dc43b53db36c4e02f927db50cd5235a22ab42dde90a.wmt_utils:Skipping line 94427 in TSV (/root/.cache/huggingface/datasets/downloads/extracted/7f5739f7e234cd46b873c999a898c40b3f56b4f6fee0ac46401810a706da2720) with 3 != 2 columns.\n",
            "WARNING:datasets_modules.datasets.wmt19.29e210fae5690e843cae5dc43b53db36c4e02f927db50cd5235a22ab42dde90a.wmt_utils:Skipping line 94429 in TSV (/root/.cache/huggingface/datasets/downloads/extracted/7f5739f7e234cd46b873c999a898c40b3f56b4f6fee0ac46401810a706da2720) with 3 != 2 columns.\n",
            "WARNING:datasets_modules.datasets.wmt19.29e210fae5690e843cae5dc43b53db36c4e02f927db50cd5235a22ab42dde90a.wmt_utils:Skipping line 102741 in TSV (/root/.cache/huggingface/datasets/downloads/extracted/7f5739f7e234cd46b873c999a898c40b3f56b4f6fee0ac46401810a706da2720) with 3 != 2 columns.\n",
            "WARNING:datasets_modules.datasets.wmt19.29e210fae5690e843cae5dc43b53db36c4e02f927db50cd5235a22ab42dde90a.wmt_utils:Skipping line 102748 in TSV (/root/.cache/huggingface/datasets/downloads/extracted/7f5739f7e234cd46b873c999a898c40b3f56b4f6fee0ac46401810a706da2720) with 3 != 2 columns.\n",
            "WARNING:datasets_modules.datasets.wmt19.29e210fae5690e843cae5dc43b53db36c4e02f927db50cd5235a22ab42dde90a.wmt_utils:Skipping line 102755 in TSV (/root/.cache/huggingface/datasets/downloads/extracted/7f5739f7e234cd46b873c999a898c40b3f56b4f6fee0ac46401810a706da2720) with 3 != 2 columns.\n",
            "WARNING:datasets_modules.datasets.wmt19.29e210fae5690e843cae5dc43b53db36c4e02f927db50cd5235a22ab42dde90a.wmt_utils:Skipping line 110708 in TSV (/root/.cache/huggingface/datasets/downloads/extracted/7f5739f7e234cd46b873c999a898c40b3f56b4f6fee0ac46401810a706da2720) with 3 != 2 columns.\n",
            "WARNING:datasets_modules.datasets.wmt19.29e210fae5690e843cae5dc43b53db36c4e02f927db50cd5235a22ab42dde90a.wmt_utils:Skipping line 124040 in TSV (/root/.cache/huggingface/datasets/downloads/extracted/7f5739f7e234cd46b873c999a898c40b3f56b4f6fee0ac46401810a706da2720) with 3 != 2 columns.\n",
            "WARNING:datasets_modules.datasets.wmt19.29e210fae5690e843cae5dc43b53db36c4e02f927db50cd5235a22ab42dde90a.wmt_utils:Skipping line 124057 in TSV (/root/.cache/huggingface/datasets/downloads/extracted/7f5739f7e234cd46b873c999a898c40b3f56b4f6fee0ac46401810a706da2720) with 3 != 2 columns.\n",
            "WARNING:datasets_modules.datasets.wmt19.29e210fae5690e843cae5dc43b53db36c4e02f927db50cd5235a22ab42dde90a.wmt_utils:Skipping line 124059 in TSV (/root/.cache/huggingface/datasets/downloads/extracted/7f5739f7e234cd46b873c999a898c40b3f56b4f6fee0ac46401810a706da2720) with 3 != 2 columns.\n",
            "WARNING:datasets_modules.datasets.wmt19.29e210fae5690e843cae5dc43b53db36c4e02f927db50cd5235a22ab42dde90a.wmt_utils:Skipping line 128721 in TSV (/root/.cache/huggingface/datasets/downloads/extracted/7f5739f7e234cd46b873c999a898c40b3f56b4f6fee0ac46401810a706da2720) with 3 != 2 columns.\n",
            "WARNING:datasets_modules.datasets.wmt19.29e210fae5690e843cae5dc43b53db36c4e02f927db50cd5235a22ab42dde90a.wmt_utils:Skipping line 128726 in TSV (/root/.cache/huggingface/datasets/downloads/extracted/7f5739f7e234cd46b873c999a898c40b3f56b4f6fee0ac46401810a706da2720) with 3 != 2 columns.\n",
            "WARNING:datasets_modules.datasets.wmt19.29e210fae5690e843cae5dc43b53db36c4e02f927db50cd5235a22ab42dde90a.wmt_utils:Skipping line 131020 in TSV (/root/.cache/huggingface/datasets/downloads/extracted/7f5739f7e234cd46b873c999a898c40b3f56b4f6fee0ac46401810a706da2720) with 3 != 2 columns.\n",
            "WARNING:datasets_modules.datasets.wmt19.29e210fae5690e843cae5dc43b53db36c4e02f927db50cd5235a22ab42dde90a.wmt_utils:Skipping line 131025 in TSV (/root/.cache/huggingface/datasets/downloads/extracted/7f5739f7e234cd46b873c999a898c40b3f56b4f6fee0ac46401810a706da2720) with 3 != 2 columns.\n",
            "WARNING:datasets_modules.datasets.wmt19.29e210fae5690e843cae5dc43b53db36c4e02f927db50cd5235a22ab42dde90a.wmt_utils:Skipping line 131030 in TSV (/root/.cache/huggingface/datasets/downloads/extracted/7f5739f7e234cd46b873c999a898c40b3f56b4f6fee0ac46401810a706da2720) with 3 != 2 columns.\n",
            "WARNING:datasets_modules.datasets.wmt19.29e210fae5690e843cae5dc43b53db36c4e02f927db50cd5235a22ab42dde90a.wmt_utils:Skipping line 141106 in TSV (/root/.cache/huggingface/datasets/downloads/extracted/7f5739f7e234cd46b873c999a898c40b3f56b4f6fee0ac46401810a706da2720) with 3 != 2 columns.\n",
            "WARNING:datasets_modules.datasets.wmt19.29e210fae5690e843cae5dc43b53db36c4e02f927db50cd5235a22ab42dde90a.wmt_utils:Skipping line 141108 in TSV (/root/.cache/huggingface/datasets/downloads/extracted/7f5739f7e234cd46b873c999a898c40b3f56b4f6fee0ac46401810a706da2720) with 3 != 2 columns.\n",
            "WARNING:datasets_modules.datasets.wmt19.29e210fae5690e843cae5dc43b53db36c4e02f927db50cd5235a22ab42dde90a.wmt_utils:Skipping line 141111 in TSV (/root/.cache/huggingface/datasets/downloads/extracted/7f5739f7e234cd46b873c999a898c40b3f56b4f6fee0ac46401810a706da2720) with 3 != 2 columns.\n",
            "WARNING:datasets_modules.datasets.wmt19.29e210fae5690e843cae5dc43b53db36c4e02f927db50cd5235a22ab42dde90a.wmt_utils:Skipping line 141113 in TSV (/root/.cache/huggingface/datasets/downloads/extracted/7f5739f7e234cd46b873c999a898c40b3f56b4f6fee0ac46401810a706da2720) with 3 != 2 columns.\n",
            "WARNING:datasets_modules.datasets.wmt19.29e210fae5690e843cae5dc43b53db36c4e02f927db50cd5235a22ab42dde90a.wmt_utils:Skipping line 141116 in TSV (/root/.cache/huggingface/datasets/downloads/extracted/7f5739f7e234cd46b873c999a898c40b3f56b4f6fee0ac46401810a706da2720) with 3 != 2 columns.\n",
            "WARNING:datasets_modules.datasets.wmt19.29e210fae5690e843cae5dc43b53db36c4e02f927db50cd5235a22ab42dde90a.wmt_utils:Skipping line 141118 in TSV (/root/.cache/huggingface/datasets/downloads/extracted/7f5739f7e234cd46b873c999a898c40b3f56b4f6fee0ac46401810a706da2720) with 3 != 2 columns.\n",
            "WARNING:datasets_modules.datasets.wmt19.29e210fae5690e843cae5dc43b53db36c4e02f927db50cd5235a22ab42dde90a.wmt_utils:Skipping line 141120 in TSV (/root/.cache/huggingface/datasets/downloads/extracted/7f5739f7e234cd46b873c999a898c40b3f56b4f6fee0ac46401810a706da2720) with 3 != 2 columns.\n",
            "WARNING:datasets_modules.datasets.wmt19.29e210fae5690e843cae5dc43b53db36c4e02f927db50cd5235a22ab42dde90a.wmt_utils:Skipping line 141128 in TSV (/root/.cache/huggingface/datasets/downloads/extracted/7f5739f7e234cd46b873c999a898c40b3f56b4f6fee0ac46401810a706da2720) with 3 != 2 columns.\n",
            "WARNING:datasets_modules.datasets.wmt19.29e210fae5690e843cae5dc43b53db36c4e02f927db50cd5235a22ab42dde90a.wmt_utils:Skipping line 141132 in TSV (/root/.cache/huggingface/datasets/downloads/extracted/7f5739f7e234cd46b873c999a898c40b3f56b4f6fee0ac46401810a706da2720) with 3 != 2 columns.\n",
            "WARNING:datasets_modules.datasets.wmt19.29e210fae5690e843cae5dc43b53db36c4e02f927db50cd5235a22ab42dde90a.wmt_utils:Skipping line 141136 in TSV (/root/.cache/huggingface/datasets/downloads/extracted/7f5739f7e234cd46b873c999a898c40b3f56b4f6fee0ac46401810a706da2720) with 3 != 2 columns.\n",
            "WARNING:datasets_modules.datasets.wmt19.29e210fae5690e843cae5dc43b53db36c4e02f927db50cd5235a22ab42dde90a.wmt_utils:Skipping line 141137 in TSV (/root/.cache/huggingface/datasets/downloads/extracted/7f5739f7e234cd46b873c999a898c40b3f56b4f6fee0ac46401810a706da2720) with 3 != 2 columns.\n",
            "WARNING:datasets_modules.datasets.wmt19.29e210fae5690e843cae5dc43b53db36c4e02f927db50cd5235a22ab42dde90a.wmt_utils:Skipping line 145070 in TSV (/root/.cache/huggingface/datasets/downloads/extracted/7f5739f7e234cd46b873c999a898c40b3f56b4f6fee0ac46401810a706da2720) with 3 != 2 columns.\n",
            "WARNING:datasets_modules.datasets.wmt19.29e210fae5690e843cae5dc43b53db36c4e02f927db50cd5235a22ab42dde90a.wmt_utils:Skipping line 145071 in TSV (/root/.cache/huggingface/datasets/downloads/extracted/7f5739f7e234cd46b873c999a898c40b3f56b4f6fee0ac46401810a706da2720) with 3 != 2 columns.\n",
            "WARNING:datasets_modules.datasets.wmt19.29e210fae5690e843cae5dc43b53db36c4e02f927db50cd5235a22ab42dde90a.wmt_utils:Skipping line 147604 in TSV (/root/.cache/huggingface/datasets/downloads/extracted/7f5739f7e234cd46b873c999a898c40b3f56b4f6fee0ac46401810a706da2720) with 3 != 2 columns.\n",
            "WARNING:datasets_modules.datasets.wmt19.29e210fae5690e843cae5dc43b53db36c4e02f927db50cd5235a22ab42dde90a.wmt_utils:Skipping line 147616 in TSV (/root/.cache/huggingface/datasets/downloads/extracted/7f5739f7e234cd46b873c999a898c40b3f56b4f6fee0ac46401810a706da2720) with 3 != 2 columns.\n",
            "WARNING:datasets_modules.datasets.wmt19.29e210fae5690e843cae5dc43b53db36c4e02f927db50cd5235a22ab42dde90a.wmt_utils:Skipping line 147618 in TSV (/root/.cache/huggingface/datasets/downloads/extracted/7f5739f7e234cd46b873c999a898c40b3f56b4f6fee0ac46401810a706da2720) with 3 != 2 columns.\n",
            "WARNING:datasets_modules.datasets.wmt19.29e210fae5690e843cae5dc43b53db36c4e02f927db50cd5235a22ab42dde90a.wmt_utils:Skipping line 149764 in TSV (/root/.cache/huggingface/datasets/downloads/extracted/7f5739f7e234cd46b873c999a898c40b3f56b4f6fee0ac46401810a706da2720) with 3 != 2 columns.\n",
            "WARNING:datasets_modules.datasets.wmt19.29e210fae5690e843cae5dc43b53db36c4e02f927db50cd5235a22ab42dde90a.wmt_utils:Skipping line 149774 in TSV (/root/.cache/huggingface/datasets/downloads/extracted/7f5739f7e234cd46b873c999a898c40b3f56b4f6fee0ac46401810a706da2720) with 3 != 2 columns.\n",
            "WARNING:datasets_modules.datasets.wmt19.29e210fae5690e843cae5dc43b53db36c4e02f927db50cd5235a22ab42dde90a.wmt_utils:Skipping line 149787 in TSV (/root/.cache/huggingface/datasets/downloads/extracted/7f5739f7e234cd46b873c999a898c40b3f56b4f6fee0ac46401810a706da2720) with 3 != 2 columns.\n",
            "WARNING:datasets_modules.datasets.wmt19.29e210fae5690e843cae5dc43b53db36c4e02f927db50cd5235a22ab42dde90a.wmt_utils:Skipping line 157494 in TSV (/root/.cache/huggingface/datasets/downloads/extracted/7f5739f7e234cd46b873c999a898c40b3f56b4f6fee0ac46401810a706da2720) with 3 != 2 columns.\n",
            "WARNING:datasets_modules.datasets.wmt19.29e210fae5690e843cae5dc43b53db36c4e02f927db50cd5235a22ab42dde90a.wmt_utils:Skipping line 157888 in TSV (/root/.cache/huggingface/datasets/downloads/extracted/7f5739f7e234cd46b873c999a898c40b3f56b4f6fee0ac46401810a706da2720) with 3 != 2 columns.\n",
            "WARNING:datasets_modules.datasets.wmt19.29e210fae5690e843cae5dc43b53db36c4e02f927db50cd5235a22ab42dde90a.wmt_utils:Skipping line 157907 in TSV (/root/.cache/huggingface/datasets/downloads/extracted/7f5739f7e234cd46b873c999a898c40b3f56b4f6fee0ac46401810a706da2720) with 3 != 2 columns.\n",
            "WARNING:datasets_modules.datasets.wmt19.29e210fae5690e843cae5dc43b53db36c4e02f927db50cd5235a22ab42dde90a.wmt_utils:Skipping line 157913 in TSV (/root/.cache/huggingface/datasets/downloads/extracted/7f5739f7e234cd46b873c999a898c40b3f56b4f6fee0ac46401810a706da2720) with 3 != 2 columns.\n",
            "WARNING:datasets_modules.datasets.wmt19.29e210fae5690e843cae5dc43b53db36c4e02f927db50cd5235a22ab42dde90a.wmt_utils:Skipping line 161073 in TSV (/root/.cache/huggingface/datasets/downloads/extracted/7f5739f7e234cd46b873c999a898c40b3f56b4f6fee0ac46401810a706da2720) with 3 != 2 columns.\n",
            "WARNING:datasets_modules.datasets.wmt19.29e210fae5690e843cae5dc43b53db36c4e02f927db50cd5235a22ab42dde90a.wmt_utils:Skipping line 161075 in TSV (/root/.cache/huggingface/datasets/downloads/extracted/7f5739f7e234cd46b873c999a898c40b3f56b4f6fee0ac46401810a706da2720) with 3 != 2 columns.\n",
            "WARNING:datasets_modules.datasets.wmt19.29e210fae5690e843cae5dc43b53db36c4e02f927db50cd5235a22ab42dde90a.wmt_utils:Skipping line 161082 in TSV (/root/.cache/huggingface/datasets/downloads/extracted/7f5739f7e234cd46b873c999a898c40b3f56b4f6fee0ac46401810a706da2720) with 3 != 2 columns.\n",
            "WARNING:datasets_modules.datasets.wmt19.29e210fae5690e843cae5dc43b53db36c4e02f927db50cd5235a22ab42dde90a.wmt_utils:Skipping line 161087 in TSV (/root/.cache/huggingface/datasets/downloads/extracted/7f5739f7e234cd46b873c999a898c40b3f56b4f6fee0ac46401810a706da2720) with 3 != 2 columns.\n",
            "WARNING:datasets_modules.datasets.wmt19.29e210fae5690e843cae5dc43b53db36c4e02f927db50cd5235a22ab42dde90a.wmt_utils:Skipping line 161089 in TSV (/root/.cache/huggingface/datasets/downloads/extracted/7f5739f7e234cd46b873c999a898c40b3f56b4f6fee0ac46401810a706da2720) with 3 != 2 columns.\n",
            "WARNING:datasets_modules.datasets.wmt19.29e210fae5690e843cae5dc43b53db36c4e02f927db50cd5235a22ab42dde90a.wmt_utils:Skipping line 161092 in TSV (/root/.cache/huggingface/datasets/downloads/extracted/7f5739f7e234cd46b873c999a898c40b3f56b4f6fee0ac46401810a706da2720) with 3 != 2 columns.\n",
            "WARNING:datasets_modules.datasets.wmt19.29e210fae5690e843cae5dc43b53db36c4e02f927db50cd5235a22ab42dde90a.wmt_utils:Skipping line 173387 in TSV (/root/.cache/huggingface/datasets/downloads/extracted/7f5739f7e234cd46b873c999a898c40b3f56b4f6fee0ac46401810a706da2720) with 3 != 2 columns.\n",
            "WARNING:datasets_modules.datasets.wmt19.29e210fae5690e843cae5dc43b53db36c4e02f927db50cd5235a22ab42dde90a.wmt_utils:Skipping line 173393 in TSV (/root/.cache/huggingface/datasets/downloads/extracted/7f5739f7e234cd46b873c999a898c40b3f56b4f6fee0ac46401810a706da2720) with 3 != 2 columns.\n",
            "WARNING:datasets_modules.datasets.wmt19.29e210fae5690e843cae5dc43b53db36c4e02f927db50cd5235a22ab42dde90a.wmt_utils:Skipping line 173399 in TSV (/root/.cache/huggingface/datasets/downloads/extracted/7f5739f7e234cd46b873c999a898c40b3f56b4f6fee0ac46401810a706da2720) with 3 != 2 columns.\n",
            "WARNING:datasets_modules.datasets.wmt19.29e210fae5690e843cae5dc43b53db36c4e02f927db50cd5235a22ab42dde90a.wmt_utils:Skipping line 173401 in TSV (/root/.cache/huggingface/datasets/downloads/extracted/7f5739f7e234cd46b873c999a898c40b3f56b4f6fee0ac46401810a706da2720) with 3 != 2 columns.\n",
            "WARNING:datasets_modules.datasets.wmt19.29e210fae5690e843cae5dc43b53db36c4e02f927db50cd5235a22ab42dde90a.wmt_utils:Skipping line 173408 in TSV (/root/.cache/huggingface/datasets/downloads/extracted/7f5739f7e234cd46b873c999a898c40b3f56b4f6fee0ac46401810a706da2720) with 3 != 2 columns.\n",
            "WARNING:datasets_modules.datasets.wmt19.29e210fae5690e843cae5dc43b53db36c4e02f927db50cd5235a22ab42dde90a.wmt_utils:Skipping line 173410 in TSV (/root/.cache/huggingface/datasets/downloads/extracted/7f5739f7e234cd46b873c999a898c40b3f56b4f6fee0ac46401810a706da2720) with 3 != 2 columns.\n",
            "WARNING:datasets_modules.datasets.wmt19.29e210fae5690e843cae5dc43b53db36c4e02f927db50cd5235a22ab42dde90a.wmt_utils:Skipping line 173421 in TSV (/root/.cache/huggingface/datasets/downloads/extracted/7f5739f7e234cd46b873c999a898c40b3f56b4f6fee0ac46401810a706da2720) with 3 != 2 columns.\n",
            "WARNING:datasets_modules.datasets.wmt19.29e210fae5690e843cae5dc43b53db36c4e02f927db50cd5235a22ab42dde90a.wmt_utils:Skipping line 177141 in TSV (/root/.cache/huggingface/datasets/downloads/extracted/7f5739f7e234cd46b873c999a898c40b3f56b4f6fee0ac46401810a706da2720) with 3 != 2 columns.\n",
            "WARNING:datasets_modules.datasets.wmt19.29e210fae5690e843cae5dc43b53db36c4e02f927db50cd5235a22ab42dde90a.wmt_utils:Skipping line 177146 in TSV (/root/.cache/huggingface/datasets/downloads/extracted/7f5739f7e234cd46b873c999a898c40b3f56b4f6fee0ac46401810a706da2720) with 3 != 2 columns.\n",
            "WARNING:datasets_modules.datasets.wmt19.29e210fae5690e843cae5dc43b53db36c4e02f927db50cd5235a22ab42dde90a.wmt_utils:Skipping line 177153 in TSV (/root/.cache/huggingface/datasets/downloads/extracted/7f5739f7e234cd46b873c999a898c40b3f56b4f6fee0ac46401810a706da2720) with 3 != 2 columns.\n",
            "WARNING:datasets_modules.datasets.wmt19.29e210fae5690e843cae5dc43b53db36c4e02f927db50cd5235a22ab42dde90a.wmt_utils:Skipping line 177159 in TSV (/root/.cache/huggingface/datasets/downloads/extracted/7f5739f7e234cd46b873c999a898c40b3f56b4f6fee0ac46401810a706da2720) with 3 != 2 columns.\n",
            "WARNING:datasets_modules.datasets.wmt19.29e210fae5690e843cae5dc43b53db36c4e02f927db50cd5235a22ab42dde90a.wmt_utils:Skipping line 192344 in TSV (/root/.cache/huggingface/datasets/downloads/extracted/7f5739f7e234cd46b873c999a898c40b3f56b4f6fee0ac46401810a706da2720) with 3 != 2 columns.\n",
            "WARNING:datasets_modules.datasets.wmt19.29e210fae5690e843cae5dc43b53db36c4e02f927db50cd5235a22ab42dde90a.wmt_utils:Skipping line 192352 in TSV (/root/.cache/huggingface/datasets/downloads/extracted/7f5739f7e234cd46b873c999a898c40b3f56b4f6fee0ac46401810a706da2720) with 3 != 2 columns.\n",
            "WARNING:datasets_modules.datasets.wmt19.29e210fae5690e843cae5dc43b53db36c4e02f927db50cd5235a22ab42dde90a.wmt_utils:Skipping line 192354 in TSV (/root/.cache/huggingface/datasets/downloads/extracted/7f5739f7e234cd46b873c999a898c40b3f56b4f6fee0ac46401810a706da2720) with 3 != 2 columns.\n",
            "WARNING:datasets_modules.datasets.wmt19.29e210fae5690e843cae5dc43b53db36c4e02f927db50cd5235a22ab42dde90a.wmt_utils:Skipping line 192356 in TSV (/root/.cache/huggingface/datasets/downloads/extracted/7f5739f7e234cd46b873c999a898c40b3f56b4f6fee0ac46401810a706da2720) with 3 != 2 columns.\n",
            "WARNING:datasets_modules.datasets.wmt19.29e210fae5690e843cae5dc43b53db36c4e02f927db50cd5235a22ab42dde90a.wmt_utils:Skipping line 192360 in TSV (/root/.cache/huggingface/datasets/downloads/extracted/7f5739f7e234cd46b873c999a898c40b3f56b4f6fee0ac46401810a706da2720) with 3 != 2 columns.\n",
            "WARNING:datasets_modules.datasets.wmt19.29e210fae5690e843cae5dc43b53db36c4e02f927db50cd5235a22ab42dde90a.wmt_utils:Skipping line 192361 in TSV (/root/.cache/huggingface/datasets/downloads/extracted/7f5739f7e234cd46b873c999a898c40b3f56b4f6fee0ac46401810a706da2720) with 3 != 2 columns.\n",
            "WARNING:datasets_modules.datasets.wmt19.29e210fae5690e843cae5dc43b53db36c4e02f927db50cd5235a22ab42dde90a.wmt_utils:Skipping line 205869 in TSV (/root/.cache/huggingface/datasets/downloads/extracted/7f5739f7e234cd46b873c999a898c40b3f56b4f6fee0ac46401810a706da2720) with 3 != 2 columns.\n",
            "WARNING:datasets_modules.datasets.wmt19.29e210fae5690e843cae5dc43b53db36c4e02f927db50cd5235a22ab42dde90a.wmt_utils:Skipping line 205870 in TSV (/root/.cache/huggingface/datasets/downloads/extracted/7f5739f7e234cd46b873c999a898c40b3f56b4f6fee0ac46401810a706da2720) with 3 != 2 columns.\n",
            "WARNING:datasets_modules.datasets.wmt19.29e210fae5690e843cae5dc43b53db36c4e02f927db50cd5235a22ab42dde90a.wmt_utils:Skipping line 205874 in TSV (/root/.cache/huggingface/datasets/downloads/extracted/7f5739f7e234cd46b873c999a898c40b3f56b4f6fee0ac46401810a706da2720) with 3 != 2 columns.\n",
            "WARNING:datasets_modules.datasets.wmt19.29e210fae5690e843cae5dc43b53db36c4e02f927db50cd5235a22ab42dde90a.wmt_utils:Skipping line 207474 in TSV (/root/.cache/huggingface/datasets/downloads/extracted/7f5739f7e234cd46b873c999a898c40b3f56b4f6fee0ac46401810a706da2720) with 3 != 2 columns.\n",
            "WARNING:datasets_modules.datasets.wmt19.29e210fae5690e843cae5dc43b53db36c4e02f927db50cd5235a22ab42dde90a.wmt_utils:Skipping line 207490 in TSV (/root/.cache/huggingface/datasets/downloads/extracted/7f5739f7e234cd46b873c999a898c40b3f56b4f6fee0ac46401810a706da2720) with 3 != 2 columns.\n",
            "WARNING:datasets_modules.datasets.wmt19.29e210fae5690e843cae5dc43b53db36c4e02f927db50cd5235a22ab42dde90a.wmt_utils:Skipping line 270477 in TSV (/root/.cache/huggingface/datasets/downloads/extracted/7f5739f7e234cd46b873c999a898c40b3f56b4f6fee0ac46401810a706da2720) with 3 != 2 columns.\n",
            "WARNING:datasets_modules.datasets.wmt19.29e210fae5690e843cae5dc43b53db36c4e02f927db50cd5235a22ab42dde90a.wmt_utils:Skipping line 270478 in TSV (/root/.cache/huggingface/datasets/downloads/extracted/7f5739f7e234cd46b873c999a898c40b3f56b4f6fee0ac46401810a706da2720) with 3 != 2 columns.\n",
            "WARNING:datasets_modules.datasets.wmt19.29e210fae5690e843cae5dc43b53db36c4e02f927db50cd5235a22ab42dde90a.wmt_utils:Skipping line 270496 in TSV (/root/.cache/huggingface/datasets/downloads/extracted/7f5739f7e234cd46b873c999a898c40b3f56b4f6fee0ac46401810a706da2720) with 3 != 2 columns.\n",
            "WARNING:datasets_modules.datasets.wmt19.29e210fae5690e843cae5dc43b53db36c4e02f927db50cd5235a22ab42dde90a.wmt_utils:Skipping line 270504 in TSV (/root/.cache/huggingface/datasets/downloads/extracted/7f5739f7e234cd46b873c999a898c40b3f56b4f6fee0ac46401810a706da2720) with 3 != 2 columns.\n",
            "WARNING:datasets_modules.datasets.wmt19.29e210fae5690e843cae5dc43b53db36c4e02f927db50cd5235a22ab42dde90a.wmt_utils:Skipping line 274370 in TSV (/root/.cache/huggingface/datasets/downloads/extracted/7f5739f7e234cd46b873c999a898c40b3f56b4f6fee0ac46401810a706da2720) with 3 != 2 columns.\n",
            "WARNING:datasets_modules.datasets.wmt19.29e210fae5690e843cae5dc43b53db36c4e02f927db50cd5235a22ab42dde90a.wmt_utils:Skipping line 274371 in TSV (/root/.cache/huggingface/datasets/downloads/extracted/7f5739f7e234cd46b873c999a898c40b3f56b4f6fee0ac46401810a706da2720) with 3 != 2 columns.\n",
            "WARNING:datasets_modules.datasets.wmt19.29e210fae5690e843cae5dc43b53db36c4e02f927db50cd5235a22ab42dde90a.wmt_utils:Skipping line 274373 in TSV (/root/.cache/huggingface/datasets/downloads/extracted/7f5739f7e234cd46b873c999a898c40b3f56b4f6fee0ac46401810a706da2720) with 3 != 2 columns.\n",
            "WARNING:datasets_modules.datasets.wmt19.29e210fae5690e843cae5dc43b53db36c4e02f927db50cd5235a22ab42dde90a.wmt_utils:Skipping line 274388 in TSV (/root/.cache/huggingface/datasets/downloads/extracted/7f5739f7e234cd46b873c999a898c40b3f56b4f6fee0ac46401810a706da2720) with 3 != 2 columns.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating validation split:   0%|          | 0/3000 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "b6253ef67cbe450ebea4b2c7f64f4b2e"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Dataset wmt19 downloaded and prepared to /root/.cache/huggingface/datasets/wmt19/ru-en/1.0.0/29e210fae5690e843cae5dc43b53db36c4e02f927db50cd5235a22ab42dde90a. Subsequent calls will reuse this data.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/2 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "f5e29c48f6c74a5882030340ab34485c"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'\\ninspect_dataset(\"wmt19\")\\nbuilder = load_dataset_builder(\\n    local_path = \"/content\",\\n    language_pair=(\"en\", \"ru\"),\\n    split = \"train\"\\n    #subsets={\\n    #    datasets.Split.TRAIN: [\"commoncrawl_frde\"],\\n    #    datasets.Split.VALIDATION: [\"euelections_dev2019\"],\\n    #},\\n'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dataset"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "X0hIfwJYHPS0",
        "outputId": "7d6d9a4f-0ca8-47f9-a949-6941620101b6"
      },
      "execution_count": 71,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DatasetDict({\n",
              "    train: Dataset({\n",
              "        features: ['translation'],\n",
              "        num_rows: 29993700\n",
              "    })\n",
              "    test: Dataset({\n",
              "        features: ['translation'],\n",
              "        num_rows: 7498426\n",
              "    })\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 71
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dataset = dataset[\"train\"].train_test_split(test_size=0.2)\n",
        "dataset"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WNg8QzxmHWnC",
        "outputId": "362796b7-3f5f-4633-e6c5-bd3aaa337c7b"
      },
      "execution_count": 72,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DatasetDict({\n",
              "    train: Dataset({\n",
              "        features: ['translation'],\n",
              "        num_rows: 23994960\n",
              "    })\n",
              "    test: Dataset({\n",
              "        features: ['translation'],\n",
              "        num_rows: 5998740\n",
              "    })\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 72
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dataset[\"train\"][5]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9mYnB1k6HuO0",
        "outputId": "e3116ef0-b3a9-4835-8148-6e89a6a4fe70"
      },
      "execution_count": 76,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'translation': {'en': '1 long blast repeated at least once a minute',\n",
              "  'ru': '1 продолжительный звук, повторяющийся с интервалом не более одной минуты'}}"
            ]
          },
          "metadata": {},
          "execution_count": 76
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data = dataset[\"train\"]\n",
        "data"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aP-lZUMvWmSC",
        "outputId": "0d98ef2d-87a1-42bb-b005-9d0461f41c65"
      },
      "execution_count": 77,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Dataset({\n",
              "    features: ['translation'],\n",
              "    num_rows: 23994960\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 77
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sharded_dataset = data.shard(num_shards = 300, index=0)\n",
        "sharded_dataset"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dtjDDSEcT897",
        "outputId": "2641eac9-c875-423c-c7a6-e67c28d01ca7"
      },
      "execution_count": 82,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Dataset({\n",
              "    features: ['translation'],\n",
              "    num_rows: 79984\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 82
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sharded_dataset[5]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xLRBju2CXpO9",
        "outputId": "d9cf4adf-7a85-4dc7-f011-70814814199f"
      },
      "execution_count": 85,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'translation': {'en': '23 February 1991', 'ru': '23 февраля 1991 года'}}"
            ]
          },
          "metadata": {},
          "execution_count": 85
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dic = {'translation': {'en': '23 February 1991', 'ru': '23 февраля 1991 года'}}\n",
        "dic['translation']['en']"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "zz2dlUyXZ-EA",
        "outputId": "28bc06d6-0c8d-491d-8c27-369d3ff8f3dd"
      },
      "execution_count": 92,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'23 February 1991'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 92
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sharded_dataset[17]['translation']['en']"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "H0juH6yKaVYy",
        "outputId": "0e83c3c1-c31d-464e-c9fa-906ee8fb013f"
      },
      "execution_count": 96,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'In fact, the very opposite is true; they have only served to harden attitudes of one community against the other.'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 96
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from tqdm import tqdm\n",
        "\n",
        "input = []\n",
        "for i in tqdm(range(len(sharded_dataset))):\n",
        "  input.append(sharded_dataset[i]['translation']['en'])\n",
        "\n",
        "output = []\n",
        "for i in tqdm(range(len(sharded_dataset))):\n",
        "  output.append(sharded_dataset[i]['translation']['ru'])\n",
        "\n",
        "print(len(input), '\\n')\n",
        "print(len(output))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Xcf24uZLaQQp",
        "outputId": "b6d618bb-a778-45cb-edec-57b04a9b6ed9"
      },
      "execution_count": 97,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 79984/79984 [04:55<00:00, 270.52it/s]\n",
            "100%|██████████| 79984/79984 [05:21<00:00, 248.97it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "79984 \n",
            "\n",
            "79984\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sharded_dataset = sharded_dataset.add_column(\"input\", input)\n",
        "sharded_dataset"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 85,
          "referenced_widgets": [
            "f5df6d2deb584d5694eab2ca3d08ca4b",
            "acf787bd9cf94a5195e57a109ce09aa9",
            "d7d54b73f3f34145a9e0da69d0e91651",
            "f9cbcc6c5f024982a709c9ccace126ed",
            "78dbff40860245ae9f94260af510fc65",
            "bcc7352ede194ba99894584cc8eee473",
            "29b2a156c31b4b0e9c199416a1c2aafe",
            "a5726e7fba824b68862cbddbd834d09c",
            "363d0b14c071447c99e942fa44a4b589",
            "dfe69dbabbd14262ad391d1226912243",
            "a64ec5489de948a5a0ed02507d1e52f9"
          ]
        },
        "id": "VN05MoGofr26",
        "outputId": "1c0a5fed-8e1e-4c93-853c-a7f103a8b2d6"
      },
      "execution_count": 118,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Flattening the indices:   0%|          | 0/79984 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "f5df6d2deb584d5694eab2ca3d08ca4b"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Dataset({\n",
              "    features: ['translation', 'input'],\n",
              "    num_rows: 79984\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 118
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sharded_dataset=sharded_dataset.remove_columns(\"translation\")"
      ],
      "metadata": {
        "id": "RF1bzlZ2gG1Y"
      },
      "execution_count": 120,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "sharded_dataset = sharded_dataset.add_column(\"output\", output)\n",
        "sharded_dataset"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "58VW1Vyugje9",
        "outputId": "2f74b865-0895-4190-9843-e120d910ab8b"
      },
      "execution_count": 121,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Dataset({\n",
              "    features: ['input', 'output'],\n",
              "    num_rows: 79984\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 121
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def create_prompt(input: str, output: str):\n",
        "  if len(output) < 1:\n",
        "    output = \"Cannot Find Answer\"\n",
        "  else:\n",
        "    output = output\n",
        "  prompt_template = f\"### INPUT\\n{input}\\n\\n### OUTPUT\\n{output}\"\n",
        "  return prompt_template"
      ],
      "metadata": {
        "id": "AL9nqLJnYsgN"
      },
      "execution_count": 110,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def tokenize_function(samples):\n",
        "  return tokenizer(create_prompt(samples['input'], samples['output']), padding='max_length', truncation=True)\n",
        "\n",
        "\n",
        "\n",
        "mapped_train_dataset = sharded_dataset.map(tokenize_function)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17,
          "referenced_widgets": [
            "a651eb1913724751ae01f4ca7e94480a",
            "4e00d390d5e14117bea06d048ac63eb5",
            "91d2b104d1394e13abf9e47d74a157d4",
            "cbb14ce460244333a4ef107b6cf7224c",
            "6e279f6975bd409bb433e5c99d1d0e83",
            "63c30f60f7644012ad60fb288e4a2dfd",
            "deb01378449e43998693b6859de88aa7",
            "edd2191891fd4ad09494a7821a82b0e6",
            "f58a54d3ee294637bb0b3b9544c2d906",
            "8462c6601e994c81a40a8f5c6c2f6865",
            "8ca62838af734cf79879e68f8eeeef3e"
          ]
        },
        "id": "tzLrRs9ldMJd",
        "outputId": "d01e5895-f402-4c94-fde1-9afd5df0b315"
      },
      "execution_count": 122,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/79984 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "a651eb1913724751ae01f4ca7e94480a"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "mapped_train_dataset"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "o4MO8_hRdhy3",
        "outputId": "a48570d4-3d92-47dc-b403-0b201221c276"
      },
      "execution_count": 123,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Dataset({\n",
              "    features: ['input', 'output', 'input_ids', 'attention_mask'],\n",
              "    num_rows: 79984\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 123
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Training and stuff"
      ],
      "metadata": {
        "id": "yWyxfbU-FiXX"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import transformers\n",
        "\n",
        "trainer = transformers.Trainer(\n",
        "    model=model,\n",
        "    train_dataset=mapped_train_dataset,\n",
        "    args=transformers.TrainingArguments(\n",
        "        per_device_train_batch_size=1,\n",
        "        gradient_accumulation_steps=16,\n",
        "        warmup_steps=100,\n",
        "        max_steps=100,\n",
        "        learning_rate=1e-3,\n",
        "        fp16=True,\n",
        "        logging_steps=1,\n",
        "        output_dir='outputs',\n",
        "    ),\n",
        "    data_collator=transformers.DataCollatorForLanguageModeling(tokenizer, mlm=False)\n",
        ")\n",
        "model.config.use_cache = False"
      ],
      "metadata": {
        "id": "E8bGIOulUuIR"
      },
      "execution_count": 128,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "with torch.autocast(\"cuda\"):\n",
        "    trainer.train()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 324
        },
        "id": "pRgrGbZrVym-",
        "outputId": "7ceecdb1-4da5-48e4-d32e-de12c10e7029"
      },
      "execution_count": 130,
      "outputs": [
        {
          "output_type": "error",
          "ename": "TypeError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-130-916c116ca58b>\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mwith\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mautocast\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"cuda\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m     \u001b[0mtrainer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrain\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/transformers/trainer.py\u001b[0m in \u001b[0;36mtrain\u001b[0;34m(self, resume_from_checkpoint, trial, ignore_keys_for_eval, **kwargs)\u001b[0m\n\u001b[1;32m   1530\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_inner_training_loop\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_train_batch_size\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mauto_find_batch_size\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1531\u001b[0m         )\n\u001b[0;32m-> 1532\u001b[0;31m         return inner_training_loop(\n\u001b[0m\u001b[1;32m   1533\u001b[0m             \u001b[0margs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1534\u001b[0m             \u001b[0mresume_from_checkpoint\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mresume_from_checkpoint\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/transformers/trainer.py\u001b[0m in \u001b[0;36m_inner_training_loop\u001b[0;34m(self, batch_size, args, resume_from_checkpoint, trial, ignore_keys_for_eval)\u001b[0m\n\u001b[1;32m   1803\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1804\u001b[0m                 \u001b[0;32mwith\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0maccelerator\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0maccumulate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1805\u001b[0;31m                     \u001b[0mtr_loss_step\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtraining_step\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1806\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1807\u001b[0m                 if (\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/transformers/trainer.py\u001b[0m in \u001b[0;36mtraining_step\u001b[0;34m(self, model, inputs)\u001b[0m\n\u001b[1;32m   2646\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2647\u001b[0m         \u001b[0;32mwith\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcompute_loss_context_manager\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2648\u001b[0;31m             \u001b[0mloss\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcompute_loss\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2649\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2650\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mn_gpu\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/transformers/trainer.py\u001b[0m in \u001b[0;36mcompute_loss\u001b[0;34m(self, model, inputs, return_outputs)\u001b[0m\n\u001b[1;32m   2671\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2672\u001b[0m             \u001b[0mlabels\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2673\u001b[0;31m         \u001b[0moutputs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0minputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2674\u001b[0m         \u001b[0;31m# Save past state if it exists\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2675\u001b[0m         \u001b[0;31m# TODO: this needs to be fixed and made cleaner later.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1499\u001b[0m                 \u001b[0;32mor\u001b[0m \u001b[0m_global_backward_pre_hooks\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0m_global_backward_hooks\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1500\u001b[0m                 or _global_forward_hooks or _global_forward_pre_hooks):\n\u001b[0;32m-> 1501\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mforward_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1502\u001b[0m         \u001b[0;31m# Do not call functions when jit is used\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1503\u001b[0m         \u001b[0mfull_backward_hooks\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnon_full_backward_hooks\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/accelerate/utils/operations.py\u001b[0m in \u001b[0;36mforward\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    579\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    580\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 581\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mmodel_forward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    582\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    583\u001b[0m     \u001b[0;31m# To act like a decorator so that it can be popped when doing `extract_model_from_parallel`\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/accelerate/utils/operations.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m    567\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    568\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__call__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 569\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mconvert_to_fp32\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmodel_forward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    570\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    571\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__getstate__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/amp/autocast_mode.py\u001b[0m in \u001b[0;36mdecorate_autocast\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m     12\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mdecorate_autocast\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     13\u001b[0m         \u001b[0;32mwith\u001b[0m \u001b[0mautocast_instance\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 14\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     15\u001b[0m     \u001b[0mdecorate_autocast\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__script_unsupported\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'@autocast() decorator is not supported in script mode'\u001b[0m  \u001b[0;31m# type: ignore[attr-defined]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     16\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mdecorate_autocast\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/accelerate/utils/operations.py\u001b[0m in \u001b[0;36mforward\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    579\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    580\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 581\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mmodel_forward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    582\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    583\u001b[0m     \u001b[0;31m# To act like a decorator so that it can be popped when doing `extract_model_from_parallel`\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/accelerate/utils/operations.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m    567\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    568\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__call__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 569\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mconvert_to_fp32\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmodel_forward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    570\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    571\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__getstate__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/amp/autocast_mode.py\u001b[0m in \u001b[0;36mdecorate_autocast\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m     12\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mdecorate_autocast\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     13\u001b[0m         \u001b[0;32mwith\u001b[0m \u001b[0mautocast_instance\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 14\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     15\u001b[0m     \u001b[0mdecorate_autocast\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__script_unsupported\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'@autocast() decorator is not supported in script mode'\u001b[0m  \u001b[0;31m# type: ignore[attr-defined]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     16\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mdecorate_autocast\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/accelerate/utils/operations.py\u001b[0m in \u001b[0;36mforward\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    579\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    580\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 581\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mmodel_forward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    582\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    583\u001b[0m     \u001b[0;31m# To act like a decorator so that it can be popped when doing `extract_model_from_parallel`\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/accelerate/utils/operations.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m    567\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    568\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__call__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 569\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mconvert_to_fp32\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmodel_forward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    570\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    571\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__getstate__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/amp/autocast_mode.py\u001b[0m in \u001b[0;36mdecorate_autocast\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m     12\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mdecorate_autocast\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     13\u001b[0m         \u001b[0;32mwith\u001b[0m \u001b[0mautocast_instance\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 14\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     15\u001b[0m     \u001b[0mdecorate_autocast\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__script_unsupported\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'@autocast() decorator is not supported in script mode'\u001b[0m  \u001b[0;31m# type: ignore[attr-defined]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     16\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mdecorate_autocast\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/accelerate/utils/operations.py\u001b[0m in \u001b[0;36mforward\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    579\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    580\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 581\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mmodel_forward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    582\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    583\u001b[0m     \u001b[0;31m# To act like a decorator so that it can be popped when doing `extract_model_from_parallel`\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/accelerate/utils/operations.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m    567\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    568\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__call__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 569\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mconvert_to_fp32\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmodel_forward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    570\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    571\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__getstate__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/amp/autocast_mode.py\u001b[0m in \u001b[0;36mdecorate_autocast\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m     12\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mdecorate_autocast\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     13\u001b[0m         \u001b[0;32mwith\u001b[0m \u001b[0mautocast_instance\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 14\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     15\u001b[0m     \u001b[0mdecorate_autocast\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__script_unsupported\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'@autocast() decorator is not supported in script mode'\u001b[0m  \u001b[0;31m# type: ignore[attr-defined]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     16\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mdecorate_autocast\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/accelerate/utils/operations.py\u001b[0m in \u001b[0;36mforward\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    579\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    580\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 581\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mmodel_forward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    582\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    583\u001b[0m     \u001b[0;31m# To act like a decorator so that it can be popped when doing `extract_model_from_parallel`\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/accelerate/utils/operations.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m    567\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    568\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__call__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 569\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mconvert_to_fp32\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmodel_forward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    570\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    571\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__getstate__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/amp/autocast_mode.py\u001b[0m in \u001b[0;36mdecorate_autocast\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m     12\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mdecorate_autocast\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     13\u001b[0m         \u001b[0;32mwith\u001b[0m \u001b[0mautocast_instance\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 14\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     15\u001b[0m     \u001b[0mdecorate_autocast\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__script_unsupported\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'@autocast() decorator is not supported in script mode'\u001b[0m  \u001b[0;31m# type: ignore[attr-defined]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     16\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mdecorate_autocast\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/peft/peft_model.py\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, input_ids, attention_mask, inputs_embeds, labels, output_attentions, output_hidden_states, return_dict, **kwargs)\u001b[0m\n\u001b[1;32m    945\u001b[0m                 )\n\u001b[1;32m    946\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 947\u001b[0;31m             return self.base_model(\n\u001b[0m\u001b[1;32m    948\u001b[0m                 \u001b[0minput_ids\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0minput_ids\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    949\u001b[0m                 \u001b[0mattention_mask\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mattention_mask\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1499\u001b[0m                 \u001b[0;32mor\u001b[0m \u001b[0m_global_backward_pre_hooks\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0m_global_backward_hooks\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1500\u001b[0m                 or _global_forward_hooks or _global_forward_pre_hooks):\n\u001b[0;32m-> 1501\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mforward_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1502\u001b[0m         \u001b[0;31m# Do not call functions when jit is used\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1503\u001b[0m         \u001b[0mfull_backward_hooks\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnon_full_backward_hooks\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/accelerate/utils/operations.py\u001b[0m in \u001b[0;36mforward\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    579\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    580\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 581\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mmodel_forward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    582\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    583\u001b[0m     \u001b[0;31m# To act like a decorator so that it can be popped when doing `extract_model_from_parallel`\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/accelerate/utils/operations.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m    567\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    568\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__call__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 569\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mconvert_to_fp32\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmodel_forward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    570\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    571\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__getstate__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/amp/autocast_mode.py\u001b[0m in \u001b[0;36mdecorate_autocast\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m     12\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mdecorate_autocast\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     13\u001b[0m         \u001b[0;32mwith\u001b[0m \u001b[0mautocast_instance\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 14\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     15\u001b[0m     \u001b[0mdecorate_autocast\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__script_unsupported\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'@autocast() decorator is not supported in script mode'\u001b[0m  \u001b[0;31m# type: ignore[attr-defined]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     16\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mdecorate_autocast\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/accelerate/utils/operations.py\u001b[0m in \u001b[0;36mforward\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    579\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    580\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 581\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mmodel_forward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    582\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    583\u001b[0m     \u001b[0;31m# To act like a decorator so that it can be popped when doing `extract_model_from_parallel`\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/accelerate/utils/operations.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m    567\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    568\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__call__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 569\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mconvert_to_fp32\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmodel_forward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    570\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    571\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__getstate__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/amp/autocast_mode.py\u001b[0m in \u001b[0;36mdecorate_autocast\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m     12\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mdecorate_autocast\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     13\u001b[0m         \u001b[0;32mwith\u001b[0m \u001b[0mautocast_instance\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 14\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     15\u001b[0m     \u001b[0mdecorate_autocast\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__script_unsupported\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'@autocast() decorator is not supported in script mode'\u001b[0m  \u001b[0;31m# type: ignore[attr-defined]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     16\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mdecorate_autocast\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/accelerate/utils/operations.py\u001b[0m in \u001b[0;36mforward\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    579\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    580\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 581\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mmodel_forward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    582\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    583\u001b[0m     \u001b[0;31m# To act like a decorator so that it can be popped when doing `extract_model_from_parallel`\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/accelerate/utils/operations.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m    567\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    568\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__call__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 569\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mconvert_to_fp32\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmodel_forward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    570\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    571\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__getstate__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/amp/autocast_mode.py\u001b[0m in \u001b[0;36mdecorate_autocast\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m     12\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mdecorate_autocast\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     13\u001b[0m         \u001b[0;32mwith\u001b[0m \u001b[0mautocast_instance\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 14\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     15\u001b[0m     \u001b[0mdecorate_autocast\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__script_unsupported\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'@autocast() decorator is not supported in script mode'\u001b[0m  \u001b[0;31m# type: ignore[attr-defined]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     16\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mdecorate_autocast\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/peft/peft_model.py\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, input_ids, attention_mask, inputs_embeds, labels, output_attentions, output_hidden_states, return_dict, **kwargs)\u001b[0m\n\u001b[1;32m    945\u001b[0m                 )\n\u001b[1;32m    946\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 947\u001b[0;31m             return self.base_model(\n\u001b[0m\u001b[1;32m    948\u001b[0m                 \u001b[0minput_ids\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0minput_ids\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    949\u001b[0m                 \u001b[0mattention_mask\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mattention_mask\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1499\u001b[0m                 \u001b[0;32mor\u001b[0m \u001b[0m_global_backward_pre_hooks\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0m_global_backward_hooks\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1500\u001b[0m                 or _global_forward_hooks or _global_forward_pre_hooks):\n\u001b[0;32m-> 1501\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mforward_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1502\u001b[0m         \u001b[0;31m# Do not call functions when jit is used\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1503\u001b[0m         \u001b[0mfull_backward_hooks\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnon_full_backward_hooks\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/accelerate/utils/operations.py\u001b[0m in \u001b[0;36mforward\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    579\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    580\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 581\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mmodel_forward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    582\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    583\u001b[0m     \u001b[0;31m# To act like a decorator so that it can be popped when doing `extract_model_from_parallel`\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/accelerate/utils/operations.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m    567\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    568\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__call__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 569\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mconvert_to_fp32\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmodel_forward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    570\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    571\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__getstate__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/amp/autocast_mode.py\u001b[0m in \u001b[0;36mdecorate_autocast\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m     12\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mdecorate_autocast\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     13\u001b[0m         \u001b[0;32mwith\u001b[0m \u001b[0mautocast_instance\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 14\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     15\u001b[0m     \u001b[0mdecorate_autocast\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__script_unsupported\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'@autocast() decorator is not supported in script mode'\u001b[0m  \u001b[0;31m# type: ignore[attr-defined]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     16\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mdecorate_autocast\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/peft/peft_model.py\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m    435\u001b[0m         \u001b[0mForward\u001b[0m \u001b[0;32mpass\u001b[0m \u001b[0mof\u001b[0m \u001b[0mthe\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    436\u001b[0m         \"\"\"\n\u001b[0;32m--> 437\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_base_model\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    438\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    439\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_get_base_model_class\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mis_prompt_tuning\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1499\u001b[0m                 \u001b[0;32mor\u001b[0m \u001b[0m_global_backward_pre_hooks\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0m_global_backward_hooks\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1500\u001b[0m                 or _global_forward_hooks or _global_forward_pre_hooks):\n\u001b[0;32m-> 1501\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mforward_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1502\u001b[0m         \u001b[0;31m# Do not call functions when jit is used\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1503\u001b[0m         \u001b[0mfull_backward_hooks\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnon_full_backward_hooks\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/accelerate/utils/operations.py\u001b[0m in \u001b[0;36mforward\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    579\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    580\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 581\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mmodel_forward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    582\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    583\u001b[0m     \u001b[0;31m# To act like a decorator so that it can be popped when doing `extract_model_from_parallel`\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/accelerate/utils/operations.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m    567\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    568\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__call__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 569\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mconvert_to_fp32\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmodel_forward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    570\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    571\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__getstate__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/amp/autocast_mode.py\u001b[0m in \u001b[0;36mdecorate_autocast\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m     12\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mdecorate_autocast\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     13\u001b[0m         \u001b[0;32mwith\u001b[0m \u001b[0mautocast_instance\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 14\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     15\u001b[0m     \u001b[0mdecorate_autocast\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__script_unsupported\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'@autocast() decorator is not supported in script mode'\u001b[0m  \u001b[0;31m# type: ignore[attr-defined]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     16\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mdecorate_autocast\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/peft/peft_model.py\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m    435\u001b[0m         \u001b[0mForward\u001b[0m \u001b[0;32mpass\u001b[0m \u001b[0mof\u001b[0m \u001b[0mthe\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    436\u001b[0m         \"\"\"\n\u001b[0;32m--> 437\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_base_model\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    438\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    439\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_get_base_model_class\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mis_prompt_tuning\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1499\u001b[0m                 \u001b[0;32mor\u001b[0m \u001b[0m_global_backward_pre_hooks\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0m_global_backward_hooks\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1500\u001b[0m                 or _global_forward_hooks or _global_forward_pre_hooks):\n\u001b[0;32m-> 1501\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mforward_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1502\u001b[0m         \u001b[0;31m# Do not call functions when jit is used\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1503\u001b[0m         \u001b[0mfull_backward_hooks\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnon_full_backward_hooks\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/accelerate/utils/operations.py\u001b[0m in \u001b[0;36mforward\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    579\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    580\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 581\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mmodel_forward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    582\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    583\u001b[0m     \u001b[0;31m# To act like a decorator so that it can be popped when doing `extract_model_from_parallel`\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/accelerate/utils/operations.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m    567\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    568\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__call__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 569\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mconvert_to_fp32\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmodel_forward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    570\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    571\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__getstate__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/amp/autocast_mode.py\u001b[0m in \u001b[0;36mdecorate_autocast\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m     12\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mdecorate_autocast\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     13\u001b[0m         \u001b[0;32mwith\u001b[0m \u001b[0mautocast_instance\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 14\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     15\u001b[0m     \u001b[0mdecorate_autocast\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__script_unsupported\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'@autocast() decorator is not supported in script mode'\u001b[0m  \u001b[0;31m# type: ignore[attr-defined]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     16\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mdecorate_autocast\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/peft/peft_model.py\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, input_ids, attention_mask, inputs_embeds, decoder_input_ids, decoder_attention_mask, decoder_inputs_embeds, labels, output_attentions, output_hidden_states, return_dict, **kwargs)\u001b[0m\n\u001b[1;32m   1103\u001b[0m         \u001b[0mpeft_config\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mactive_peft_config\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1104\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpeft_config\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mPromptLearningConfig\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1105\u001b[0;31m             return self.base_model(\n\u001b[0m\u001b[1;32m   1106\u001b[0m                 \u001b[0minput_ids\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0minput_ids\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1107\u001b[0m                 \u001b[0mattention_mask\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mattention_mask\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1499\u001b[0m                 \u001b[0;32mor\u001b[0m \u001b[0m_global_backward_pre_hooks\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0m_global_backward_hooks\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1500\u001b[0m                 or _global_forward_hooks or _global_forward_pre_hooks):\n\u001b[0;32m-> 1501\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mforward_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1502\u001b[0m         \u001b[0;31m# Do not call functions when jit is used\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1503\u001b[0m         \u001b[0mfull_backward_hooks\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnon_full_backward_hooks\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/accelerate/hooks.py\u001b[0m in \u001b[0;36mnew_forward\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    163\u001b[0m                 \u001b[0moutput\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mold_forward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    164\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 165\u001b[0;31m             \u001b[0moutput\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mold_forward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    166\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mmodule\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_hf_hook\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpost_forward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodule\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moutput\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    167\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mTypeError\u001b[0m: LlamaForCausalLM.forward() got an unexpected keyword argument 'decoder_input_ids'"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "for CausalLM"
      ],
      "metadata": {
        "id": "gGsG0m2bUn9g"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import transformers\n",
        "\n",
        "trainer = transformers.Trainer(\n",
        "    model=model,\n",
        "    train_dataset=mapped_train_dataset,\n",
        "    args=transformers.TrainingArguments(\n",
        "        per_device_train_batch_size=1,\n",
        "        gradient_accumulation_steps=16,\n",
        "        warmup_steps=100,\n",
        "        max_steps=100,\n",
        "        learning_rate=1e-3,\n",
        "        fp16=True,\n",
        "        logging_steps=1,\n",
        "        output_dir='outputs',\n",
        "    ),\n",
        "    data_collator=transformers.DataCollatorForLanguageModeling(tokenizer, mlm=False)\n",
        ")\n",
        "model.config.use_cache = False  # silence the warnings. Please re-enable for inference!"
      ],
      "metadata": {
        "id": "mzQU-OWyhJ2G"
      },
      "execution_count": 29,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "with torch.autocast(\"cuda\"):\n",
        "    trainer.train()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 324
        },
        "id": "0krYK52RhMpi",
        "outputId": "a351243b-0328-440f-f71e-88067341d57b"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "error",
          "ename": "TypeError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-30-916c116ca58b>\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mwith\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mautocast\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"cuda\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m     \u001b[0mtrainer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrain\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/transformers/trainer.py\u001b[0m in \u001b[0;36mtrain\u001b[0;34m(self, resume_from_checkpoint, trial, ignore_keys_for_eval, **kwargs)\u001b[0m\n\u001b[1;32m   1530\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_inner_training_loop\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_train_batch_size\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mauto_find_batch_size\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1531\u001b[0m         )\n\u001b[0;32m-> 1532\u001b[0;31m         return inner_training_loop(\n\u001b[0m\u001b[1;32m   1533\u001b[0m             \u001b[0margs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1534\u001b[0m             \u001b[0mresume_from_checkpoint\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mresume_from_checkpoint\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/transformers/trainer.py\u001b[0m in \u001b[0;36m_inner_training_loop\u001b[0;34m(self, batch_size, args, resume_from_checkpoint, trial, ignore_keys_for_eval)\u001b[0m\n\u001b[1;32m   1803\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1804\u001b[0m                 \u001b[0;32mwith\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0maccelerator\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0maccumulate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1805\u001b[0;31m                     \u001b[0mtr_loss_step\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtraining_step\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1806\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1807\u001b[0m                 if (\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/transformers/trainer.py\u001b[0m in \u001b[0;36mtraining_step\u001b[0;34m(self, model, inputs)\u001b[0m\n\u001b[1;32m   2646\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2647\u001b[0m         \u001b[0;32mwith\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcompute_loss_context_manager\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2648\u001b[0;31m             \u001b[0mloss\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcompute_loss\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2649\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2650\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mn_gpu\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/transformers/trainer.py\u001b[0m in \u001b[0;36mcompute_loss\u001b[0;34m(self, model, inputs, return_outputs)\u001b[0m\n\u001b[1;32m   2671\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2672\u001b[0m             \u001b[0mlabels\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2673\u001b[0;31m         \u001b[0moutputs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0minputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2674\u001b[0m         \u001b[0;31m# Save past state if it exists\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2675\u001b[0m         \u001b[0;31m# TODO: this needs to be fixed and made cleaner later.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1499\u001b[0m                 \u001b[0;32mor\u001b[0m \u001b[0m_global_backward_pre_hooks\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0m_global_backward_hooks\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1500\u001b[0m                 or _global_forward_hooks or _global_forward_pre_hooks):\n\u001b[0;32m-> 1501\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mforward_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1502\u001b[0m         \u001b[0;31m# Do not call functions when jit is used\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1503\u001b[0m         \u001b[0mfull_backward_hooks\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnon_full_backward_hooks\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/accelerate/utils/operations.py\u001b[0m in \u001b[0;36mforward\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    579\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    580\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 581\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mmodel_forward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    582\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    583\u001b[0m     \u001b[0;31m# To act like a decorator so that it can be popped when doing `extract_model_from_parallel`\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/accelerate/utils/operations.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m    567\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    568\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__call__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 569\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mconvert_to_fp32\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmodel_forward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    570\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    571\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__getstate__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/amp/autocast_mode.py\u001b[0m in \u001b[0;36mdecorate_autocast\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m     12\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mdecorate_autocast\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     13\u001b[0m         \u001b[0;32mwith\u001b[0m \u001b[0mautocast_instance\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 14\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     15\u001b[0m     \u001b[0mdecorate_autocast\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__script_unsupported\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'@autocast() decorator is not supported in script mode'\u001b[0m  \u001b[0;31m# type: ignore[attr-defined]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     16\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mdecorate_autocast\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/peft/peft_model.py\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, input_ids, attention_mask, inputs_embeds, labels, output_attentions, output_hidden_states, return_dict, **kwargs)\u001b[0m\n\u001b[1;32m    945\u001b[0m                 )\n\u001b[1;32m    946\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 947\u001b[0;31m             return self.base_model(\n\u001b[0m\u001b[1;32m    948\u001b[0m                 \u001b[0minput_ids\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0minput_ids\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    949\u001b[0m                 \u001b[0mattention_mask\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mattention_mask\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1499\u001b[0m                 \u001b[0;32mor\u001b[0m \u001b[0m_global_backward_pre_hooks\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0m_global_backward_hooks\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1500\u001b[0m                 or _global_forward_hooks or _global_forward_pre_hooks):\n\u001b[0;32m-> 1501\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mforward_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1502\u001b[0m         \u001b[0;31m# Do not call functions when jit is used\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1503\u001b[0m         \u001b[0mfull_backward_hooks\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnon_full_backward_hooks\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/accelerate/utils/operations.py\u001b[0m in \u001b[0;36mforward\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    579\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    580\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 581\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mmodel_forward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    582\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    583\u001b[0m     \u001b[0;31m# To act like a decorator so that it can be popped when doing `extract_model_from_parallel`\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/accelerate/utils/operations.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m    567\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    568\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__call__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 569\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mconvert_to_fp32\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmodel_forward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    570\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    571\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__getstate__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/amp/autocast_mode.py\u001b[0m in \u001b[0;36mdecorate_autocast\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m     12\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mdecorate_autocast\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     13\u001b[0m         \u001b[0;32mwith\u001b[0m \u001b[0mautocast_instance\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 14\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     15\u001b[0m     \u001b[0mdecorate_autocast\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__script_unsupported\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'@autocast() decorator is not supported in script mode'\u001b[0m  \u001b[0;31m# type: ignore[attr-defined]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     16\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mdecorate_autocast\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/peft/peft_model.py\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m    435\u001b[0m         \u001b[0mForward\u001b[0m \u001b[0;32mpass\u001b[0m \u001b[0mof\u001b[0m \u001b[0mthe\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    436\u001b[0m         \"\"\"\n\u001b[0;32m--> 437\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_base_model\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    438\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    439\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_get_base_model_class\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mis_prompt_tuning\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1499\u001b[0m                 \u001b[0;32mor\u001b[0m \u001b[0m_global_backward_pre_hooks\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0m_global_backward_hooks\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1500\u001b[0m                 or _global_forward_hooks or _global_forward_pre_hooks):\n\u001b[0;32m-> 1501\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mforward_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1502\u001b[0m         \u001b[0;31m# Do not call functions when jit is used\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1503\u001b[0m         \u001b[0mfull_backward_hooks\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnon_full_backward_hooks\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/accelerate/utils/operations.py\u001b[0m in \u001b[0;36mforward\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    579\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    580\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 581\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mmodel_forward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    582\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    583\u001b[0m     \u001b[0;31m# To act like a decorator so that it can be popped when doing `extract_model_from_parallel`\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/accelerate/utils/operations.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m    567\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    568\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__call__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 569\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mconvert_to_fp32\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmodel_forward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    570\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    571\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__getstate__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/amp/autocast_mode.py\u001b[0m in \u001b[0;36mdecorate_autocast\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m     12\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mdecorate_autocast\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     13\u001b[0m         \u001b[0;32mwith\u001b[0m \u001b[0mautocast_instance\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 14\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     15\u001b[0m     \u001b[0mdecorate_autocast\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__script_unsupported\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'@autocast() decorator is not supported in script mode'\u001b[0m  \u001b[0;31m# type: ignore[attr-defined]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     16\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mdecorate_autocast\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/peft/peft_model.py\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m    435\u001b[0m         \u001b[0mForward\u001b[0m \u001b[0;32mpass\u001b[0m \u001b[0mof\u001b[0m \u001b[0mthe\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    436\u001b[0m         \"\"\"\n\u001b[0;32m--> 437\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_base_model\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    438\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    439\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_get_base_model_class\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mis_prompt_tuning\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1499\u001b[0m                 \u001b[0;32mor\u001b[0m \u001b[0m_global_backward_pre_hooks\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0m_global_backward_hooks\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1500\u001b[0m                 or _global_forward_hooks or _global_forward_pre_hooks):\n\u001b[0;32m-> 1501\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mforward_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1502\u001b[0m         \u001b[0;31m# Do not call functions when jit is used\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1503\u001b[0m         \u001b[0mfull_backward_hooks\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnon_full_backward_hooks\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/accelerate/utils/operations.py\u001b[0m in \u001b[0;36mforward\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    579\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    580\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 581\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mmodel_forward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    582\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    583\u001b[0m     \u001b[0;31m# To act like a decorator so that it can be popped when doing `extract_model_from_parallel`\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/accelerate/utils/operations.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m    567\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    568\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__call__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 569\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mconvert_to_fp32\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmodel_forward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    570\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    571\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__getstate__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/amp/autocast_mode.py\u001b[0m in \u001b[0;36mdecorate_autocast\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m     12\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mdecorate_autocast\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     13\u001b[0m         \u001b[0;32mwith\u001b[0m \u001b[0mautocast_instance\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 14\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     15\u001b[0m     \u001b[0mdecorate_autocast\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__script_unsupported\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'@autocast() decorator is not supported in script mode'\u001b[0m  \u001b[0;31m# type: ignore[attr-defined]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     16\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mdecorate_autocast\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/peft/peft_model.py\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, input_ids, attention_mask, inputs_embeds, decoder_input_ids, decoder_attention_mask, decoder_inputs_embeds, labels, output_attentions, output_hidden_states, return_dict, **kwargs)\u001b[0m\n\u001b[1;32m   1103\u001b[0m         \u001b[0mpeft_config\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mactive_peft_config\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1104\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpeft_config\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mPromptLearningConfig\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1105\u001b[0;31m             return self.base_model(\n\u001b[0m\u001b[1;32m   1106\u001b[0m                 \u001b[0minput_ids\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0minput_ids\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1107\u001b[0m                 \u001b[0mattention_mask\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mattention_mask\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1499\u001b[0m                 \u001b[0;32mor\u001b[0m \u001b[0m_global_backward_pre_hooks\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0m_global_backward_hooks\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1500\u001b[0m                 or _global_forward_hooks or _global_forward_pre_hooks):\n\u001b[0;32m-> 1501\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mforward_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1502\u001b[0m         \u001b[0;31m# Do not call functions when jit is used\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1503\u001b[0m         \u001b[0mfull_backward_hooks\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnon_full_backward_hooks\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/accelerate/hooks.py\u001b[0m in \u001b[0;36mnew_forward\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    163\u001b[0m                 \u001b[0moutput\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mold_forward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    164\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 165\u001b[0;31m             \u001b[0moutput\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mold_forward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    166\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mmodule\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_hf_hook\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpost_forward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodule\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moutput\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    167\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mTypeError\u001b[0m: LlamaForCausalLM.forward() got an unexpected keyword argument 'decoder_input_ids'"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from huggingface_hub import notebook_login\n",
        "notebook_login()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49,
          "referenced_widgets": [
            "d6366dac8a0c45a8b58a8ad0394c14c6",
            "c4df2c767a9246efa014ec5634ff54b3",
            "26a4dbdb00e64d08a88d6e6ae91ceddc",
            "b740b35c1a714ee187d5e863c11fd143",
            "848d2db23c1b4479a8e99afeebb4d917",
            "d5d0421ececc484faf681f2f706e93c1",
            "f31fc96adb8a4c3b81f6e9f47c611654",
            "78d10864e0df4352b7ae5d0d55bd5921",
            "88350001e0174a4dade8e3af3650e0bc",
            "10fc0ae5183645d498f3532c8ade91e6",
            "f4afc84ff6eb4b1182b5cf385772f01c",
            "8f76f7fbe5fb4e869ae3dc679ee285f6",
            "ef9c8e7ced1f42adb5a8eefab75dbec6",
            "9815e53f0a4d4f1ea0d7c72598af13cf",
            "e67c5b4a4b704f9e830e32793d3a343d",
            "515e1454485f436cb6a7235e9b4203b1",
            "d5607884b2d0428faedcd1b1b3a1747f",
            "acfda2a05204478eb9c6d03f77b003b1",
            "3f33452ee45f4c7897b610f19f0346a1",
            "8500aee3274346fca0c97c2a9a8794af",
            "cd976d46be2b43feab71f999275735b3",
            "f8922bc5a977472facb5072e2edb3a00",
            "dbbfd1de88b247a5bdbde40d56d24eb1"
          ]
        },
        "id": "Le9FHImziLMJ",
        "outputId": "20210ce2-8589-4884-cd1b-d75855723aab"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "VBox(children=(HTML(value='<center> <img\\nsrc=https://huggingface.co/front/assets/huggingface_logo-noborder.sv…"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "d6366dac8a0c45a8b58a8ad0394c14c6"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "HUGGING_FACE_USER_NAME = \"Andron00e\""
      ],
      "metadata": {
        "id": "fOawdoU4iBo8"
      },
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "adapter_name = \"YetAnother_Open-Llama-3B-LoRA-adapter\""
      ],
      "metadata": {
        "id": "voGweXfsiGTJ"
      },
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.push_to_hub(f\"{HUGGING_FACE_USER_NAME}/{adapter_name}\", use_auth_token=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 83,
          "referenced_widgets": [
            "e8ad5f8938e24a989225b7173afb0d57",
            "145846cc6ad5495ba773aa2dfa347867",
            "2ee2750e8582440499a8f410a6390131",
            "fb8a95b5b4794a68943c25752af0ac5b",
            "26e8d0da09574c7e9770e9a9b26d3645",
            "4332577a95c944c3a324174a127b865d",
            "8bd09f7901ce4995aed17a96a00daafc",
            "a37a74ce8d8844e58e0f5b69ac0eadfb",
            "b37ca8de84b7498296d288b369164e03",
            "9d3e0d0d1ea84d1485f9c7a40fe16043",
            "787dcf23355f4c079e1445e7b90be64c"
          ]
        },
        "id": "1aKBuPpCicRy",
        "outputId": "e18e3cb8-65b3-4f80-c32e-9893cc9c5871"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "adapter_model.bin:   0%|          | 0.00/10.7M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "e8ad5f8938e24a989225b7173afb0d57"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "CommitInfo(commit_url='https://huggingface.co/Andron00e/YetAnother_Open-Llama-3B-LoRA-adapter/commit/eb414491f1ee1115875da39f77580738a32f592b', commit_message='Upload model', commit_description='', oid='eb414491f1ee1115875da39f77580738a32f592b', pr_url=None, pr_revision=None, pr_num=None)"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import torch\n",
        "from peft import PeftModel, PeftConfig\n",
        "from transformers import AutoModelForCausalLM, AutoTokenizer\n",
        "\n",
        "peft_model_id = f\"{HUGGING_FACE_USER_NAME}/{adapter_name}\"\n",
        "config = PeftConfig.from_pretrained(peft_model_id)\n",
        "model = AutoModelForCausalLM.from_pretrained(config.base_model_name_or_path, return_dict=True, load_in_8bit=False, torch_dtype=torch.bfloat16, device_map='auto')\n",
        "tokenizer = AutoTokenizer.from_pretrained(config.base_model_name_or_path)\n",
        "\n",
        "\n",
        "qa_model = PeftModel.from_pretrained(model, peft_model_id)"
      ],
      "metadata": {
        "id": "iuqm5MpBijeB"
      },
      "execution_count": 29,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from IPython.display import display, Markdown\n",
        "\n",
        "def make_inference(system_prompt, question):\n",
        "  batch = tokenizer(f\"### CONTEXT\\n{system_prompt}\\n\\n### QUESTION\\n{question}\\n\\n### RESPONSE\\n\\n### TRANSLATED RESPONSE\\n\", return_tensors='pt')\n",
        "\n",
        "  #with torch.cuda.amp.autocast():\n",
        "    #output_tokens = qa_model.generate(**batch, max_new_tokens=200)\n",
        "  with torch.cuda.amp.autocast():\n",
        "  batch = {k: v.to(qa_model.device) for k, v in batch.items()}\n",
        "  output_tokens = qa_model.generate(**batch, max_new_tokens=200)\n",
        "\n",
        "  display(Markdown((tokenizer.decode(output_tokens[0], skip_special_tokens=True))))"
      ],
      "metadata": {
        "id": "ctqBzwfUiqC5"
      },
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "system_prompt = \"Cheese is the best food.\"\n",
        "question = \"What is the best food?\"\n",
        "\n",
        "make_inference(system_prompt, question)"
      ],
      "metadata": {
        "id": "x8oTqUqQjb5h"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "merged_model = qa_model.merge_and_unload()"
      ],
      "metadata": {
        "id": "d44TR816EgrG"
      },
      "execution_count": 30,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "merged_model"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "I6lz8fUOEhvA",
        "outputId": "c5b3ce9e-5770-496b-ccdf-2770bf4a49bd"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LlamaForCausalLM(\n",
              "  (model): LlamaModel(\n",
              "    (embed_tokens): Embedding(32000, 3200, padding_idx=0)\n",
              "    (layers): ModuleList(\n",
              "      (0-25): 26 x LlamaDecoderLayer(\n",
              "        (self_attn): LlamaAttention(\n",
              "          (q_proj): Linear(in_features=3200, out_features=3200, bias=False)\n",
              "          (k_proj): Linear(in_features=3200, out_features=3200, bias=False)\n",
              "          (v_proj): Linear(in_features=3200, out_features=3200, bias=False)\n",
              "          (o_proj): Linear(in_features=3200, out_features=3200, bias=False)\n",
              "          (rotary_emb): LlamaRotaryEmbedding()\n",
              "        )\n",
              "        (mlp): LlamaMLP(\n",
              "          (gate_proj): Linear(in_features=3200, out_features=8640, bias=False)\n",
              "          (up_proj): Linear(in_features=3200, out_features=8640, bias=False)\n",
              "          (down_proj): Linear(in_features=8640, out_features=3200, bias=False)\n",
              "          (act_fn): SiLUActivation()\n",
              "        )\n",
              "        (input_layernorm): LlamaRMSNorm()\n",
              "        (post_attention_layernorm): LlamaRMSNorm()\n",
              "      )\n",
              "    )\n",
              "    (norm): LlamaRMSNorm()\n",
              "  )\n",
              "  (lm_head): Linear(in_features=3200, out_features=32000, bias=False)\n",
              ")"
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tokenizer"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Mg8OpUmyEuIW",
        "outputId": "fc5df277-2054-4f47-e902-fd8a7f01d15f"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LlamaTokenizerFast(name_or_path='openlm-research/open_llama_3b', vocab_size=32000, model_max_length=2048, is_fast=True, padding_side='left', truncation_side='right', special_tokens={'bos_token': AddedToken(\"<s>\", rstrip=False, lstrip=False, single_word=False, normalized=True), 'eos_token': AddedToken(\"</s>\", rstrip=False, lstrip=False, single_word=False, normalized=True), 'unk_token': AddedToken(\"<unk>\", rstrip=False, lstrip=False, single_word=False, normalized=True)}, clean_up_tokenization_spaces=False)"
            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "BXJHyOt4ExKw"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "merged_model_name = \"YetAnother_Open-Llama-3B-LoRA\"\n",
        "\n",
        "merged_model.push_to_hub(f\"{HUGGING_FACE_USER_NAME}/{merged_model_name}\", use_auth_token=True)\n",
        "tokenizer.push_to_hub(f\"{HUGGING_FACE_USER_NAME}/{merged_model_name}\", use_auth_token=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 115,
          "referenced_widgets": [
            "746892b0538f430e99c8bac0c4b774c4",
            "b5c69186fda24431b663256020a9e462",
            "ebeb7d1178ce44538d3da6ea3f9b59e7",
            "bd3ffd4ba0474f12967c9abb490db620",
            "c11c6a8802034107bf62593eaad5996e",
            "bed35210d3a54e20a5770abafaabc191",
            "bca6fc7ee4fe45ee90647993e4a6b96e",
            "f1300dd42219437b9fe5a4b2301a706c",
            "33ffbdb51aa2493eb45a41e4cc0d9333",
            "d79146f637bf404586db3478c4c47e71",
            "2586b5294551415baae6a3ceefa9136e",
            "a74fc98e3c3f46bb85cf29558a9dc74e",
            "e8695f9846cb4d1abac42055511750e4",
            "ab2a4a7bd9304778be0b2d05f677d2ad",
            "6cf244be17fb495d8d626fb9329e78e5",
            "535cc2318b874628a58650d681196e6d",
            "792f38621ec444b88f99f934d3e03db1",
            "7fa1b157b339487bb146d0de36f069b5",
            "d2506c4e999a47f88f1317700ea75d31",
            "1c0b7ee417ff43b5b425c0a41f7ff532",
            "48e2d6ce358143dda8ac200fe6c3f3ba",
            "c811b3c41ab0421e8afa9d357a7a42a2"
          ]
        },
        "id": "S_IOSxf7Ek7q",
        "outputId": "7e7b1a67-7f74-4472-c675-9bb461477c6c"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "pytorch_model.bin:   0%|          | 0.00/6.85G [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "746892b0538f430e99c8bac0c4b774c4"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer.model:   0%|          | 0.00/534k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "a74fc98e3c3f46bb85cf29558a9dc74e"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "CommitInfo(commit_url='https://huggingface.co/Andron00e/YetAnother_Open-Llama-3B-LoRA/commit/101108cebb8bdc8fa9e461c2d3758492da02b0cb', commit_message='Upload tokenizer', commit_description='', oid='101108cebb8bdc8fa9e461c2d3758492da02b0cb', pr_url=None, pr_revision=None, pr_num=None)"
            ]
          },
          "metadata": {},
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "evaluation"
      ],
      "metadata": {
        "id": "YHvnOluvI2o4"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import torch"
      ],
      "metadata": {
        "id": "cOuu6z1zJWiT"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Load model directly\n",
        "from transformers import AutoTokenizer, AutoModelForCausalLM\n",
        "\n",
        "tokenizer = AutoTokenizer.from_pretrained(\"Andron00e/YetAnother_Open-Llama-3B-LoRA\")\n",
        "model = AutoModelForCausalLM.from_pretrained(\"Andron00e/YetAnother_Open-Llama-3B-LoRA\", load_in_8bit=True, torch_dtype=torch.bfloat16, device_map='auto')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49,
          "referenced_widgets": [
            "9f80fddf4fa447c5bdf034b51d413b52",
            "35514bc83aa342f48e8cbadff8167feb",
            "f951e202d6f6448f83214b5b2daf5032",
            "dbb70ba258e24c90882f142bb1251027",
            "0be459212e4b4677be2982a31f5673d3",
            "adfaf2a6bf114143ba19b093a7e810b5",
            "3a1f9995f8f040d59bbe68103bb9733f",
            "b7a3e3525947405e8b63d8f2519656dd",
            "65c5ddfbb44641fe9fb7482e0db5cb4e",
            "5e189dbff0a446ed8e3e04c429a7a9b1",
            "ad1121e22a3d4f8c89018c0ddc534c4d"
          ]
        },
        "id": "oAk1CeDCE2Mn",
        "outputId": "67df0586-7674-4d0a-d81a-3df3b02e3a99"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)neration_config.json:   0%|          | 0.00/137 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "9f80fddf4fa447c5bdf034b51d413b52"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install safetensors"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yCPxIGP2Mq9p",
        "outputId": "d5ea8d24-9d41-49d6-ef99-8c8c426f09d9"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: safetensors in /usr/local/lib/python3.10/dist-packages (0.3.1)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from safetensors import safe_open\n",
        "\n",
        "tensors = {}\n",
        "with safe_open(\"model.safetensors\", framework=\"pt\", device=0) as f:\n",
        "    for k in f.keys():\n",
        "        tensors[k] = f.get_tensor(k)"
      ],
      "metadata": {
        "id": "Q7Rkqw7iMvZA"
      },
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import torch\n",
        "from safetensors.torch import save_file\n",
        "\n",
        "tensors = {\n",
        "    \"embedding\": torch.zeros((2, 2)),\n",
        "    \"attention\": torch.zeros((2, 3))\n",
        "}\n",
        "save_file(tensors, \"model.safetensors\")"
      ],
      "metadata": {
        "id": "ksHqx9aBMvyd"
      },
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from safetensors.torch import load_file\n",
        "\n",
        "model.safetensors = load_file(\"./model.safetensors\")\n",
        "model.safetensors"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JwZTTGIJPOL8",
        "outputId": "a8422fb6-5398-4535-cd5d-5f3e92833ebb"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'attention': tensor([[0., 0., 0.],\n",
              "         [0., 0., 0.]]),\n",
              " 'embedding': tensor([[0., 0.],\n",
              "         [0., 0.]])}"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "merged_model_name = \"YetAnother_Open-Llama-3B-LoRA\"\n",
        "HUGGING_FACE_USER_NAME = \"Andron00e\"\n",
        "\n",
        "model.safetensors.push_to_hub(f\"{HUGGING_FACE_USER_NAME}/{merged_model_name}\", use_auth_token=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 198
        },
        "id": "B2nHERjnPCHe",
        "outputId": "e66ce48d-50ed-42d9-c742-cbfe95b79f26"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "error",
          "ename": "AttributeError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-24-744a5f933e79>\u001b[0m in \u001b[0;36m<cell line: 4>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0mHUGGING_FACE_USER_NAME\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"Andron00e\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0mloaded\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpush_to_hub\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf\"{HUGGING_FACE_USER_NAME}/{merged_model_name}\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0muse_auth_token\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;31mAttributeError\u001b[0m: 'dict' object has no attribute 'push_to_hub'"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from huggingface_hub import upload_file"
      ],
      "metadata": {
        "id": "h0AIBFOgQxTN"
      },
      "execution_count": 29,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "upload_file(path_or_fileobj=\"./model.safetensors\",\n",
        "    path_in_repo=\"model.safetensors\",\n",
        "    repo_id=\"Andron00e/YetAnother_Open-Llama-3B-LoRA\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 67,
          "referenced_widgets": [
            "405d8cb299d14baeaa25a95ade6250d0",
            "d433e7991d1d4ee48b1ecf115b6325b4",
            "5e889a43bff748aa8badfaadcca702b6",
            "d731feebeb724a8c858c0866d4da2675",
            "a22c20654dd74a93aa78a4a1b099fb6a",
            "c00906780437424c88cd337838c6bb6b",
            "96eb15449e564674b135a3d3c45ce9f3",
            "466be7cf326149a3a532f5565db8e823",
            "10f585e0e3bb4cc2b9a2481f05263fbc",
            "b3741191f6b04809a8768cf4c89d78e9",
            "164f4dbfd5f64f85818c00641fec174b"
          ]
        },
        "id": "F5lHteIaQaD_",
        "outputId": "88e390da-a9c1-4a06-82ce-96ab98847200"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "model.safetensors:   0%|          | 0.00/184 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "405d8cb299d14baeaa25a95ade6250d0"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'https://huggingface.co/Andron00e/YetAnother_Open-Llama-3B-LoRA/blob/main/model.safetensors'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!git clone https://github.com/EleutherAI/lm-evaluation-harness"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_Nni7UidI47O",
        "outputId": "893ed9a2-7549-474d-978f-791f190840cd"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "fatal: destination path 'lm-evaluation-harness' already exists and is not an empty directory.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "%cd lm-evaluation-harness"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "I62fmjqDK2fe",
        "outputId": "f07e5228-b6b6-4e20-9a96-0525f24321b2"
      },
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content/lm-evaluation-harness\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install -e \".[multilingual]\""
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JNdOHI5hK5GH",
        "outputId": "a5b2d890-e151-49a6-e69f-720f94acdd78"
      },
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Obtaining file:///content/lm-evaluation-harness\n",
            "  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "Requirement already satisfied: datasets>=2.0.0 in /usr/local/lib/python3.10/dist-packages (from lm-eval==0.3.0) (2.13.1)\n",
            "Requirement already satisfied: jsonlines in /usr/local/lib/python3.10/dist-packages (from lm-eval==0.3.0) (3.1.0)\n",
            "Requirement already satisfied: numexpr in /usr/local/lib/python3.10/dist-packages (from lm-eval==0.3.0) (2.8.4)\n",
            "Requirement already satisfied: openai>=0.6.4 in /usr/local/lib/python3.10/dist-packages (from lm-eval==0.3.0) (0.27.8)\n",
            "Requirement already satisfied: omegaconf>=2.2 in /usr/local/lib/python3.10/dist-packages (from lm-eval==0.3.0) (2.3.0)\n",
            "Requirement already satisfied: peft>=0.2.0 in /usr/local/lib/python3.10/dist-packages (from lm-eval==0.3.0) (0.5.0.dev0)\n",
            "Requirement already satisfied: pybind11>=2.6.2 in /usr/local/lib/python3.10/dist-packages (from lm-eval==0.3.0) (2.11.1)\n",
            "Requirement already satisfied: pycountry in /usr/local/lib/python3.10/dist-packages (from lm-eval==0.3.0) (22.3.5)\n",
            "Requirement already satisfied: pytablewriter in /usr/local/lib/python3.10/dist-packages (from lm-eval==0.3.0) (1.0.0)\n",
            "Requirement already satisfied: rouge-score>=0.0.4 in /usr/local/lib/python3.10/dist-packages (from lm-eval==0.3.0) (0.1.2)\n",
            "Requirement already satisfied: sacrebleu==1.5.0 in /usr/local/lib/python3.10/dist-packages (from lm-eval==0.3.0) (1.5.0)\n",
            "Requirement already satisfied: scikit-learn>=0.24.1 in /usr/local/lib/python3.10/dist-packages (from lm-eval==0.3.0) (1.2.2)\n",
            "Requirement already satisfied: sqlitedict in /usr/local/lib/python3.10/dist-packages (from lm-eval==0.3.0) (2.1.0)\n",
            "Requirement already satisfied: torch>=1.7 in /usr/local/lib/python3.10/dist-packages (from lm-eval==0.3.0) (2.0.1+cu118)\n",
            "Requirement already satisfied: tqdm-multiprocess in /usr/local/lib/python3.10/dist-packages (from lm-eval==0.3.0) (0.0.11)\n",
            "Requirement already satisfied: transformers>=4.1 in /usr/local/lib/python3.10/dist-packages (from lm-eval==0.3.0) (4.32.0.dev0)\n",
            "Requirement already satisfied: zstandard in /usr/local/lib/python3.10/dist-packages (from lm-eval==0.3.0) (0.21.0)\n",
            "Requirement already satisfied: accelerate>=0.17.1 in /usr/local/lib/python3.10/dist-packages (from lm-eval==0.3.0) (0.21.0)\n",
            "Requirement already satisfied: nagisa>=0.2.7 in /usr/local/lib/python3.10/dist-packages (from lm-eval==0.3.0) (0.2.8)\n",
            "Requirement already satisfied: jieba>=0.42.1 in /usr/local/lib/python3.10/dist-packages (from lm-eval==0.3.0) (0.42.1)\n",
            "Requirement already satisfied: portalocker in /usr/local/lib/python3.10/dist-packages (from sacrebleu==1.5.0->lm-eval==0.3.0) (2.7.0)\n",
            "Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.10/dist-packages (from accelerate>=0.17.1->lm-eval==0.3.0) (1.22.4)\n",
            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from accelerate>=0.17.1->lm-eval==0.3.0) (23.1)\n",
            "Requirement already satisfied: psutil in /usr/local/lib/python3.10/dist-packages (from accelerate>=0.17.1->lm-eval==0.3.0) (5.9.5)\n",
            "Requirement already satisfied: pyyaml in /usr/local/lib/python3.10/dist-packages (from accelerate>=0.17.1->lm-eval==0.3.0) (6.0)\n",
            "Requirement already satisfied: pyarrow>=8.0.0 in /usr/local/lib/python3.10/dist-packages (from datasets>=2.0.0->lm-eval==0.3.0) (9.0.0)\n",
            "Requirement already satisfied: dill<0.3.7,>=0.3.0 in /usr/local/lib/python3.10/dist-packages (from datasets>=2.0.0->lm-eval==0.3.0) (0.3.6)\n",
            "Requirement already satisfied: pandas in /usr/local/lib/python3.10/dist-packages (from datasets>=2.0.0->lm-eval==0.3.0) (1.5.3)\n",
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            "Installing collected packages: lm-eval\n",
            "  Attempting uninstall: lm-eval\n",
            "    Found existing installation: lm-eval 0.3.0\n",
            "    Uninstalling lm-eval-0.3.0:\n",
            "      Successfully uninstalled lm-eval-0.3.0\n",
            "  Running setup.py develop for lm-eval\n",
            "Successfully installed lm-eval-0.3.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!python main.py \\\n",
        "    --model hf-causal \\\n",
        "    --model_args pretrained=Andron00e/YetAnother_Open-Llama-3B-LoRA \\\n",
        "    --tasks hellaswag \\\n",
        "    --device cuda:0"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Uu2vXAF0K7OT",
        "outputId": "20ad42d9-c83f-4a52-e324-d6e5b62a199c"
      },
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2023-07-21 11:54:39.467153: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\n",
            "[dynet] random seed: 1234\n",
            "[dynet] allocating memory: 32MB\n",
            "[dynet] memory allocation done.\n",
            "Selected Tasks: ['hellaswag']\n",
            "Using device 'cuda:0'\n",
            "Traceback (most recent call last):\n",
            "  File \"/content/lm-evaluation-harness/main.py\", line 93, in <module>\n",
            "    main()\n",
            "  File \"/content/lm-evaluation-harness/main.py\", line 59, in main\n",
            "    results = evaluator.simple_evaluate(\n",
            "  File \"/content/lm-evaluation-harness/lm_eval/utils.py\", line 243, in _wrapper\n",
            "    return fn(*args, **kwargs)\n",
            "  File \"/content/lm-evaluation-harness/lm_eval/evaluator.py\", line 76, in simple_evaluate\n",
            "    lm = lm_eval.models.get_model(model).create_from_arg_string(\n",
            "  File \"/content/lm-evaluation-harness/lm_eval/base.py\", line 115, in create_from_arg_string\n",
            "    return cls(**args, **args2)\n",
            "  File \"/content/lm-evaluation-harness/lm_eval/models/gpt2.py\", line 85, in __init__\n",
            "    self.model = transformers.AutoModelForCausalLM.from_pretrained(\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/transformers/models/auto/auto_factory.py\", line 493, in from_pretrained\n",
            "    return model_class.from_pretrained(\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/transformers/modeling_utils.py\", line 2629, in from_pretrained\n",
            "    state_dict = load_state_dict(resolved_archive_file)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/transformers/modeling_utils.py\", line 449, in load_state_dict\n",
            "    if metadata.get(\"format\") not in [\"pt\", \"tf\", \"flax\"]:\n",
            "AttributeError: 'NoneType' object has no attribute 'get'\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!git clone https://github.com/EleutherAI/lm-evaluation-harness\n",
        "%cd lm-evaluation-harness"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2TOBePSEUQEr",
        "outputId": "28875d66-ad56-4c70-87c2-68b8ac9fcbf7"
      },
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "fatal: destination path 'lm-evaluation-harness' already exists and is not an empty directory.\n",
            "/content/lm-evaluation-harness\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install -e ."
      ],
      "metadata": {
        "id": "d4JEwL97T221"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install -e \".[multilingual]\""
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "3a-d1ltdT7WA",
        "outputId": "0c5c92d5-72b1-49b9-b24c-b1bf43ace3b8"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.colab-display-data+json": {
              "pip_warning": {
                "packages": [
                  "pydevd_plugins"
                ]
              }
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          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!python main.py \\\n",
        "    --model hf-causal \\\n",
        "    --model_args pretrained=Andron00e/YetAnother_Open-Llama-3B-LoRA \\\n",
        "    --tasks hellaswag \\\n",
        "    --device cuda:0"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OrmBY82-UGxo",
        "outputId": "5d40e079-341f-451f-abc2-ce8c7b92dd2f"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2023-07-21 12:18:29.757618: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\n",
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            "[dynet] memory allocation done.\n",
            "Selected Tasks: ['hellaswag']\n",
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            "Dataset hellaswag downloaded and prepared to /root/.cache/huggingface/datasets/hellaswag/default/0.1.0/512a66dd8b1b1643ab4a48aa4f150d04c91680da6a4096498a5e5f799623d5ae. Subsequent calls will reuse this data.\n",
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            "Task: hellaswag; number of docs: 10042\n",
            "Task: hellaswag; document 0; context prompt (starting on next line):\n",
            "Personal Care and Style: How to increase breast size with a bra. Check your bra size. Wearing a bra that is too big will not make your breasts look larger. That is why it is important to wear the right size bra for you.\n",
            "(end of prompt on previous line)\n",
            "Requests: [Req_loglikelihood('Personal Care and Style: How to increase breast size with a bra. Check your bra size. Wearing a bra that is too big will not make your breasts look larger. That is why it is important to wear the right size bra for you.', ' You can visit a lingerie shop and have them measure you to help you fit a bra to your size, or measure yourself before you shop for a new bra to ensure that you get a good fit. Use a flexible tape measure, like one found in a sewing kit.')[0]\n",
            ", Req_loglikelihood('Personal Care and Style: How to increase breast size with a bra. Check your bra size. Wearing a bra that is too big will not make your breasts look larger. That is why it is important to wear the right size bra for you.', ' This is why it is important to keep your breasts under protection when in the shower and only wear bras that are larger than your breast size. If you are not wearing a bra, try wearing something that is a little bigger.')[0]\n",
            ", Req_loglikelihood('Personal Care and Style: How to increase breast size with a bra. Check your bra size. Wearing a bra that is too big will not make your breasts look larger. That is why it is important to wear the right size bra for you.', ' For a girl, a bra with a support strap will be easier for her, because most women are unable to pull through bra straps and bras that are too small will not be able to support breasts from side-to-side. Many bras have even been created that cover the breast side, and can be sent to other women in the world to make them look bigger.')[0]\n",
            ", Req_loglikelihood('Personal Care and Style: How to increase breast size with a bra. Check your bra size. Wearing a bra that is too big will not make your breasts look larger. That is why it is important to wear the right size bra for you.', ' Choose a color that is flattering to your breast type and specific event, in addition to those that make you uncomfortable. Look for sports bras made from natural material, such as spandex or lycra, as this is a more breathable bra.')[0]\n",
            "]\n",
            "Running loglikelihood requests\n",
            "100% 40145/40145 [1:32:39<00:00,  7.22it/s]\n",
            "{\n",
            "  \"results\": {\n",
            "    \"hellaswag\": {\n",
            "      \"acc\": 0.46176060545708025,\n",
            "      \"acc_stderr\": 0.004975167382061836,\n",
            "      \"acc_norm\": 0.6018721370244972,\n",
            "      \"acc_norm_stderr\": 0.00488511646555027\n",
            "    }\n",
            "  },\n",
            "  \"versions\": {\n",
            "    \"hellaswag\": 0\n",
            "  },\n",
            "  \"config\": {\n",
            "    \"model\": \"hf-causal\",\n",
            "    \"model_args\": \"pretrained=Andron00e/YetAnother_Open-Llama-3B-LoRA\",\n",
            "    \"num_fewshot\": 0,\n",
            "    \"batch_size\": null,\n",
            "    \"batch_sizes\": [],\n",
            "    \"device\": \"cuda:0\",\n",
            "    \"no_cache\": false,\n",
            "    \"limit\": null,\n",
            "    \"bootstrap_iters\": 100000,\n",
            "    \"description_dict\": {}\n",
            "  }\n",
            "}\n",
            "hf-causal (pretrained=Andron00e/YetAnother_Open-Llama-3B-LoRA), limit: None, provide_description: False, num_fewshot: 0, batch_size: None\n",
            "|  Task   |Version| Metric |Value |   |Stderr|\n",
            "|---------|------:|--------|-----:|---|-----:|\n",
            "|hellaswag|      0|acc     |0.4618|±  |0.0050|\n",
            "|         |       |acc_norm|0.6019|±  |0.0049|\n",
            "\n"
          ]
        }
      ]
    }
  ]
}