Fine_Tune_LLaMA_2_with_QLoRA_for_QA_Datasets.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/UmarIgan/Machine-Learning/blob/master/Fine_Tune_LLaMA_2_with_QLoRA_for_QA_Datasets.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Fine-Tune LLaMA 2 with QLoRA\n",
        "By Matt Shumer (https://twitter.com/mattshumer_)\n",
        "\n",
        "Fine-tune LLaMA 2 models w/ very low resource usage.\n",
        "\n",
        "Use the best GPU available (go to Runtime -> change runtime type\n",
        "\n",
        "To fine-tune a model, just load in a JSONL file `train.jsonl` with `prompt` and `response` keys, and do the same for `test.jsonl`. From there, just run all the cells.\n",
        "\n",
        "You can change the model you want to fine-tune by changing `model_name` in the `Define Hyperparameters` cell.\n",
        "\n",
        "Heavy credit goes to https://mlabonne.github.io/blog/posts/Fine_Tune_Your_Own_Llama_2_Model_in_a_Colab_Notebook.html... this is just a slightly modified version that adds a few extra features and supports OpenAI-like jsonl datasets."
      ],
      "metadata": {
        "id": "wM8MRkf8Dr94"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Install&Import libraries"
      ],
      "metadata": {
        "id": "AbrFgrhG_xYi"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install -q accelerate==0.21.0 peft==0.4.0 bitsandbytes==0.40.2 transformers==4.31.0 trl==0.4.7\n",
        "\n",
        "import os\n",
        "import torch\n",
        "from datasets import load_dataset\n",
        "from transformers import (\n",
        "    AutoModelForCausalLM,\n",
        "    AutoTokenizer,\n",
        "    BitsAndBytesConfig,\n",
        "    HfArgumentParser,\n",
        "    TrainingArguments,\n",
        "    pipeline,\n",
        "    logging,\n",
        ")\n",
        "from peft import LoraConfig, PeftModel\n",
        "from trl import SFTTrainer"
      ],
      "metadata": {
        "id": "lPG7wEPetFx2",
        "outputId": "2eecaec5-bb74-4abd-a91c-0068019973ef",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[?25l     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/244.2 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K     \u001b[91m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[91m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m122.9/244.2 kB\u001b[0m \u001b[31m3.8 MB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0m\r\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m244.2/244.2 kB\u001b[0m \u001b[31m4.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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            "\u001b[?25h"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Define Hyperparameters"
      ],
      "metadata": {
        "id": "moVo0led-6tu"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model_name = \"meta-llama/Llama-2-7b-chat-hf\"\n",
        "dataset_name = \"/content/finqa\"\n",
        "new_model = \"llama-2-finetuned-finqa_peft\"\n",
        "lora_r = 64\n",
        "lora_alpha = 16\n",
        "lora_dropout = 0.1\n",
        "use_4bit = True\n",
        "bnb_4bit_compute_dtype = \"float16\"\n",
        "bnb_4bit_quant_type = \"nf4\"\n",
        "use_nested_quant = False\n",
        "output_dir = \"./model\"\n",
        "num_train_epochs = 1\n",
        "fp16 = False\n",
        "bf16 = False\n",
        "per_device_train_batch_size = 4\n",
        "per_device_eval_batch_size = 4\n",
        "gradient_accumulation_steps = 1\n",
        "gradient_checkpointing = True\n",
        "max_grad_norm = 0.3\n",
        "learning_rate = 2e-4\n",
        "weight_decay = 0.001\n",
        "optim = \"paged_adamw_32bit\"\n",
        "lr_scheduler_type = \"constant\"\n",
        "max_steps = -1\n",
        "warmup_ratio = 0.03\n",
        "group_by_length = True\n",
        "save_steps = 5\n",
        "logging_steps = 5\n",
        "max_seq_length = None\n",
        "packing = False\n",
        "device_map = {\"\": 0}"
      ],
      "metadata": {
        "id": "bqfbhUZI-4c_"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Load Datasets"
      ],
      "metadata": {
        "id": "F-J5p5KS_MZY"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "train_dataset = load_dataset(\"dreamerdeo/finqa\", split = 'train').remove_columns([\"post_text\", 'gold_evidence', 'table'])\n",
        "test_dataset = load_dataset(\"dreamerdeo/finqa\", split = 'test').remove_columns([\"post_text\", 'gold_evidence', 'table'])"
      ],
      "metadata": {
        "id": "sv_wzZOJ9CFl",
        "outputId": "aed98d3f-ae49-4305-d166-05166550099f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 298,
          "referenced_widgets": [
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            "text/plain": [
              "Downloading builder script:   0%|          | 0.00/3.56k [00:00<?, ?B/s]"
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              "Downloading readme:   0%|          | 0.00/515 [00:00<?, ?B/s]"
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        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Repo card metadata block was not found. Setting CardData to empty.\n",
            "WARNING:huggingface_hub.repocard:Repo card metadata block was not found. Setting CardData to empty.\n"
          ]
        },
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              "Downloading data: 0.00B [00:00, ?B/s]"
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          "metadata": {}
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        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/root/.cache/huggingface/datasets/downloads/extracted/ca045c27488b49db24426c67e0b35e06b0188746c40ffaf8d01a7b51e52924dc\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating train split: 0 examples [00:00, ? examples/s]"
            ],
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              "model_id": "e556f2e8dea640e1ae2e9b7f40a71d9f"
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          "data": {
            "text/plain": [
              "Generating validation split: 0 examples [00:00, ? examples/s]"
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              "model_id": "9eff5d57d14043959b0e1d95900bb9c6"
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          "metadata": {}
        },
        {
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              "Generating test split: 0 examples [00:00, ? examples/s]"
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              "version_minor": 0,
              "model_id": "f0e92aff0593473cbe877728023f6005"
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          "metadata": {}
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        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Repo card metadata block was not found. Setting CardData to empty.\n",
            "WARNING:huggingface_hub.repocard:Repo card metadata block was not found. Setting CardData to empty.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def add_prefix(example):\n",
        "    example[\"text\"] = ('Follow the given context: ' + ' '.join([str(item) for item in example['pre_text']]) + 'This is the question about this context: ' + example[\"question\"] + 'and this is the answer: ' +  example[\"answer\"])\n",
        "    return example"
      ],
      "metadata": {
        "id": "ClFE53H0-Q9d"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train_dataset.features"
      ],
      "metadata": {
        "id": "8hcksVUv9XTj",
        "outputId": "446efded-a277-4525-8da3-d98f3d078897",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'id': Value(dtype='string', id=None),\n",
              " 'pre_text': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None),\n",
              " 'question': Value(dtype='string', id=None),\n",
              " 'answer': Value(dtype='string', id=None)}"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "test_dataset.features"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "E-QNZDh3ttDW",
        "outputId": "6abc39f3-d0cf-4f74-8975-f5790347873f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'id': Value(dtype='string', id=None),\n",
              " 'pre_text': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None),\n",
              " 'question': Value(dtype='string', id=None),\n",
              " 'answer': Value(dtype='string', id=None)}"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from huggingface_hub import login\n",
        "login()"
      ],
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        }
      },
      "execution_count": null,
      "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": "d168828273da419bae53246126ce5da0"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Training"
      ],
      "metadata": {
        "id": "v9ct-9kWfMVX"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Preprocess datasets\n",
        "train_dataset = train_dataset.map(add_prefix)\n",
        "test_dataset = test_dataset.map(add_prefix)\n",
        "\n",
        "compute_dtype = getattr(torch, bnb_4bit_compute_dtype)\n",
        "bnb_config = BitsAndBytesConfig(\n",
        "    load_in_4bit=use_4bit,\n",
        "    bnb_4bit_quant_type=bnb_4bit_quant_type,\n",
        "    bnb_4bit_compute_dtype=compute_dtype,\n",
        "    bnb_4bit_use_double_quant=use_nested_quant,\n",
        ")\n",
        "\n",
        "model = AutoModelForCausalLM.from_pretrained(\n",
        "    model_name,\n",
        "    quantization_config=bnb_config,\n",
        "    device_map=device_map\n",
        "    )\n",
        "\n",
        "model.config.use_cache = False\n",
        "model.config.pretraining_tp = 1\n",
        "tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)\n",
        "tokenizer.pad_token = tokenizer.eos_token\n",
        "tokenizer.padding_side = \"right\"\n",
        "\n",
        "peft_config = LoraConfig(\n",
        "    lora_alpha=lora_alpha,\n",
        "    lora_dropout=lora_dropout,\n",
        "    r=lora_r,\n",
        "    bias=\"none\",\n",
        "    task_type=\"CAUSAL_LM\",\n",
        ")"
      ],
      "metadata": {
        "id": "AXFfmRB0Fulo",
        "outputId": "bb088468-8ad1-4fc4-d8ae-982310121b0c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 433,
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        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
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            ],
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          },
          "metadata": {}
        },
        {
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    },
    {
      "cell_type": "code",
      "source": [
        "# Set training parameters\n",
        "training_arguments = TrainingArguments(\n",
        "    output_dir=output_dir,\n",
        "    num_train_epochs=num_train_epochs,\n",
        "    per_device_train_batch_size=per_device_train_batch_size,\n",
        "    gradient_accumulation_steps=gradient_accumulation_steps,\n",
        "    optim=optim,\n",
        "    save_steps=save_steps,\n",
        "    logging_steps=logging_steps,\n",
        "    learning_rate=learning_rate,\n",
        "    weight_decay=weight_decay,\n",
        "    fp16=fp16,\n",
        "    bf16=bf16,\n",
        "    max_grad_norm=max_grad_norm,\n",
        "    max_steps=max_steps,\n",
        "    warmup_ratio=warmup_ratio,\n",
        "    group_by_length=group_by_length,\n",
        "    lr_scheduler_type=lr_scheduler_type,\n",
        "    report_to=\"all\",\n",
        "    evaluation_strategy=\"steps\",\n",
        "    eval_steps=20  # Evaluate every 20 steps\n",
        ")\n",
        "# Set supervised fine-tuning parameters\n",
        "trainer = SFTTrainer(\n",
        "    model=model,\n",
        "    train_dataset=train_dataset,\n",
        "    eval_dataset=test_dataset,  # Pass validation dataset here\n",
        "    peft_config=peft_config,\n",
        "    dataset_text_field=\"text\",\n",
        "    max_seq_length=max_seq_length,\n",
        "    tokenizer=tokenizer,\n",
        "    args=training_arguments,\n",
        "    packing=packing,\n",
        ")\n",
        "trainer.train()"
      ],
      "metadata": {
        "id": "qf1qxbiF-x6p",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 821,
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            "03f78d0244254bcb85d9c6758b3975d9",
            "9fdf610d7a1f48448eeef998f35a1762",
            "020cdec40d6a4d92a19cc1e78abd2b0a",
            "c99a523115ae4bc8b3936dbbbc0faab3",
            "15904e65c4ef4eb8bb7fd8eafb4121e4",
            "53d01667e2254e26b0adbb228007fe4b",
            "0dd830090eb143bf899397d50425b3ef",
            "43cb65ffaca44397befb4ab8089dc993",
            "25fca763e8f04d4eb5def01ddc8063b8"
          ]
        },
        "outputId": "159c9a3e-3cf9-41bb-b0f6-3a69379ed234"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/peft/utils/other.py:102: FutureWarning: prepare_model_for_int8_training is deprecated and will be removed in a future version. Use prepare_model_for_kbit_training instead.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/trl/trainer/sft_trainer.py:159: UserWarning: You didn't pass a `max_seq_length` argument to the SFTTrainer, this will default to 1024\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/6251 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "c0867131a13f463ba3cd9ec0532bbf42"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/1147 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "ca1674b73b574cec8e00058fc0d821f1"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "You're using a LlamaTokenizerFast tokenizer. Please note that with a fast tokenizer, using the `__call__` method is faster than using a method to encode the text followed by a call to the `pad` method to get a padded encoding.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='141' max='1563' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [ 141/1563 2:22:07 < 24:13:56, 0.02 it/s, Epoch 0.09/1]\n",
              "    </div>\n",
              "    <table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              " <tr style=\"text-align: left;\">\n",
              "      <th>Step</th>\n",
              "      <th>Training Loss</th>\n",
              "      <th>Validation Loss</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>20</td>\n",
              "      <td>1.723800</td>\n",
              "      <td>1.620092</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>40</td>\n",
              "      <td>1.492200</td>\n",
              "      <td>1.406125</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>60</td>\n",
              "      <td>1.375000</td>\n",
              "      <td>1.345147</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>80</td>\n",
              "      <td>1.321100</td>\n",
              "      <td>1.314960</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>100</td>\n",
              "      <td>1.212900</td>\n",
              "      <td>1.331055</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>120</td>\n",
              "      <td>1.311300</td>\n",
              "      <td>1.299244</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>\n",
              "    <div>\n",
              "      \n",
              "      <progress value='106' max='144' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [106/144 14:55 < 05:24, 0.12 it/s]\n",
              "    </div>\n",
              "    "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "error",
          "ename": "KeyboardInterrupt",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-7-4a75c26af992>\u001b[0m in \u001b[0;36m<cell line: 35>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     33\u001b[0m     \u001b[0mpacking\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mpacking\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     34\u001b[0m )\n\u001b[0;32m---> 35\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   1537\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   1538\u001b[0m         )\n\u001b[0;32m-> 1539\u001b[0;31m         return inner_training_loop(\n\u001b[0m\u001b[1;32m   1540\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   1541\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   1899\u001b[0m                     \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcontrol\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcallback_handler\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mon_step_end\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstate\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcontrol\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1900\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1901\u001b[0;31m                     \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_maybe_log_save_evaluate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtr_loss\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtrial\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mepoch\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mignore_keys_for_eval\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   1902\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   1903\u001b[0m                     \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcontrol\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcallback_handler\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mon_substep_end\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstate\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcontrol\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_maybe_log_save_evaluate\u001b[0;34m(self, tr_loss, model, trial, epoch, ignore_keys_for_eval)\u001b[0m\n\u001b[1;32m   2224\u001b[0m                     \u001b[0mmetrics\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdataset_metrics\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2225\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-> 2226\u001b[0;31m                 \u001b[0mmetrics\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mevaluate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mignore_keys\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mignore_keys_for_eval\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   2227\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_report_to_hp_search\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrial\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstate\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mglobal_step\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmetrics\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2228\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;36mevaluate\u001b[0;34m(self, eval_dataset, ignore_keys, metric_key_prefix)\u001b[0m\n\u001b[1;32m   2932\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2933\u001b[0m         \u001b[0meval_loop\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprediction_loop\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[0muse_legacy_prediction_loop\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mevaluation_loop\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2934\u001b[0;31m         output = eval_loop(\n\u001b[0m\u001b[1;32m   2935\u001b[0m             \u001b[0meval_dataloader\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2936\u001b[0m             \u001b[0mdescription\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"Evaluation\"\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;36mevaluation_loop\u001b[0;34m(self, dataloader, description, prediction_loss_only, ignore_keys, metric_key_prefix)\u001b[0m\n\u001b[1;32m   3111\u001b[0m         \u001b[0mobserved_num_examples\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3112\u001b[0m         \u001b[0;31m# Main evaluation loop\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3113\u001b[0;31m         \u001b[0;32mfor\u001b[0m \u001b[0mstep\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minputs\u001b[0m \u001b[0;32min\u001b[0m \u001b[0menumerate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdataloader\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   3114\u001b[0m             \u001b[0;31m# Update the observed num examples\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3115\u001b[0m             \u001b[0mobserved_batch_size\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfind_batch_size\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[0;32m/usr/local/lib/python3.10/dist-packages/accelerate/data_loader.py\u001b[0m in \u001b[0;36m__iter__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    391\u001b[0m                 \u001b[0;31m# But we still move it to the device so it is done before `StopIteration` is reached\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    392\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdevice\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 393\u001b[0;31m                     \u001b[0mcurrent_batch\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msend_to_device\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcurrent_batch\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdevice\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    394\u001b[0m                 \u001b[0mnext_batch\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnext\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdataloader_iter\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    395\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0mbatch_index\u001b[0m \u001b[0;34m>=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mskip_batches\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;36msend_to_device\u001b[0;34m(tensor, device, non_blocking, skip_keys)\u001b[0m\n\u001b[1;32m    158\u001b[0m             \u001b[0mskip_keys\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[1;32m    159\u001b[0m         return type(tensor)(\n\u001b[0;32m--> 160\u001b[0;31m             {\n\u001b[0m\u001b[1;32m    161\u001b[0m                 \u001b[0mk\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mt\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mk\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mskip_keys\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0msend_to_device\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdevice\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnon_blocking\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnon_blocking\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mskip_keys\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mskip_keys\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    162\u001b[0m                 \u001b[0;32mfor\u001b[0m \u001b[0mk\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mt\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mtensor\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\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;36m<dictcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m    159\u001b[0m         return type(tensor)(\n\u001b[1;32m    160\u001b[0m             {\n\u001b[0;32m--> 161\u001b[0;31m                 \u001b[0mk\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mt\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mk\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mskip_keys\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0msend_to_device\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdevice\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnon_blocking\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnon_blocking\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mskip_keys\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mskip_keys\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    162\u001b[0m                 \u001b[0;32mfor\u001b[0m \u001b[0mk\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mt\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mtensor\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\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    163\u001b[0m             }\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/accelerate/utils/operations.py\u001b[0m in \u001b[0;36msend_to_device\u001b[0;34m(tensor, device, non_blocking, skip_keys)\u001b[0m\n\u001b[1;32m    165\u001b[0m     \u001b[0;32melif\u001b[0m \u001b[0mhasattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtensor\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"to\"\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    166\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 167\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mtensor\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdevice\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnon_blocking\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnon_blocking\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    168\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m:\u001b[0m  \u001b[0;31m# .to() doesn't accept non_blocking as kwarg\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    169\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mtensor\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdevice\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "trainer.push_to_hub(\"llama-2-finetuned-finqa_peft\")"
      ],
      "metadata": {
        "id": "a4shgZUPF_RN"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#trainer.model.save_pretrained(new_model) - uncomment your option\n",
        "from peft import PeftModel\n",
        "from transformers import AutoModelForCausalLM\n",
        "import torch\n",
        "model = AutoModelForCausalLM.from_pretrained(\"meta-llama/Llama-2-7b-chat-hf\", trust_remote_code=True, torch_dtype=torch.float32)\n",
        "peft_model = PeftModel.from_pretrained(model, \"umarigan/llama-2-finetuned-finqa\", from_transformers=True)\n",
        "model = peft_model.merge_and_unload()\n",
        "model.push_to_hub(\"llama-2-finetuned-finqa\")"
      ],
      "metadata": {
        "id": "r8EutIEWNMSU"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Testing new model."
      ],
      "metadata": {
        "id": "xrw8I1nlGj8o"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import torch\n",
        "from transformers import AutoModelForCausalLM, AutoTokenizer\n",
        "\n",
        "model = AutoModelForCausalLM.from_pretrained(\"umarigan/llama-2-finetuned-finqa\", trust_remote_code=True, torch_dtype=torch.float32)\n",
        "tokenizer = AutoTokenizer.from_pretrained(\"meta-llama/Llama-2-7b-chat-hf\", trust_remote_code=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 177,
          "referenced_widgets": [
            "fd93b331262b4b18af6ed6ded01bc1bf",
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            "6fcefe2ea2d54c6dae2a435d990061ab",
            "ef78a28d18614cb38f7725ca9fc19743",
            "a91612ab4ffd45989d1615e539ed6bce",
            "cc74a0f033a44389acbbf5c68e2c5677",
            "500ef8f6c984490287f5332f35499e54",
            "af05540a317f4108a2150918372698fe",
            "ab81caa681be4f02abdb043751eefc88",
            "a9f7be7430454662a31bcdd9675b88fb",
            "ee83789f9cc4423d981b11c7aa8f6bd9",
            "10fd0134e4e2474182dcd2ddba4a4d58",
            "f50b7774f9494b1397906593737e29db",
            "d7230b0321d64591b26543cb4cb4b194",
            "97415705d55c4503b06c5787a366e57f",
            "cf33bdbd131a4898a7215fb42537309e",
            "b3938c71b3604d7cb456120cbf399ad1",
            "61b3e19d941f4dfe9d423614c535e18d",
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            "3385b737bd53499f9ad6daa3b28d3868",
            "ad20083dc1b542adaaaafa5fe8432de6",
            "37d79778d6a84ec5b51288aabeaa1357",
            "a9ea68930786460280459dd929139971",
            "08331712642c45588b802e69dd91ece3",
            "d28a725139014443b3776366e965a91d",
            "070401597b3547c28c66e55bb92c7cdc",
            "d88f92e3c0354809b6799b1b286b73ab",
            "639494857ff54406a0730635c414152c",
            "89c1600c388546cc95779be505205463",
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            "54d16a04ff5049c4be326e2bfc529081",
            "07f108f149b24e9588b5c4375bcd8a89",
            "56635b73d1564d28afb5dd843aaf1c73",
            "ced9bc7ef7304de9b26e2dba9410428e",
            "f867ede9a2c24802ae5b93ae9fe2e6be",
            "6dc54868820d4df599592371d08df1fc",
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            "d57d4f3813c64c1db4da99f871ec4328",
            "570b47435f434899bc4142edfac2e44d",
            "9e294f0ace86482aa2f8059a46ae00ef",
            "d853d581cd5344fa929b85bdc4b73bbb",
            "51d25d6ae1cd49698798c83c9fb7d5a0",
            "2e42affb929a4e4d8df06887488a04a9",
            "081159a75c034f43957b287e5fa45af1",
            "6cfdf2c2e21f4c2ab8c88d0b12a9420d",
            "0dfcacaef95e462aa0d0dba1cfe82959",
            "0cb57022066c4092a2156ee0cf9eb044",
            "2a15d52ced2f422790bdbd9db5d627ee",
            "c5d286a7ba714125bdbf5ec625025968",
            "1a9a029bc2304f83b72dc4a1ce4c0e2c",
            "23226d53933341f7a959874311215088",
            "128f5c1cdaca44569be665dae72fd16a",
            "5ce6425f81974f8f8737c79d6f239f49",
            "8098219bacc148128e049da6559293b0"
          ]
        },
        "id": "4wa39AXUN2Q1",
        "outputId": "1424d5a5-090f-4636-cba6-e3a05cc8ef95"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)/adapter_config.json:   0%|          | 0.00/451 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "fd93b331262b4b18af6ed6ded01bc1bf"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)lve/main/config.json:   0%|          | 0.00/507 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "ee83789f9cc4423d981b11c7aa8f6bd9"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)model.bin.index.json:   0%|          | 0.00/26.8k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "37d79778d6a84ec5b51288aabeaa1357"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading shards:   0%|          | 0/2 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "56635b73d1564d28afb5dd843aaf1c73"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)l-00001-of-00002.bin:   0%|          | 0.00/9.98G [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "081159a75c034f43957b287e5fa45af1"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "actual_valid_dataset = load_dataset(\"dreamerdeo/finqa\", split = 'validation')"
      ],
      "metadata": {
        "id": "94Y-xWK3FAGF",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 176
        },
        "outputId": "af59d17b-3eb8-40f7-a2d0-ea7423465838"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "NameError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-1-1d80d71c5e98>\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mactual_valid_dataset\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mload_dataset\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"dreamerdeo/finqa\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msplit\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'validation'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m: name 'load_dataset' is not defined"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "actual_valid_dataset[\"pre_text\"][:3]"
      ],
      "metadata": {
        "id": "1mOm05FtFTy6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 176
        },
        "outputId": "a7756222-6607-4a93-a671-5f2146de2a5b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "NameError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
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        "question_answerer = pipeline(\"question-answering\")\n",
        "context = \"\"\"\n",
        "performance graph comparison of five-year cumulative total return the following graph and table compare the cumulative\n",
        "total return on citi 2019s common stock , which is listed on the nyse under the ticker symbol 201cc 201d and held by\n",
        "65691 common stockholders of record as of january 31 , 2018 , with the cumulative total return of the s&p 500 index\n",
        "and the s&p financial index over the five-year period through december 31 , 2017 .\", \"the graph and table assume\n",
        "that $ 100 was invested on december 31 , 2012 in citi 2019s common stock , the s&p 500 index and the s&p financial\n",
        "index , and that all dividends were reinvested .\", \"comparison of five-year cumulative total return for the years\n",
        "ended date citi s&p 500 financials .\n",
        "\"\"\"\n",
        "question = \"what was the percentage cumulative total return for the five year period ended 31-dec-2017 of citi common stock?\"\n",
        "question_answerer(question=question, context=context, model=model, tokenizer=tokenizer, max_length=200)#answer is 93.5%"
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