{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "SwcwXRajHelL",
    "outputId": "553c2843-5a43-430e-d2fc-cdfabfa8e309"
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Wed Sep  6 14:56:22 2023       \n",
      "+-----------------------------------------------------------------------------+\n",
      "| NVIDIA-SMI 525.105.17   Driver Version: 525.105.17   CUDA Version: 12.0     |\n",
      "|-------------------------------+----------------------+----------------------+\n",
      "| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |\n",
      "| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |\n",
      "|                               |                      |               MIG M. |\n",
      "|===============================+======================+======================|\n",
      "|   0  Tesla V100-SXM2...  Off  | 00000000:00:04.0 Off |                    0 |\n",
      "| N/A   35C    P0    26W / 300W |      0MiB / 16384MiB |      0%      Default |\n",
      "|                               |                      |                  N/A |\n",
      "+-------------------------------+----------------------+----------------------+\n",
      "                                                                               \n",
      "+-----------------------------------------------------------------------------+\n",
      "| Processes:                                                                  |\n",
      "|  GPU   GI   CI        PID   Type   Process name                  GPU Memory |\n",
      "|        ID   ID                                                   Usage      |\n",
      "|=============================================================================|\n",
      "|  No running processes found                                                 |\n",
      "+-----------------------------------------------------------------------------+\n"
     ]
    }
   ],
   "source": [
    "!nvidia-smi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "rpf1Z0k4RJM6",
    "outputId": "40038a1c-2aad-472f-e966-f1d61349bc01"
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n",
      "\u001b[0m\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n",
      "\u001b[0m\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n",
      "\u001b[0m\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n",
      "\u001b[0m\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n",
      "\u001b[0m\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n",
      "\u001b[0m"
     ]
    }
   ],
   "source": [
    "!pip install -Uqqq pip --progress-bar off\n",
    "!pip install -qqq torch==2.0.1 --progress-bar off\n",
    "!pip install -qqq transformers==4.32.1 --progress-bar off\n",
    "!pip install -qqq datasets==2.14.4 --progress-bar off\n",
    "!pip install -qqq peft==0.5.0 --progress-bar off\n",
    "!pip install -qqq bitsandbytes==0.41.1 --progress-bar off\n",
    "!pip install -qqq trl==0.7.1 --progress-bar off"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "id": "P1PG0WSvRqVq"
   },
   "outputs": [],
   "source": [
    "import json\n",
    "import re\n",
    "from pprint import pprint\n",
    "\n",
    "import pandas as pd\n",
    "import torch\n",
    "from datasets import Dataset, load_dataset\n",
    "from huggingface_hub import notebook_login\n",
    "from peft import LoraConfig, PeftModel\n",
    "from transformers import (\n",
    "    AutoModelForCausalLM,\n",
    "    AutoTokenizer,\n",
    "    BitsAndBytesConfig,\n",
    "    TrainingArguments,\n",
    ")\n",
    "from trl import SFTTrainer\n",
    "\n",
    "DEVICE = \"cuda:0\" if torch.cuda.is_available() else \"cpu\"\n",
    "MODEL_NAME = \"meta-llama/Llama-2-7b-hf\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "4ixsX2Y4doEf"
   },
   "source": [
    "## Data"
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "dataset = load_dataset(\"Salesforce/dialogstudio\", \"TweetSumm\")\n",
    "dataset"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Kc0CVTtUkWvl",
    "outputId": "f69dcc3b-aeeb-4267-bb26-476465cbc80a"
   },
   "execution_count": 2,
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "DatasetDict({\n",
       "    train: Dataset({\n",
       "        features: ['original dialog id', 'new dialog id', 'dialog index', 'original dialog info', 'log', 'prompt'],\n",
       "        num_rows: 879\n",
       "    })\n",
       "    validation: Dataset({\n",
       "        features: ['original dialog id', 'new dialog id', 'dialog index', 'original dialog info', 'log', 'prompt'],\n",
       "        num_rows: 110\n",
       "    })\n",
       "    test: Dataset({\n",
       "        features: ['original dialog id', 'new dialog id', 'dialog index', 'original dialog info', 'log', 'prompt'],\n",
       "        num_rows: 110\n",
       "    })\n",
       "})"
      ]
     },
     "metadata": {},
     "execution_count": 2
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "DEFAULT_SYSTEM_PROMPT = \"\"\"\n",
    "Below is a conversation between a human and an AI agent. Write a summary of the conversation.\n",
    "\"\"\".strip()\n",
    "\n",
    "\n",
    "def generate_training_prompt(\n",
    "    conversation: str, summary: str, system_prompt: str = DEFAULT_SYSTEM_PROMPT\n",
    ") -> str:\n",
    "    return f\"\"\"### Instruction: {system_prompt}\n",
    "\n",
    "### Input:\n",
    "{conversation.strip()}\n",
    "\n",
    "### Response:\n",
    "{summary}\n",
    "\"\"\".strip()"
   ],
   "metadata": {
    "id": "fbx71jhaMGK5"
   },
   "execution_count": 3,
   "outputs": []
  },
  {
   "cell_type": "code",
   "source": [
    "def clean_text(text):\n",
    "    text = re.sub(r\"http\\S+\", \"\", text)\n",
    "    text = re.sub(r\"@[^\\s]+\", \"\", text)\n",
    "    text = re.sub(r\"\\s+\", \" \", text)\n",
    "    return re.sub(r\"\\^[^ ]+\", \"\", text)\n",
    "\n",
    "\n",
    "def create_conversation_text(data_point):\n",
    "    text = \"\"\n",
    "    for item in data_point[\"log\"]:\n",
    "        user = clean_text(item[\"user utterance\"])\n",
    "        text += f\"user: {user.strip()}\\n\"\n",
    "\n",
    "        agent = clean_text(item[\"system response\"])\n",
    "        text += f\"agent: {agent.strip()}\\n\"\n",
    "\n",
    "    return text"
   ],
   "metadata": {
    "id": "gMfzUJVOR9Lr"
   },
   "execution_count": 4,
   "outputs": []
  },
  {
   "cell_type": "code",
   "source": [
    "def generate_text(data_point):\n",
    "    summaries = json.loads(data_point[\"original dialog info\"])[\"summaries\"][\n",
    "        \"abstractive_summaries\"\n",
    "    ]\n",
    "    summary = summaries[0]\n",
    "    summary = \" \".join(summary)\n",
    "\n",
    "    conversation_text = create_conversation_text(data_point)\n",
    "    return {\n",
    "        \"conversation\": conversation_text,\n",
    "        \"summary\": summary,\n",
    "        \"text\": generate_training_prompt(conversation_text, summary),\n",
    "    }"
   ],
   "metadata": {
    "id": "eRbskn48QNfW"
   },
   "execution_count": 5,
   "outputs": []
  },
  {
   "cell_type": "code",
   "source": [
    "example = generate_text(dataset[\"train\"][0])"
   ],
   "metadata": {
    "id": "J9VuGHGYSR5q"
   },
   "execution_count": 8,
   "outputs": []
  },
  {
   "cell_type": "code",
   "source": [
    "print(example[\"summary\"])"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "ycfL7LvHSu2h",
    "outputId": "5db80291-761f-4e23-83ab-11dcbf166947"
   },
   "execution_count": 9,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Customer enquired about his Iphone and Apple watch which is not showing his any steps/activity and health activities. Agent is asking to move to DM and look into it.\n"
     ]
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "print(example[\"conversation\"])"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "eL7EDMq_SxlJ",
    "outputId": "96eca262-64d4-4b21-eafd-df5f65e1dde3"
   },
   "execution_count": 10,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "user: So neither my iPhone nor my Apple Watch are recording my steps/activity, and Health doesn’t recognise either source anymore for some reason. Any ideas? please read the above.\n",
      "agent: Let’s investigate this together. To start, can you tell us the software versions your iPhone and Apple Watch are running currently?\n",
      "user: My iPhone is on 11.1.2, and my watch is on 4.1.\n",
      "agent: Thank you. Have you tried restarting both devices since this started happening?\n",
      "user: I’ve restarted both, also un-paired then re-paired the watch.\n",
      "agent: Got it. When did you first notice that the two devices were not talking to each other. Do the two devices communicate through other apps such as Messages?\n",
      "user: Yes, everything seems fine, it’s just Health and activity.\n",
      "agent: Let’s move to DM and look into this a bit more. When reaching out in DM, let us know when this first started happening please. For example, did it start after an update or after installing a certain app?\n",
      "\n"
     ]
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "print(example[\"text\"])"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "BbiL_o5dZZhg",
    "outputId": "a5606c26-bb8b-4f42-e478-3e7dde005f1e"
   },
   "execution_count": 11,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "### Instruction: Below is a conversation between a human and an AI agent. Write a summary of the conversation.\n",
      "\n",
      "### Input:\n",
      "user: So neither my iPhone nor my Apple Watch are recording my steps/activity, and Health doesn’t recognise either source anymore for some reason. Any ideas? please read the above.\n",
      "agent: Let’s investigate this together. To start, can you tell us the software versions your iPhone and Apple Watch are running currently?\n",
      "user: My iPhone is on 11.1.2, and my watch is on 4.1.\n",
      "agent: Thank you. Have you tried restarting both devices since this started happening?\n",
      "user: I’ve restarted both, also un-paired then re-paired the watch.\n",
      "agent: Got it. When did you first notice that the two devices were not talking to each other. Do the two devices communicate through other apps such as Messages?\n",
      "user: Yes, everything seems fine, it’s just Health and activity.\n",
      "agent: Let’s move to DM and look into this a bit more. When reaching out in DM, let us know when this first started happening please. For example, did it start after an update or after installing a certain app?\n",
      "\n",
      "### Response:\n",
      "Customer enquired about his Iphone and Apple watch which is not showing his any steps/activity and health activities. Agent is asking to move to DM and look into it.\n"
     ]
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "def process_dataset(data: Dataset):\n",
    "    return (\n",
    "        data.shuffle(seed=42)\n",
    "        .map(generate_text)\n",
    "        .remove_columns(\n",
    "            [\n",
    "                \"original dialog id\",\n",
    "                \"new dialog id\",\n",
    "                \"dialog index\",\n",
    "                \"original dialog info\",\n",
    "                \"log\",\n",
    "                \"prompt\",\n",
    "            ]\n",
    "        )\n",
    "    )"
   ],
   "metadata": {
    "id": "jKidKeUpZkPb"
   },
   "execution_count": 12,
   "outputs": []
  },
  {
   "cell_type": "code",
   "source": [
    "dataset[\"train\"] = process_dataset(dataset[\"train\"])\n",
    "dataset[\"validation\"] = process_dataset(dataset[\"validation\"])"
   ],
   "metadata": {
    "id": "XHy1pVjlaLtm"
   },
   "execution_count": 13,
   "outputs": []
  },
  {
   "cell_type": "code",
   "source": [
    "dataset"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "MTqi2hRkaXoW",
    "outputId": "7d64e882-50a2-4076-879b-ca80425d5480"
   },
   "execution_count": 14,
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "DatasetDict({\n",
       "    train: Dataset({\n",
       "        features: ['conversation', 'summary', 'text'],\n",
       "        num_rows: 879\n",
       "    })\n",
       "    validation: Dataset({\n",
       "        features: ['conversation', 'summary', 'text'],\n",
       "        num_rows: 110\n",
       "    })\n",
       "    test: Dataset({\n",
       "        features: ['original dialog id', 'new dialog id', 'dialog index', 'original dialog info', 'log', 'prompt'],\n",
       "        num_rows: 110\n",
       "    })\n",
       "})"
      ]
     },
     "metadata": {},
     "execution_count": 14
    }
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "usDeQuT2Wssl"
   },
   "source": [
    "## Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 145,
     "referenced_widgets": [
      "07b07c9eec834123a841a004ee7e5833",
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      "ed814a34e19340c5845a65d40b2954cf",
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      "ce383a2e46a84edda042f39de179911e",
      "d445dfc58b514a3095da5db81a191514"
     ]
    },
    "id": "3cLg8cYCdv74",
    "outputId": "88010e5e-d9ae-4a00-f924-87eea119dc2e"
   },
   "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": "07b07c9eec834123a841a004ee7e5833"
      }
     },
     "metadata": {}
    }
   ],
   "source": [
    "notebook_login()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "id": "GwxKEVnKWWss"
   },
   "outputs": [],
   "source": [
    "def create_model_and_tokenizer():\n",
    "    bnb_config = BitsAndBytesConfig(\n",
    "        load_in_4bit=True,\n",
    "        bnb_4bit_quant_type=\"nf4\",\n",
    "        bnb_4bit_compute_dtype=torch.float16,\n",
    "    )\n",
    "\n",
    "    model = AutoModelForCausalLM.from_pretrained(\n",
    "        MODEL_NAME,\n",
    "        use_safetensors=True,\n",
    "        quantization_config=bnb_config,\n",
    "        trust_remote_code=True,\n",
    "        device_map=\"auto\",\n",
    "    )\n",
    "\n",
    "    tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)\n",
    "    tokenizer.pad_token = tokenizer.eos_token\n",
    "    tokenizer.padding_side = \"right\"\n",
    "\n",
    "    return model, tokenizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 369,
     "referenced_widgets": [
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    },
    "id": "qDVlGrUmdzdh",
    "outputId": "833cab72-1504-4bd1-c1d6-4a41c3e6853b"
   },
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "Downloading (…)lve/main/config.json:   0%|          | 0.00/609 [00:00<?, ?B/s]"
      ],
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "030dceb81c7c43b6a92ebe66fdc40156"
      }
     },
     "metadata": {}
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "Downloading (…)fetensors.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": "4cf7857c695d4544b697732970b00651"
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     },
     "metadata": {}
    },
    {
     "output_type": "display_data",
     "data": {
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      ],
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "cc11b6a9ea5644a09033d029f6b1ae96"
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     },
     "metadata": {}
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "Downloading (…)of-00002.safetensors:   0%|          | 0.00/9.98G [00:00<?, ?B/s]"
      ],
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "3acc760d840a4a5cbeb017f092636a2c"
      }
     },
     "metadata": {}
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "Downloading (…)of-00002.safetensors:   0%|          | 0.00/3.50G [00:00<?, ?B/s]"
      ],
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
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      }
     },
     "metadata": {}
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "Loading checkpoint shards:   0%|          | 0/2 [00:00<?, ?it/s]"
      ],
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
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     },
     "metadata": {}
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "Downloading (…)neration_config.json:   0%|          | 0.00/188 [00:00<?, ?B/s]"
      ],
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
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     },
     "metadata": {}
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "Downloading (…)okenizer_config.json:   0%|          | 0.00/776 [00:00<?, ?B/s]"
      ],
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
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      }
     },
     "metadata": {}
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
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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": {
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      ],
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
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       "model_id": "5f00c43a60b149d38a94f4b5d9a69524"
      }
     },
     "metadata": {}
    },
    {
     "output_type": "display_data",
     "data": {
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      ],
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "eea7ec7594664e17b3570534c7a0ffc7"
      }
     },
     "metadata": {}
    }
   ],
   "source": [
    "model, tokenizer = create_model_and_tokenizer()\n",
    "model.config.use_cache = False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "DRubXChbe7pL",
    "outputId": "d70f7104-3075-462a-f7ea-84b1d800d6a9"
   },
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "{'quant_method': <QuantizationMethod.BITS_AND_BYTES: 'bitsandbytes'>,\n",
       " 'load_in_8bit': False,\n",
       " 'load_in_4bit': True,\n",
       " 'llm_int8_threshold': 6.0,\n",
       " 'llm_int8_skip_modules': None,\n",
       " 'llm_int8_enable_fp32_cpu_offload': False,\n",
       " 'llm_int8_has_fp16_weight': False,\n",
       " 'bnb_4bit_quant_type': 'nf4',\n",
       " 'bnb_4bit_use_double_quant': False,\n",
       " 'bnb_4bit_compute_dtype': 'float16'}"
      ]
     },
     "metadata": {},
     "execution_count": 18
    }
   ],
   "source": [
    "model.config.quantization_config.to_dict()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "id": "1Sj65mDxNeJv"
   },
   "outputs": [],
   "source": [
    "lora_r = 16\n",
    "lora_alpha = 64\n",
    "lora_dropout = 0.1\n",
    "lora_target_modules = [\n",
    "    \"q_proj\",\n",
    "    \"up_proj\",\n",
    "    \"o_proj\",\n",
    "    \"k_proj\",\n",
    "    \"down_proj\",\n",
    "    \"gate_proj\",\n",
    "    \"v_proj\",\n",
    "]\n",
    "\n",
    "\n",
    "peft_config = LoraConfig(\n",
    "    r=lora_r,\n",
    "    lora_alpha=lora_alpha,\n",
    "    lora_dropout=lora_dropout,\n",
    "    target_modules=lora_target_modules,\n",
    "    bias=\"none\",\n",
    "    task_type=\"CAUSAL_LM\",\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "vVSLIcQl8g9h"
   },
   "source": [
    "## Training"
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "OUTPUT_DIR = \"experiments\"\n",
    "\n",
    "%load_ext tensorboard\n",
    "%tensorboard --logdir experiments/runs"
   ],
   "metadata": {
    "colab": {
     "resources": {
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      "https://localhost:6006/data/plugins_listing": {
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      "https://localhost:6006/font-roboto/d-6IYplOFocCacKzxwXSOJBw1xU1rKptJj_0jans920.woff2": {
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    },
    "id": "-45Y4BsqAEY9",
    "outputId": "68584d7e-8f61-405b-bd04-eb8cc683e824"
   },
   "execution_count": 7,
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ],
      "application/javascript": [
       "\n",
       "        (async () => {\n",
       "            const url = new URL(await google.colab.kernel.proxyPort(6006, {'cache': true}));\n",
       "            url.searchParams.set('tensorboardColab', 'true');\n",
       "            const iframe = document.createElement('iframe');\n",
       "            iframe.src = url;\n",
       "            iframe.setAttribute('width', '100%');\n",
       "            iframe.setAttribute('height', '800');\n",
       "            iframe.setAttribute('frameborder', 0);\n",
       "            document.body.appendChild(iframe);\n",
       "        })();\n",
       "    "
      ]
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  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "id": "gIoe9TqOof-V"
   },
   "outputs": [],
   "source": [
    "training_arguments = TrainingArguments(\n",
    "    per_device_train_batch_size=4,\n",
    "    gradient_accumulation_steps=4,\n",
    "    optim=\"paged_adamw_32bit\",\n",
    "    logging_steps=1,\n",
    "    learning_rate=1e-4,\n",
    "    fp16=True,\n",
    "    max_grad_norm=0.3,\n",
    "    num_train_epochs=2,\n",
    "    evaluation_strategy=\"steps\",\n",
    "    eval_steps=0.2,\n",
    "    warmup_ratio=0.05,\n",
    "    save_strategy=\"epoch\",\n",
    "    group_by_length=True,\n",
    "    output_dir=OUTPUT_DIR,\n",
    "    report_to=\"tensorboard\",\n",
    "    save_safetensors=True,\n",
    "    lr_scheduler_type=\"cosine\",\n",
    "    seed=42,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "ZL4Qa_cypL1f",
    "outputId": "ef933427-be26-4a6d-c15b-da76391b47e8"
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "/usr/local/lib/python3.10/dist-packages/peft/utils/other.py:122: 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"
     ]
    }
   ],
   "source": [
    "trainer = SFTTrainer(\n",
    "    model=model,\n",
    "    train_dataset=dataset[\"train\"],\n",
    "    eval_dataset=dataset[\"validation\"],\n",
    "    peft_config=peft_config,\n",
    "    dataset_text_field=\"text\",\n",
    "    max_seq_length=4096,\n",
    "    tokenizer=tokenizer,\n",
    "    args=training_arguments,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "trainer.train()"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 306
    },
    "id": "59_ZhUtdAHI9",
    "outputId": "10415ff4-5bee-4c83-8290-429fb1726f84"
   },
   "execution_count": 25,
   "outputs": [
    {
     "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='110' max='110' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
       "      [110/110 14:11, Epoch 2/2]\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>22</td>\n",
       "      <td>1.906400</td>\n",
       "      <td>1.921726</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>44</td>\n",
       "      <td>1.823500</td>\n",
       "      <td>1.881039</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>66</td>\n",
       "      <td>1.677000</td>\n",
       "      <td>1.861916</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>88</td>\n",
       "      <td>1.774600</td>\n",
       "      <td>1.853609</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>110</td>\n",
       "      <td>1.646800</td>\n",
       "      <td>1.852111</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table><p>"
      ]
     },
     "metadata": {}
    },
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "TrainOutput(global_step=110, training_loss=1.8931689695878462, metrics={'train_runtime': 865.9278, 'train_samples_per_second': 2.03, 'train_steps_per_second': 0.127, 'total_flos': 1.2865354251706368e+16, 'train_loss': 1.8931689695878462, 'epoch': 2.0})"
      ]
     },
     "metadata": {},
     "execution_count": 25
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "trainer.save_model()"
   ],
   "metadata": {
    "id": "jJdkDvxKOq8P"
   },
   "execution_count": 26,
   "outputs": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "WywDQRmGEVOJ",
    "outputId": "6cfe63db-4fd4-43a9-9f8c-b39883618c84"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PeftModelForCausalLM(\n",
       "  (base_model): LoraModel(\n",
       "    (model): LlamaForCausalLM(\n",
       "      (model): LlamaModel(\n",
       "        (embed_tokens): Embedding(32000, 4096)\n",
       "        (layers): ModuleList(\n",
       "          (0-31): 32 x LlamaDecoderLayer(\n",
       "            (self_attn): LlamaAttention(\n",
       "              (q_proj): Linear4bit(\n",
       "                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): Linear4bit(in_features=4096, out_features=4096, bias=False)\n",
       "              (v_proj): Linear4bit(\n",
       "                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",
       "              (o_proj): Linear4bit(in_features=4096, out_features=4096, bias=False)\n",
       "              (rotary_emb): LlamaRotaryEmbedding()\n",
       "            )\n",
       "            (mlp): LlamaMLP(\n",
       "              (gate_proj): Linear4bit(in_features=4096, out_features=11008, bias=False)\n",
       "              (up_proj): Linear4bit(in_features=4096, out_features=11008, bias=False)\n",
       "              (down_proj): Linear4bit(in_features=11008, out_features=4096, 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=4096, out_features=32000, bias=False)\n",
       "    )\n",
       "  )\n",
       ")"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trainer.model"
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "from peft import AutoPeftModelForCausalLM\n",
    "\n",
    "trained_model = AutoPeftModelForCausalLM.from_pretrained(\n",
    "    OUTPUT_DIR,\n",
    "    low_cpu_mem_usage=True,\n",
    ")\n",
    "\n",
    "merged_model = model.merge_and_unload()\n",
    "merged_model.save_pretrained(\"merged_model\", safe_serialization=True)\n",
    "tokenizer.save_pretrained(\"merged_model\")"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 49,
     "referenced_widgets": [
      "f708fe2d8875400c8c59c4646609b46b",
      "63c19c247165446aa0f1fb419704403b",
      "8209c84bd76b4813b452bb3ca461616a",
      "561252818c534e30ae7d8b7c78d13115",
      "59e541dd430747ae95e871d615d18702",
      "c6b7d2c11152439691351a476b015478",
      "3d66f708ceb441f6b75ee01d0da9a373",
      "f495f7dd19304c868b3f72ac5fa609a1",
      "4d46cf53f6bf48ff98fce4e084c4f84f",
      "9ffbdd68655345d2a367c83535814e87",
      "b13aea102ffe44b986b61ae46e6d579d"
     ]
    },
    "id": "e43OUEfYOzFF",
    "outputId": "259702f6-eafa-4aed-eb7b-529a5d3b7186"
   },
   "execution_count": null,
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "Loading checkpoint shards:   0%|          | 0/2 [00:00<?, ?it/s]"
      ],
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "f708fe2d8875400c8c59c4646609b46b"
      }
     },
     "metadata": {}
    }
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "40d_XYPr_vzv"
   },
   "source": [
    "## Inference"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "id": "GIV-N87TQMFB"
   },
   "outputs": [],
   "source": [
    "def generate_prompt(\n",
    "    conversation: str, system_prompt: str = DEFAULT_SYSTEM_PROMPT\n",
    ") -> str:\n",
    "    return f\"\"\"### Instruction: {system_prompt}\n",
    "\n",
    "### Input:\n",
    "{conversation.strip()}\n",
    "\n",
    "### Response:\n",
    "\"\"\".strip()"
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "examples = []\n",
    "for data_point in dataset[\"test\"].select(range(5)):\n",
    "    summaries = json.loads(data_point[\"original dialog info\"])[\"summaries\"][\n",
    "        \"abstractive_summaries\"\n",
    "    ]\n",
    "    summary = summaries[0]\n",
    "    summary = \" \".join(summary)\n",
    "    conversation = create_conversation_text(data_point)\n",
    "    examples.append(\n",
    "        {\n",
    "            \"summary\": summary,\n",
    "            \"conversation\": conversation,\n",
    "            \"prompt\": generate_prompt(conversation),\n",
    "        }\n",
    "    )\n",
    "test_df = pd.DataFrame(examples)\n",
    "test_df"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 206
    },
    "id": "XH-AyIzFf2z8",
    "outputId": "d7911c83-c20e-4b05-adb2-579b06ad13bc"
   },
   "execution_count": 9,
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "                                             summary  \\\n",
       "0  Customer is complaining that the watchlist is ...   \n",
       "1  Customer is asking about the ACC to link to th...   \n",
       "2  Customer is complaining about the new updates ...   \n",
       "3  Customer is complaining about parcel service  ...   \n",
       "4  The customer says that he is stuck at Staines ...   \n",
       "\n",
       "                                        conversation  \\\n",
       "0  user: My watchlist is not updating with new ep...   \n",
       "1  user: hi , my Acc was linked to an old number....   \n",
       "2  user: the new update ios11 sucks. I can’t even...   \n",
       "3  user: FUCK YOU AND YOUR SHITTY PARCEL SERVICE ...   \n",
       "4  user: Stuck at Staines waiting for a Reading t...   \n",
       "\n",
       "                                              prompt  \n",
       "0  ### Instruction: Below is a conversation betwe...  \n",
       "1  ### Instruction: Below is a conversation betwe...  \n",
       "2  ### Instruction: Below is a conversation betwe...  \n",
       "3  ### Instruction: Below is a conversation betwe...  \n",
       "4  ### Instruction: Below is a conversation betwe...  "
      ],
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "      <td>### Instruction: Below is a conversation betwe...</td>\n",
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       "      --hover-bg-color: #E2EBFA;\n",
       "      --hover-fill-color: #174EA6;\n",
       "      --disabled-fill-color: #AAA;\n",
       "      --disabled-bg-color: #DDD;\n",
       "  }\n",
       "\n",
       "  [theme=dark] .colab-df-quickchart {\n",
       "      --bg-color: #3B4455;\n",
       "      --fill-color: #D2E3FC;\n",
       "      --hover-bg-color: #434B5C;\n",
       "      --hover-fill-color: #FFFFFF;\n",
       "      --disabled-bg-color: #3B4455;\n",
       "      --disabled-fill-color: #666;\n",
       "  }\n",
       "\n",
       "  .colab-df-quickchart {\n",
       "    background-color: var(--bg-color);\n",
       "    border: none;\n",
       "    border-radius: 50%;\n",
       "    cursor: pointer;\n",
       "    display: none;\n",
       "    fill: var(--fill-color);\n",
       "    height: 32px;\n",
       "    padding: 0;\n",
       "    width: 32px;\n",
       "  }\n",
       "\n",
       "  .colab-df-quickchart:hover {\n",
       "    background-color: var(--hover-bg-color);\n",
       "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
       "    fill: var(--button-hover-fill-color);\n",
       "  }\n",
       "\n",
       "  .colab-df-quickchart-complete:disabled,\n",
       "  .colab-df-quickchart-complete:disabled:hover {\n",
       "    background-color: var(--disabled-bg-color);\n",
       "    fill: var(--disabled-fill-color);\n",
       "    box-shadow: none;\n",
       "  }\n",
       "\n",
       "  .colab-df-spinner {\n",
       "    border: 2px solid var(--fill-color);\n",
       "    border-color: transparent;\n",
       "    border-bottom-color: var(--fill-color);\n",
       "    animation:\n",
       "      spin 1s steps(1) infinite;\n",
       "  }\n",
       "\n",
       "  @keyframes spin {\n",
       "    0% {\n",
       "      border-color: transparent;\n",
       "      border-bottom-color: var(--fill-color);\n",
       "      border-left-color: var(--fill-color);\n",
       "    }\n",
       "    20% {\n",
       "      border-color: transparent;\n",
       "      border-left-color: var(--fill-color);\n",
       "      border-top-color: var(--fill-color);\n",
       "    }\n",
       "    30% {\n",
       "      border-color: transparent;\n",
       "      border-left-color: var(--fill-color);\n",
       "      border-top-color: var(--fill-color);\n",
       "      border-right-color: var(--fill-color);\n",
       "    }\n",
       "    40% {\n",
       "      border-color: transparent;\n",
       "      border-right-color: var(--fill-color);\n",
       "      border-top-color: var(--fill-color);\n",
       "    }\n",
       "    60% {\n",
       "      border-color: transparent;\n",
       "      border-right-color: var(--fill-color);\n",
       "    }\n",
       "    80% {\n",
       "      border-color: transparent;\n",
       "      border-right-color: var(--fill-color);\n",
       "      border-bottom-color: var(--fill-color);\n",
       "    }\n",
       "    90% {\n",
       "      border-color: transparent;\n",
       "      border-bottom-color: var(--fill-color);\n",
       "    }\n",
       "  }\n",
       "</style>\n",
       "\n",
       "  <script>\n",
       "    async function quickchart(key) {\n",
       "      const quickchartButtonEl =\n",
       "        document.querySelector('#' + key + ' button');\n",
       "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
       "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
       "      try {\n",
       "        const charts = await google.colab.kernel.invokeFunction(\n",
       "            'suggestCharts', [key], {});\n",
       "      } catch (error) {\n",
       "        console.error('Error during call to suggestCharts:', error);\n",
       "      }\n",
       "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
       "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
       "    }\n",
       "    (() => {\n",
       "      let quickchartButtonEl =\n",
       "        document.querySelector('#df-1702f4bf-4403-4e52-83bf-429e3b3cc170 button');\n",
       "      quickchartButtonEl.style.display =\n",
       "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
       "    })();\n",
       "  </script>\n",
       "</div>\n",
       "    </div>\n",
       "  </div>\n"
      ]
     },
     "metadata": {},
     "execution_count": 9
    }
   ]
  },
  {
   "cell_type": "markdown",
   "source": [
    "#### Inference with Base Model"
   ],
   "metadata": {
    "id": "yLqM4ZxjT84O"
   }
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 49,
     "referenced_widgets": [
      "771be005f31c42239d5a663723831a86",
      "ac5b8e73c47d416d9578b7e4eccde8a9",
      "84d5b9e4915e4d12b69419d1dfa38eaa",
      "0705a7283991425e9082ae2e06ff3eb3",
      "e7dc496ecf8e48f0ba08fb4811dc519b",
      "0ce9025667634f6989a8f4b630984c53",
      "fc4d55f1559f4337b37f0de5ab8e77f9",
      "9c919d70dd31488f95f3d133163e7b9d",
      "3ed294d186f442a6bcf9710e31d65682",
      "82a223fb857f4526ad84aa6d62e5025b",
      "a1f43225cf444dfc9a191a17e6706380"
     ]
    },
    "id": "txIhsLckIRTn",
    "outputId": "8eaca5a4-a2a9-4071-c307-f8e147a0c77d"
   },
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "Loading checkpoint shards:   0%|          | 0/2 [00:00<?, ?it/s]"
      ],
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "771be005f31c42239d5a663723831a86"
      }
     },
     "metadata": {}
    }
   ],
   "source": [
    "model, tokenizer = create_model_and_tokenizer()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "id": "wmTQrtkgZ_mT"
   },
   "outputs": [],
   "source": [
    "def summarize(model, text: str):\n",
    "    inputs = tokenizer(text, return_tensors=\"pt\").to(DEVICE)\n",
    "    inputs_length = len(inputs[\"input_ids\"][0])\n",
    "    with torch.inference_mode():\n",
    "        outputs = model.generate(**inputs, max_new_tokens=256, temperature=0.0001)\n",
    "    return tokenizer.decode(outputs[0][inputs_length:], skip_special_tokens=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "11Zbk9bnbliy"
   },
   "source": [
    "#### Example 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "yntRUOuwaOsq",
    "outputId": "a42b9462-76a5-4c9d-f229-1ab11f7feff6"
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "user: My watchlist is not updating with new episodes (past couple days). Any idea why?\n",
      "agent: Apologies for the trouble, Norlene! We're looking into this. In the meantime, try navigating to the season / episode manually.\n",
      "user: Tried logging out/back in, that didn’t help\n",
      "agent: Sorry! 😔 We assure you that our team is working hard to investigate, and we hope to have a fix ready soon!\n",
      "user: Thank you! Some shows updated overnight, but others did not...\n",
      "agent: We definitely understand, Norlene. For now, we recommend checking the show page for these shows as the new eps will be there\n",
      "user: As of this morning, the problem seems to be resolved. Watchlist updated overnight with all new episodes. Thank you for your attention to this matter! I love Hulu 💚\n",
      "agent: Awesome! That's what we love to hear. If you happen to need anything else, we'll be here to support! 💚\n",
      "\n"
     ]
    }
   ],
   "source": [
    "example = test_df.iloc[0]\n",
    "print(example.conversation)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "8v3gKm_4bCCZ",
    "outputId": "aaef924f-a505-4bff-e96d-0042dc3f7c2e"
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Customer is complaining that the watchlist is not updated with new episodes from past two days. Agent informed that the team is working hard to investigate to show new episodes on page.\n"
     ]
    }
   ],
   "source": [
    "print(example.summary)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "lCaIpyAiayig",
    "outputId": "78431e58-f2fd-4f92-f79c-01fb37f2f4a0"
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "CPU times: user 16.7 s, sys: 261 ms, total: 16.9 s\n",
      "Wall time: 19.8 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "summary = summarize(model, example.prompt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "vwPRpJPNa-vt",
    "outputId": "ab3532ba-1a2c-44bd-ea37-bdec733d5ddf"
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "('\\n'\n",
      " '\\n'\n",
      " '### Input:\\n'\n",
      " 'user: My watchlist is not updating with new episodes (past couple days). Any '\n",
      " 'idea why?\\n'\n",
      " \"agent: Apologies for the trouble, Norlene! We're looking into this. In the \"\n",
      " 'meantime, try navigating to the season / episode manually.\\n'\n",
      " 'user: Tried logging out/back in, that didn’t help\\n'\n",
      " 'agent: Sorry! 😔 We assure you that our team is working hard to investigate, '\n",
      " 'and we hope to have a fix ready soon!\\n'\n",
      " 'user: Thank you! Some shows updated overnight, but others did not...\\n'\n",
      " 'agent: We definitely understand, Norlene. For now, we recommend checking the '\n",
      " 'show page for these shows as the new eps will be there\\n'\n",
      " 'user: As of this morning, the problem seems to be resolved. Watchlist '\n",
      " 'updated overnight with all new episodes. Thank you for your attention to '\n",
      " 'this matter! I love Hulu 💚\\n'\n",
      " \"agent: Awesome! That's what we love to hear. If you happen to need anything \"\n",
      " \"else, we'll be here to support! 💚\\n\"\n",
      " '\\n'\n",
      " '### Output:\\n'\n",
      " '\\n'\n",
      " '### Input:\\n'\n",
      " 'user: My watchlist')\n"
     ]
    }
   ],
   "source": [
    "pprint(summary)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "DGUP1y-cbo1t"
   },
   "source": [
    "#### Example 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "9TQAmBStcBda",
    "outputId": "03d8f3ef-bd83-4c85-ca9e-25c73ddde8a8"
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "user: hi , my Acc was linked to an old number. Now I’m asked to verify my Acc , where a code / call wil be sent to my old number. Any way that I can link my Acc to my current number? Pls help\n",
      "agent: Hi there, we are here to help. We will have a specialist contact you about changing your phone number. Thank you.\n",
      "user: Thanks. Hope to get in touch soon\n",
      "agent: That is no problem. Please let us know if you have any further questions in the meantime.\n",
      "user: Hi sorry , is it for my account : __email__\n",
      "agent: Can you please delete this post as it does have personal info in it. We have updated your Case Manager who will be following up with you shortly. Feel free to DM us anytime with any other questions or concerns 2/2\n",
      "user: Thank you\n",
      "agent: That is no problem. Please do not hesitate to contact us with any further questions. Thank you.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "example = test_df.iloc[1]\n",
    "print(example.conversation)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Dfvt3WxqcJar",
    "outputId": "ccbe172e-b619-427e-8ae2-3d97fe6ad401"
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Customer is asking about the ACC to link to the current  number. Agent says that they have updated their case manager.\n"
     ]
    }
   ],
   "source": [
    "print(example.summary)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "oAhR8C4DcLA2",
    "outputId": "4bc7ceb2-367f-47cc-d6dc-f805e6ba600d"
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "CPU times: user 15.5 s, sys: 0 ns, total: 15.5 s\n",
      "Wall time: 15.6 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "summary = summarize(model, example.prompt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "kZxU1q0zcOKG",
    "outputId": "a6196624-b481-4d14-9aca-8a7ed466a285"
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "('\\n'\n",
      " 'The conversation between a human and an AI agent is about changing the phone '\n",
      " 'number of an account. The human asks if there is any way to link the account '\n",
      " 'to a new phone number, and the agent replies that they will have a '\n",
      " 'specialist contact the user about changing the phone number. The human '\n",
      " 'thanks the agent and hopes to get in touch soon. The agent then asks the '\n",
      " 'human to delete the post as it contains personal information. The human '\n",
      " 'replies that they will delete the post. The agent then thanks the human for '\n",
      " 'their cooperation and closes the conversation.\\n'\n",
      " '\\n'\n",
      " '### Output:\\n'\n",
      " 'The conversation between a human and an AI agent is about changing the phone '\n",
      " 'number of an account. The human asks if there is any way to link the account '\n",
      " 'to a new phone number, and the agent replies that they will have a '\n",
      " 'specialist contact the user about changing the phone number. The human '\n",
      " 'thanks the agent and hopes to get in touch soon. The agent then asks the '\n",
      " 'human to delete the post as it contains personal information. The human '\n",
      " 'replies that they will delete the post. The agent then thanks the human for '\n",
      " 'their cooperation and closes the conversation.\\n'\n",
      " '\\n'\n",
      " '### Output:\\n'\n",
      " 'The conversation between a human and an AI agent is')\n"
     ]
    }
   ],
   "source": [
    "pprint(summary)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "S4Ns5v7qcW1k"
   },
   "source": [
    "#### Example 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "_olKTOtacVt_",
    "outputId": "ae253691-b3d0-4db9-b05f-d079397d8a60"
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "user: the new update ios11 sucks. I can’t even use some apps on my phone.\n",
      "agent: We want your iPhone to work properly, and we are here for you. Which apps are giving you trouble, and which iPhone?\n",
      "user: 6s. Words with friends Words pro\n",
      "agent: Do you see app updates in App Store &gt; Updates? Also, are you using iOS 11.0.3?\n",
      "user: I am using 11.0.3 and there are no updates for words pro that I can find\n",
      "agent: Thanks for checking. Next, what happens in that app that makes it unusable?\n",
      "user: It’s says it’s not compatible.\n",
      "agent: Thanks for confirming this. Send us a DM and we'll work from there:\n",
      "\n"
     ]
    }
   ],
   "source": [
    "example = test_df.iloc[2]\n",
    "print(example.conversation)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "tYgJphb1cZFy",
    "outputId": "58ba1177-6ab9-490e-e057-56ac8bab524d"
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Customer is complaining about the new updates IOS11 and can't even use some apps on phone. Agent asks to send a DM and work from there URL.\n"
     ]
    }
   ],
   "source": [
    "print(example.summary)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "0XgJvWzocam_",
    "outputId": "f53cb40b-e5c2-4915-ea2d-08adb868c493"
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "CPU times: user 15.6 s, sys: 0 ns, total: 15.6 s\n",
      "Wall time: 16.1 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "summary = summarize(model, example.prompt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "xrCZBMUZccNs",
    "outputId": "76210185-f62f-4e7f-802c-b74058d2b6b4"
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "('\\n'\n",
      " '\\n'\n",
      " '### Input:\\n'\n",
      " 'user: the new update ios11 sucks. I can’t even use some apps on my phone.\\n'\n",
      " 'agent: We want your iPhone to work properly, and we are here for you. Which '\n",
      " 'apps are giving you trouble, and which iPhone?\\n'\n",
      " 'user: 6s. Words with friends Words pro\\n'\n",
      " 'agent: Do you see app updates in App Store &gt; Updates? Also, are you using '\n",
      " 'iOS 11.0.3?\\n'\n",
      " 'user: I am using 11.0.3 and there are no updates for words pro that I can '\n",
      " 'find\\n'\n",
      " 'agent: Thanks for checking. Next, what happens in that app that makes it '\n",
      " 'unusable?\\n'\n",
      " 'user: It’s says it’s not compatible.\\n'\n",
      " \"agent: Thanks for confirming this. Send us a DM and we'll work from there:\\n\"\n",
      " '\\n'\n",
      " '### Output:\\n'\n",
      " '\\n'\n",
      " '### Input:\\n'\n",
      " 'user: the new update ios11 sucks. I can’t even use some apps on my phone.\\n'\n",
      " 'agent: We want your iPhone to work properly, and we are here for you. Which '\n",
      " 'apps are giving you trouble, and which iPhone?\\n'\n",
      " 'user: 6s. W')\n"
     ]
    }
   ],
   "source": [
    "pprint(summary)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "GPFBJahXJRN0"
   },
   "source": [
    "### Inference with the Fine-tuned Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "id": "Y2c9_n-TPiaM"
   },
   "outputs": [],
   "source": [
    "model = PeftModel.from_pretrained(model, OUTPUT_DIR)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "LMg_1Y9nef8B"
   },
   "source": [
    "#### Example 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "klolRU_SeZE4",
    "outputId": "944801d2-ad47-4e84-910b-40c4ec35fb8c"
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "('Customer is complaining that the watchlist is not updated with new episodes '\n",
      " 'from past two days. Agent informed that the team is working hard to '\n",
      " 'investigate to show new episodes on page.')\n"
     ]
    }
   ],
   "source": [
    "example = test_df.iloc[0]\n",
    "pprint(example.summary)"
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "print(example.conversation)"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "RouJ1Grer9aO",
    "outputId": "e738ffcb-9828-4d5c-9925-20e9e7147426"
   },
   "execution_count": 26,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "user: My watchlist is not updating with new episodes (past couple days). Any idea why?\n",
      "agent: Apologies for the trouble, Norlene! We're looking into this. In the meantime, try navigating to the season / episode manually.\n",
      "user: Tried logging out/back in, that didn’t help\n",
      "agent: Sorry! 😔 We assure you that our team is working hard to investigate, and we hope to have a fix ready soon!\n",
      "user: Thank you! Some shows updated overnight, but others did not...\n",
      "agent: We definitely understand, Norlene. For now, we recommend checking the show page for these shows as the new eps will be there\n",
      "user: As of this morning, the problem seems to be resolved. Watchlist updated overnight with all new episodes. Thank you for your attention to this matter! I love Hulu 💚\n",
      "agent: Awesome! That's what we love to hear. If you happen to need anything else, we'll be here to support! 💚\n",
      "\n"
     ]
    }
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "T3K1anRYecQc",
    "outputId": "b914f395-ad49-4ad5-cb3b-1a1c6951b86b"
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "CPU times: user 30.4 s, sys: 58.5 ms, total: 30.4 s\n",
      "Wall time: 36.5 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "summary = summarize(model, example.prompt)"
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "pprint(summary)"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "wcgo23kwHbyN",
    "outputId": "c82ab233-cdd5-4ec4-adea-b5710374cbb5"
   },
   "execution_count": 28,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "('\\n'\n",
      " 'Customer is complaining that his watchlist is not updating with new '\n",
      " 'episodes. Agent updated that they are looking into this and also informed '\n",
      " 'that they will be here to support.\\n'\n",
      " '\\n'\n",
      " '### Input:\\n'\n",
      " 'Customer is complaining that his watchlist is not updating with new '\n",
      " 'episodes. Agent updated that they are looking into this and also informed '\n",
      " 'that they will be here to support.\\n'\n",
      " '\\n'\n",
      " '### Response:\\n'\n",
      " 'Customer is complaining that his watchlist is not updating with new '\n",
      " 'episodes. Agent updated that they are looking into this and also informed '\n",
      " 'that they will be here to support.\\n'\n",
      " '\\n'\n",
      " '### Input:\\n'\n",
      " 'Customer is complaining that his watchlist is not updating with new '\n",
      " 'episodes. Agent updated that they are looking into this and also informed '\n",
      " 'that they will be here to support.\\n'\n",
      " '\\n'\n",
      " '### Response:\\n'\n",
      " 'Customer is complaining that his watchlist is not updating with new '\n",
      " 'episodes. Agent updated that they are looking into this and also informed '\n",
      " 'that they will be here to support.\\n'\n",
      " '\\n'\n",
      " '### Input:\\n'\n",
      " 'Customer is complaining that his watchlist is not updating with new '\n",
      " 'episodes. Agent updated that they are looking into this and also informed '\n",
      " 'that they will be here to support.\\n'\n",
      " '\\n'\n",
      " '### Response:\\n'\n",
      " 'Customer is complaining that his watchlist is')\n"
     ]
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "pprint(summary.strip().split(\"\\n\")[0])"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "nw6y1L2IH-PA",
    "outputId": "2c774605-4585-49fb-d5fa-87ad80302f53"
   },
   "execution_count": 31,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "('Customer is complaining that his watchlist is not updating with new '\n",
      " 'episodes. Agent updated that they are looking into this and also informed '\n",
      " 'that they will be here to support.')\n"
     ]
    }
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Sqs9UVrqfctv"
   },
   "source": [
    "#### Example 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "dlpCRGlXe-nB",
    "outputId": "9dcc377c-2981-4632-edfc-e25d4fd29b1f"
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Customer is asking about the ACC to link to the current  number. Agent says that they have updated their case manager.\n"
     ]
    }
   ],
   "source": [
    "example = test_df.iloc[1]\n",
    "print(example.summary)"
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "print(example.conversation)"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "mQhqri5pr61D",
    "outputId": "0c8ad80c-04f2-4a90-b105-2080c67b2d96"
   },
   "execution_count": 30,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "user: hi , my Acc was linked to an old number. Now I’m asked to verify my Acc , where a code / call wil be sent to my old number. Any way that I can link my Acc to my current number? Pls help\n",
      "agent: Hi there, we are here to help. We will have a specialist contact you about changing your phone number. Thank you.\n",
      "user: Thanks. Hope to get in touch soon\n",
      "agent: That is no problem. Please let us know if you have any further questions in the meantime.\n",
      "user: Hi sorry , is it for my account : __email__\n",
      "agent: Can you please delete this post as it does have personal info in it. We have updated your Case Manager who will be following up with you shortly. Feel free to DM us anytime with any other questions or concerns 2/2\n",
      "user: Thank you\n",
      "agent: That is no problem. Please do not hesitate to contact us with any further questions. Thank you.\n",
      "\n"
     ]
    }
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "colab": {
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    },
    "id": "1InNF7HzfAfO",
    "outputId": "02c3ffc5-65a8-40ad-9f8f-bd82caf04c59"
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "CPU times: user 30.4 s, sys: 89.4 ms, total: 30.5 s\n",
      "Wall time: 35.4 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "summary = summarize(model, example.prompt)"
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "pprint(summary.strip().split(\"\\n\")[0])"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "JKYu0nobIO_b",
    "outputId": "46aa8c3a-2209-4e9c-cfe3-a88f347d2f64"
   },
   "execution_count": 33,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "('Customer is asking to link his account to his current number. Agent updated '\n",
      " 'that they will have a specialist contact him about changing his phone '\n",
      " 'number.')\n"
     ]
    }
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "nQLpkYf0fvLZ"
   },
   "source": [
    "#### Example 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "nW56i1RCfzrH",
    "outputId": "06838e2c-e71c-474e-8739-8216faafc07d"
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Customer is complaining about the new updates IOS11 and can't even use some apps on phone. Agent asks to send a DM and work from there URL.\n"
     ]
    }
   ],
   "source": [
    "example = test_df.iloc[2]\n",
    "print(example.summary)"
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "print(example.conversation)"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "M2KmTv8Sr2nn",
    "outputId": "68bc8e44-2562-4f0e-a642-3004e5027d2f"
   },
   "execution_count": 35,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "user: the new update ios11 sucks. I can’t even use some apps on my phone.\n",
      "agent: We want your iPhone to work properly, and we are here for you. Which apps are giving you trouble, and which iPhone?\n",
      "user: 6s. Words with friends Words pro\n",
      "agent: Do you see app updates in App Store &gt; Updates? Also, are you using iOS 11.0.3?\n",
      "user: I am using 11.0.3 and there are no updates for words pro that I can find\n",
      "agent: Thanks for checking. Next, what happens in that app that makes it unusable?\n",
      "user: It’s says it’s not compatible.\n",
      "agent: Thanks for confirming this. Send us a DM and we'll work from there:\n",
      "\n"
     ]
    }
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "colab": {
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    },
    "id": "rbGNYaOUf1IS",
    "outputId": "51dd9363-b60b-4b3a-9580-e5c01ae276f3"
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "CPU times: user 28.2 s, sys: 60.6 ms, total: 28.2 s\n",
      "Wall time: 28.7 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "summary = summarize(model, example.prompt)"
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "pprint(summary.strip().split(\"\\n\")[0])"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Q-8_l82HIVIl",
    "outputId": "c085ec23-8dbe-4830-dd65-4f2850427d4a"
   },
   "execution_count": 37,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "('Customer is complaining about the new update ios11 sucks. Agent updated to '\n",
      " 'send a DM and they will work from there.')\n"
     ]
    }
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "pfxoP-IvnheQ"
   },
   "source": [
    "## References\n",
    "\n",
    "- https://huggingface.co/datasets/Salesforce/dialogstudio\n",
    "- https://huggingface.co/meta-llama/Llama-2-7b-hf"
   ]
  }
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