FineTune.ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "DtgV4TTQ71np"
      },
      "outputs": [],
      "source": [
        "# base model from huggingFace or path to model\n",
        "base_model = \"mistralai/Mistral-7B-Instruct-v0.2\"\n",
        "new_model = \"Enlighten_Instruct\"\n",
        "\n",
        "test_path='/content/Enlighten-Instruct/Dataset/TestData.csv'\n",
        "train_path='/content/Enlighten-Instruct/Dataset/TrainData.csv'"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "fNZZilUV_L4r"
      },
      "outputs": [],
      "source": [
        "%%capture\n",
        "!git clone 'https://github.com/ali7919/Enlighten-Instruct.git'\n",
        "!pip install -U bitsandbytes\n",
        "!pip install transformers==4.36.2\n",
        "!pip install -U peft\n",
        "!pip install -U accelerate\n",
        "!pip install -U trl\n",
        "!pip install datasets==2.16.0\n",
        "!pip install sentencepiece"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "VLzgZ14X_rMs"
      },
      "outputs": [],
      "source": [
        "from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig,HfArgumentParser,TrainingArguments,pipeline, logging\n",
        "from peft import LoraConfig, PeftModel, prepare_model_for_kbit_training, get_peft_model\n",
        "import os,torch\n",
        "from datasets import load_dataset\n",
        "from trl import SFTTrainer\n",
        "import pandas as pd\n",
        "import pyarrow as pa\n",
        "import pyarrow.dataset as ds\n",
        "import pandas as pd\n",
        "from datasets import Dataset\n",
        "import re\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EB3dOioD71nr"
      },
      "source": [
        "# Login to HF"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MwW_ql6WAE7T",
        "outputId": "d0f20555-485a-48d2-e5e1-76a599e2e87e"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Token will not been saved to git credential helper. Pass `add_to_git_credential=True` if you want to set the git credential as well.\n",
            "Token is valid (permission: write).\n",
            "Your token has been saved to /root/.cache/huggingface/token\n",
            "Login successful\n"
          ]
        }
      ],
      "source": [
        "from google.colab import userdata\n",
        "secret_hf = userdata.get('HUGGINGFACE_TOKEN')\n",
        "!huggingface-cli login --token $secret_hf"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cSm1bEkY71ns"
      },
      "source": [
        "# Build Dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uPCfm2jxAE7U",
        "outputId": "444c3dce-8aea-4b76-ccbb-4a2ddb80a708"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "Dataset({\n",
              "    features: ['text'],\n",
              "    num_rows: 1910\n",
              "})"
            ]
          },
          "execution_count": 5,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df = pd.read_csv(train_path)\n",
        "\n",
        "# build training dataset with the right format\n",
        "df['text'] = '<s>[INST]@Enlighten. ' + df['Q'] +'[/INST]'+ df['A'] + '</s>'\n",
        "\n",
        "# remove columns\n",
        "df=df.drop(['Q','A','class'],axis=1)\n",
        "\n",
        "# convert to dataset object\n",
        "dataset = ds.dataset(pa.Table.from_pandas(df).to_batches())\n",
        "dataset = Dataset(pa.Table.from_pandas(df))\n",
        "dataset"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9kEXGYa171ns"
      },
      "source": [
        "# Load the model"
      ]
    },
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        "id": "StJKGiDDHzdk",
        "outputId": "f0355b39-9f75-4c14-f443-cc2cc0b8e758"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:88: UserWarning: \n",
            "The secret `HF_TOKEN` does not exist in your Colab secrets.\n",
            "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n",
            "You will be able to reuse this secret in all of your notebooks.\n",
            "Please note that authentication is recommended but still optional to access public models or datasets.\n",
            "  warnings.warn(\n"
          ]
        },
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        {
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        {
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          "metadata": {},
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        },
        {
          "data": {
            "text/plain": [
              "('<s>', '</s>')"
            ]
          },
          "execution_count": 6,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Load base model\n",
        "bnb_config = BitsAndBytesConfig(\n",
        "    load_in_4bit= True,\n",
        "    bnb_4bit_quant_type= \"nf4\",\n",
        "    bnb_4bit_compute_dtype= torch.bfloat16,\n",
        "    bnb_4bit_use_double_quant= False,\n",
        ")\n",
        "model = AutoModelForCausalLM.from_pretrained(\n",
        "        base_model,\n",
        "        load_in_4bit=True,\n",
        "        quantization_config=bnb_config,\n",
        "        torch_dtype=torch.bfloat16,\n",
        "        device_map=\"auto\",\n",
        "        trust_remote_code=True,\n",
        ")\n",
        "\n",
        "\n",
        "model.config.use_cache = False # silence the warnings. Please re-enable for inference!\n",
        "model.config.pretraining_tp = 1\n",
        "model.gradient_checkpointing_enable()\n",
        "\n",
        "# Load tokenizer\n",
        "tokenizer = AutoTokenizer.from_pretrained(base_model, trust_remote_code=True)\n",
        "tokenizer.padding_side = 'right'\n",
        "tokenizer.pad_token = tokenizer.eos_token\n",
        "tokenizer.add_eos_token = True\n",
        "tokenizer.bos_token, tokenizer.eos_token\n"
      ]
    },
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          ]
        },
        "id": "jSRv5wDMAE7U",
        "outputId": "f291e733-35c8-4750-dc62-2014b7046664"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "1937800e57b84220a6d8d4eb3e2d4f8a",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "tokenizer_config.json:   0%|          | 0.00/776 [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "b3162989ac1647378d27aad38eacd8ce",
              "version_major": 2,
              "version_minor": 0
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            "text/plain": [
              "tokenizer.model:   0%|          | 0.00/500k [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
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              "version_major": 2,
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            },
            "text/plain": [
              "special_tokens_map.json:   0%|          | 0.00/414 [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "c23becaa610a4506b97dc96333aaa163",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "tokenizer.json:   0%|          | 0.00/1.84M [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "text/plain": [
              "962734"
            ]
          },
          "execution_count": 7,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# count trainging tokens\n",
        "from transformers import LlamaTokenizer\n",
        "tokenizer_ = LlamaTokenizer.from_pretrained(\"cognitivecomputations/dolphin-llama2-7b\")\n",
        "tokens = tokenizer_.tokenize(dataset.to_pandas().to_string())\n",
        "len(tokens)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kwEw2EpM71nt"
      },
      "source": [
        "# Fine-Tune"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "DZoClwHcPTqQ"
      },
      "outputs": [],
      "source": [
        "#Adding the adapters in the layers\n",
        "model = prepare_model_for_kbit_training(model)\n",
        "peft_config = LoraConfig(\n",
        "    lora_alpha=16,\n",
        "    lora_dropout=0.1,\n",
        "    r=64,\n",
        "    bias=\"none\",\n",
        "    task_type=\"CAUSAL_LM\",\n",
        "    target_modules=[\"q_proj\", \"k_proj\", \"v_proj\", \"o_proj\",\"gate_proj\"]\n",
        ")\n",
        "model = get_peft_model(model, peft_config)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "peOnLAAhS0y1"
      },
      "outputs": [],
      "source": [
        "#Hyperparamter\n",
        "training_arguments = TrainingArguments(\n",
        "    output_dir=\"./results\",\n",
        "    num_train_epochs=1,\n",
        "    per_device_train_batch_size=4,\n",
        "    gradient_accumulation_steps=1,\n",
        "    optim=\"paged_adamw_32bit\",\n",
        "    save_steps=50,\n",
        "    logging_steps=1,\n",
        "    learning_rate=2e-4,\n",
        "    weight_decay=0.001,\n",
        "    fp16=False,\n",
        "    bf16=False,\n",
        "    max_grad_norm=0.3,\n",
        "    max_steps=-1,\n",
        "    warmup_ratio=0.03,\n",
        "    group_by_length=True,\n",
        "    lr_scheduler_type=\"constant\",\n",
        ")\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 84,
          "referenced_widgets": [
            "893b78dcf1de451a8ca15ee84efbe6b7",
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          ]
        },
        "id": "nLWq2yIZAE7U",
        "outputId": "5c4d843a-6748-446a-daae-840b9e887306"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/trl/trainer/sft_trainer.py:223: UserWarning: You didn't pass a `max_seq_length` argument to the SFTTrainer, this will default to 1024\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "893b78dcf1de451a8ca15ee84efbe6b7",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Map:   0%|          | 0/1910 [00:00<?, ? examples/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Setting sft parameters\n",
        "trainer = SFTTrainer(\n",
        "    model=model,\n",
        "    train_dataset=dataset,\n",
        "    peft_config=peft_config,\n",
        "    max_seq_length= None,\n",
        "    dataset_text_field=\"text\",\n",
        "    tokenizer=tokenizer,\n",
        "    args=training_arguments,\n",
        "    packing= False,\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true,
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "r5EgWxuuAE7U",
        "outputId": "86d96226-8310-4d6c-9b36-90c4ec6193be"
      },
      "outputs": [],
      "source": [
        "trainer.train()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rlBgQqor71nt"
      },
      "source": [
        "# Save and push the adapter to HF"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true,
          "base_uri": "https://localhost:8080/"
        },
        "id": "nKgZBEGVS5a2",
        "outputId": "fa172862-d4c0-4945-8b99-a4b3e3cf3cab"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "PeftModelForCausalLM(\n",
              "  (base_model): LoraModel(\n",
              "    (model): MistralForCausalLM(\n",
              "      (model): MistralModel(\n",
              "        (embed_tokens): Embedding(32000, 4096)\n",
              "        (layers): ModuleList(\n",
              "          (0-31): 32 x MistralDecoderLayer(\n",
              "            (self_attn): MistralAttention(\n",
              "              (q_proj): lora.Linear4bit(\n",
              "                (base_layer): Linear4bit(in_features=4096, out_features=4096, bias=False)\n",
              "                (lora_dropout): ModuleDict(\n",
              "                  (default): Dropout(p=0.1, inplace=False)\n",
              "                )\n",
              "                (lora_A): ModuleDict(\n",
              "                  (default): Linear(in_features=4096, out_features=64, bias=False)\n",
              "                )\n",
              "                (lora_B): ModuleDict(\n",
              "                  (default): Linear(in_features=64, out_features=4096, bias=False)\n",
              "                )\n",
              "                (lora_embedding_A): ParameterDict()\n",
              "                (lora_embedding_B): ParameterDict()\n",
              "              )\n",
              "              (k_proj): lora.Linear4bit(\n",
              "                (base_layer): Linear4bit(in_features=4096, out_features=1024, bias=False)\n",
              "                (lora_dropout): ModuleDict(\n",
              "                  (default): Dropout(p=0.1, inplace=False)\n",
              "                )\n",
              "                (lora_A): ModuleDict(\n",
              "                  (default): Linear(in_features=4096, out_features=64, bias=False)\n",
              "                )\n",
              "                (lora_B): ModuleDict(\n",
              "                  (default): Linear(in_features=64, out_features=1024, bias=False)\n",
              "                )\n",
              "                (lora_embedding_A): ParameterDict()\n",
              "                (lora_embedding_B): ParameterDict()\n",
              "              )\n",
              "              (v_proj): lora.Linear4bit(\n",
              "                (base_layer): Linear4bit(in_features=4096, out_features=1024, bias=False)\n",
              "                (lora_dropout): ModuleDict(\n",
              "                  (default): Dropout(p=0.1, inplace=False)\n",
              "                )\n",
              "                (lora_A): ModuleDict(\n",
              "                  (default): Linear(in_features=4096, out_features=64, bias=False)\n",
              "                )\n",
              "                (lora_B): ModuleDict(\n",
              "                  (default): Linear(in_features=64, out_features=1024, bias=False)\n",
              "                )\n",
              "                (lora_embedding_A): ParameterDict()\n",
              "                (lora_embedding_B): ParameterDict()\n",
              "              )\n",
              "              (o_proj): lora.Linear4bit(\n",
              "                (base_layer): Linear4bit(in_features=4096, out_features=4096, bias=False)\n",
              "                (lora_dropout): ModuleDict(\n",
              "                  (default): Dropout(p=0.1, inplace=False)\n",
              "                )\n",
              "                (lora_A): ModuleDict(\n",
              "                  (default): Linear(in_features=4096, out_features=64, bias=False)\n",
              "                )\n",
              "                (lora_B): ModuleDict(\n",
              "                  (default): Linear(in_features=64, out_features=4096, bias=False)\n",
              "                )\n",
              "                (lora_embedding_A): ParameterDict()\n",
              "                (lora_embedding_B): ParameterDict()\n",
              "              )\n",
              "              (rotary_emb): MistralRotaryEmbedding()\n",
              "            )\n",
              "            (mlp): MistralMLP(\n",
              "              (gate_proj): lora.Linear4bit(\n",
              "                (base_layer): Linear4bit(in_features=4096, out_features=14336, bias=False)\n",
              "                (lora_dropout): ModuleDict(\n",
              "                  (default): Dropout(p=0.1, inplace=False)\n",
              "                )\n",
              "                (lora_A): ModuleDict(\n",
              "                  (default): Linear(in_features=4096, out_features=64, bias=False)\n",
              "                )\n",
              "                (lora_B): ModuleDict(\n",
              "                  (default): Linear(in_features=64, out_features=14336, bias=False)\n",
              "                )\n",
              "                (lora_embedding_A): ParameterDict()\n",
              "                (lora_embedding_B): ParameterDict()\n",
              "              )\n",
              "              (up_proj): Linear4bit(in_features=4096, out_features=14336, bias=False)\n",
              "              (down_proj): Linear4bit(in_features=14336, out_features=4096, bias=False)\n",
              "              (act_fn): SiLU()\n",
              "            )\n",
              "            (input_layernorm): MistralRMSNorm()\n",
              "            (post_attention_layernorm): MistralRMSNorm()\n",
              "          )\n",
              "        )\n",
              "        (norm): MistralRMSNorm()\n",
              "      )\n",
              "      (lm_head): Linear(in_features=4096, out_features=32000, bias=False)\n",
              "    )\n",
              "  )\n",
              ")"
            ]
          },
          "execution_count": 12,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Save the fine-tuned model\n",
        "trainer.model.save_pretrained(new_model)\n",
        "model.config.use_cache = True\n",
        "model.eval()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true,
          "referenced_widgets": [
            "0eaa2092f34a41fda3e3e294f2cae77e"
          ]
        },
        "id": "WDGk9dI0AE7U",
        "outputId": "967e701e-0ce3-4bf9-ada4-2913edf36985"
      },
      "outputs": [],
      "source": [
        "trainer.model.push_to_hub(new_model)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Q_ncORsl71nt"
      },
      "source": [
        "# Test the model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "ThPFD1zZAE7U"
      },
      "outputs": [],
      "source": [
        "logging.set_verbosity(logging.CRITICAL)\n",
        "pipe = pipeline(task=\"text-generation\", model=model, tokenizer=tokenizer, max_length=200)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "d6Axoojg71nt"
      },
      "outputs": [],
      "source": [
        "def build_prompt(question):\n",
        "  prompt=f\"<s>[INST]@Enlighten. {question} [/INST]\"\n",
        "  return prompt"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wsWt_lJbAE7U",
        "outputId": "400e796a-be1f-439f-cdd8-580f4dc2f2b2"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "<s>[INST]@Enlighten. what is the pwoer outage event? [/INST]The power outage event is a global event that is triggered when the power is turned off. it is triggered by the level manager when the power is turned off. it is used to update the UI and change the state of the power switches. it is also used to turn off the power switches and turn on the emergency lights. it is a custom event that is defined in the level manager. it is a delegate that takes no arguments and returns nothing. it is called by the level manager when the power is turned off. it is called by the level manager when the power is turned on. it is called by the level manager when the power is turned off. it is called by the level manager when the power is turned on. it is called by the level manager when the power is turned off. it is called by the level manager when the power is turned on.\n"
          ]
        }
      ],
      "source": [
        "question = \"what is the pwoer outage event?\"\n",
        "prompt = build_prompt(question)\n",
        "result = pipe(prompt)\n",
        "\n",
        "print(result[0]['generated_text'])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QNhtTMafAE7V",
        "outputId": "c74873d9-3cf5-4a9f-d7dd-b2f9ca8398ea"
      },
      "outputs": [],
      "source": [
        "df_test=pd.read_csv(test_path)\n",
        "\n",
        "questionCounter=0\n",
        "correct=0\n",
        "promptEnding = \"[/INST]\"\n",
        "\n",
        "# this must be >= 2\n",
        "fail_limit=10\n",
        "\n",
        "# chain of thought activator, model might run out of output tokens\n",
        "USE_COT=True\n",
        "\n",
        "#this comes before the question\n",
        "testGuide='Answer the following question, at the end of your response write the answer like this: Answer:a or Answer:b or Answer:c or Answer:d \\n'\n",
        "\n",
        "for index, row in df_test.iterrows():\n",
        "    print(\"#############################\")\n",
        "    questionCounter = questionCounter + 1\n",
        "\n",
        "    #chain of thought activator\n",
        "    if USE_COT:\n",
        "        chainOfThoughtActivator='\\nfirst think step by step\\n'\n",
        "    else:\n",
        "        chainOfThoughtActivator='\\n'\n",
        "\n",
        "    #build the prompt\n",
        "    question=testGuide + row['Question'] + '\\na)' + row['a'] + '\\nb)' + row['b'] + '\\nc)' + row['c'] + '\\nd)' + row['d'] + chainOfThoughtActivator\n",
        "    print(question)\n",
        "\n",
        "    #true answer\n",
        "    truth=row['Answer']\n",
        "\n",
        "    #use a loop, if llm stopped before reaching the answer. ask again\n",
        "    index=-1\n",
        "    failCounter=0\n",
        "    while(index==-1):\n",
        "\n",
        "        #build the prompt\n",
        "        prompt = build_prompt(question)\n",
        "\n",
        "        #generate answer\n",
        "        result = pipe(prompt)\n",
        "        llmAnswer = result[0]['generated_text']\n",
        "\n",
        "        #remove our prompt from it\n",
        "        index = llmAnswer.find(promptEnding)\n",
        "        llmAnswer = llmAnswer[len(promptEnding)+index:]\n",
        "\n",
        "        print(\"LLM Answer:\")\n",
        "        print(llmAnswer)\n",
        "\n",
        "        #remove spaces\n",
        "        llmAnswer=llmAnswer.replace(' ','')\n",
        "\n",
        "        #find the option in response\n",
        "        index = llmAnswer.find('Answer:')\n",
        "\n",
        "        #edge case - llm stoped at the worst time\n",
        "        if(index+len('Answer:')==len(llmAnswer)):\n",
        "            index=-1\n",
        "\n",
        "        #update question for the next try. remove chain of thought\n",
        "        question=testGuide + row['Question'] + '\\na)' + row['a'] + '\\nb)' + row['b'] + '\\nc)' + row['c'] + '\\nd)' + row['d']\n",
        "\n",
        "        #Don't get stock on a question\n",
        "        failCounter=failCounter+1\n",
        "        if failCounter==fail_limit:\n",
        "            break\n",
        "\n",
        "    if failCounter==fail_limit:\n",
        "        continue\n",
        "\n",
        "    #find and match the option\n",
        "    next_char = llmAnswer[index+len('Answer:'):][0]\n",
        "    if next_char in truth:\n",
        "        correct=correct+1\n",
        "        print('correct')\n",
        "    else:\n",
        "        print('wrong')\n",
        "\n",
        "    #update accuracy\n",
        "    accuracy=correct/questionCounter\n",
        "    print(f\"Progress: {questionCounter/len(df_test)}\")\n",
        "    print(f\"Accuracy: {accuracy}\")\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "z8izClu0AE7V"
      },
      "source": [
        "--------------------------"
      ]
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "provenance": []
    },
    "kaggle": {
      "accelerator": "gpu",
      "dataSources": [
        {
          "datasetId": 4296684,
          "sourceId": 7391182,
          "sourceType": "datasetVersion"
        },
        {
          "datasetId": 4297213,
          "sourceId": 7391924,
          "sourceType": "datasetVersion"
        },
        {
          "modelInstanceId": 3900,
          "sourceId": 5112,
          "sourceType": "modelInstanceVersion"
        }
      ],
      "dockerImageVersionId": 30559,
      "isGpuEnabled": true,
      "isInternetEnabled": true,
      "language": "python",
      "sourceType": "notebook"
    },
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
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