llama2-finetuning-qlora-summarization.ipynb

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    "# install the required libraries\n",
    "\n",
    "! pip install accelerate==0.21.0 peft==0.4.0 bitsandbytes==0.40.2 transformers==4.30.2 trl==0.4.7 --quiet"
   ]
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   "source": [
    "# ignoring the warnings\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "import os\n",
    "os.environ[\"WANDB_DISABLED\"] = \"true\""
   ]
  },
  {
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   "source": [
    "### Dataset preparation"
   ]
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     "text": [
      "Downloading and preparing dataset csv/knkarthick--dialogsum to /root/.cache/huggingface/datasets/csv/knkarthick--dialogsum-1aed23a5f481e688/0.0.0/433e0ccc46f9880962cc2b12065189766fbb2bee57a221866138fb9203c83519...\n"
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     "text": [
      "Dataset csv downloaded and prepared to /root/.cache/huggingface/datasets/csv/knkarthick--dialogsum-1aed23a5f481e688/0.0.0/433e0ccc46f9880962cc2b12065189766fbb2bee57a221866138fb9203c83519. Subsequent calls will reuse this data.\n"
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      "text/plain": [
       "DatasetDict({\n",
       "    train: Dataset({\n",
       "        features: ['id', 'dialogue', 'summary', 'topic'],\n",
       "        num_rows: 12460\n",
       "    })\n",
       "    test: Dataset({\n",
       "        features: ['id', 'dialogue', 'summary', 'topic'],\n",
       "        num_rows: 1500\n",
       "    })\n",
       "    validation: Dataset({\n",
       "        features: ['id', 'dialogue', 'summary', 'topic'],\n",
       "        num_rows: 500\n",
       "    })\n",
       "})"
      ]
     },
     "execution_count": 3,
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    }
   ],
   "source": [
    "# Load the dataset from HF - https://huggingface.co/datasets/knkarthick/dialogsum\n",
    "\n",
    "from datasets import load_dataset\n",
    "\n",
    "dataset_name = 'knkarthick/dialogsum' \n",
    "\n",
    "ds = load_dataset(dataset_name)\n",
    "\n",
    "# looking at the dataset splits\n",
    "ds"
   ]
  },
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    {
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   "source": [
    "# loading the full train dataset and subset of test dataset\n",
    "\n",
    "train_ds , test_ds = load_dataset(dataset_name,split =['train', 'test[0:200]'])"
   ]
  },
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   "execution_count": 5,
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   "source": [
    "# covert the dataset to pandas dataframe for instruction finetuning dataset preparation\n",
    "\n",
    "import pandas as pd\n",
    "\n",
    "train_df = pd.DataFrame(train_ds)\n",
    "test_df = pd.DataFrame(test_ds)"
   ]
  },
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>dialogue</th>\n",
       "      <th>summary</th>\n",
       "      <th>topic</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>train_0</td>\n",
       "      <td>#Person1#: Hi, Mr. Smith. I'm Doctor Hawkins. ...</td>\n",
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       "      <td>get a check-up</td>\n",
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       "      <td>train_1</td>\n",
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       "      <td>vaccines</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>train_2</td>\n",
       "      <td>#Person1#: Excuse me, did you see a set of key...</td>\n",
       "      <td>#Person1#'s looking for a set of keys and asks...</td>\n",
       "      <td>find keys</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>train_3</td>\n",
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       "      <td>have a girlfriend</td>\n",
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       "        id                                           dialogue  \\\n",
       "0  train_0  #Person1#: Hi, Mr. Smith. I'm Doctor Hawkins. ...   \n",
       "1  train_1  #Person1#: Hello Mrs. Parker, how have you bee...   \n",
       "2  train_2  #Person1#: Excuse me, did you see a set of key...   \n",
       "3  train_3  #Person1#: Why didn't you tell me you had a gi...   \n",
       "4  train_4  #Person1#: Watsup, ladies! Y'll looking'fine t...   \n",
       "\n",
       "                                             summary              topic  \n",
       "0  Mr. Smith's getting a check-up, and Doctor Haw...     get a check-up  \n",
       "1  Mrs Parker takes Ricky for his vaccines. Dr. P...           vaccines  \n",
       "2  #Person1#'s looking for a set of keys and asks...          find keys  \n",
       "3  #Person1#'s angry because #Person2# didn't tel...  have a girlfriend  \n",
       "4  Malik invites Nikki to dance. Nikki agrees if ...              dance  "
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     "execution_count": 6,
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   ],
   "source": [
    "# looking at the training dataset\n",
    "\n",
    "train_df.head()"
   ]
  },
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    "tags": []
   },
   "source": [
    "Write a concise summary of the following text which starts with ### Input: \\n\n",
    "Return your response in bullet points which covers the key points of the text."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
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   "source": [
    "# instruction finetuning data preparation function\n",
    "\n",
    "def prepare_dataset(df,split='train'):\n",
    "    text_col = []\n",
    "    instruction = \"\"\"Write a concise summary of the below input text. \"\"\" # change instuction according to the task\n",
    "    if split == 'train':\n",
    "        for _ , row in df.iterrows():\n",
    "            input_q = row[\"dialogue\"]\n",
    "            output = row[\"summary\"]\n",
    "            text = (\"### Instruction: \\n\" + instruction + \n",
    "                    \"\\n### Input: \\n\" + input_q + \n",
    "                    \"\\n### Response :\\n\" + output) # keeping output column in training dataset\n",
    "            text_col.append(text)\n",
    "        df.loc[:,'text'] = text_col\n",
    "    else:\n",
    "        for _ , row in df.iterrows():\n",
    "            input_q = row[\"dialogue\"]\n",
    "            text = (\"### Instruction: \\n\" + instruction + \n",
    "                    \"\\n### Input: \\n\" + input_q +\n",
    "                    \"\\n### Response :\\n\" ) # not keeping output column in test dataset\n",
    "            text_col.append(text)\n",
    "        df.loc[:,'text'] = text_col\n",
    "    return df"
   ]
  },
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   "source": [
    "train_df = prepare_dataset(train_df,'train')\n",
    "test_df = prepare_dataset(test_df,'test')"
   ]
  },
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       "      <td>train_0</td>\n",
       "      <td>#Person1#: Hi, Mr. Smith. I'm Doctor Hawkins. ...</td>\n",
       "      <td>Mr. Smith's getting a check-up, and Doctor Haw...</td>\n",
       "      <td>get a check-up</td>\n",
       "      <td>### Instruction: \\nWrite a concise summary of ...</td>\n",
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       "      <td>Mrs Parker takes Ricky for his vaccines. Dr. P...</td>\n",
       "      <td>vaccines</td>\n",
       "      <td>### Instruction: \\nWrite a concise summary of ...</td>\n",
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       "      <td>dance</td>\n",
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       "        id                                           dialogue  \\\n",
       "0  train_0  #Person1#: Hi, Mr. Smith. I'm Doctor Hawkins. ...   \n",
       "1  train_1  #Person1#: Hello Mrs. Parker, how have you bee...   \n",
       "2  train_2  #Person1#: Excuse me, did you see a set of key...   \n",
       "3  train_3  #Person1#: Why didn't you tell me you had a gi...   \n",
       "4  train_4  #Person1#: Watsup, ladies! Y'll looking'fine t...   \n",
       "\n",
       "                                             summary              topic  \\\n",
       "0  Mr. Smith's getting a check-up, and Doctor Haw...     get a check-up   \n",
       "1  Mrs Parker takes Ricky for his vaccines. Dr. P...           vaccines   \n",
       "2  #Person1#'s looking for a set of keys and asks...          find keys   \n",
       "3  #Person1#'s angry because #Person2# didn't tel...  have a girlfriend   \n",
       "4  Malik invites Nikki to dance. Nikki agrees if ...              dance   \n",
       "\n",
       "                                                text  \n",
       "0  ### Instruction: \\nWrite a concise summary of ...  \n",
       "1  ### Instruction: \\nWrite a concise summary of ...  \n",
       "2  ### Instruction: \\nWrite a concise summary of ...  \n",
       "3  ### Instruction: \\nWrite a concise summary of ...  \n",
       "4  ### Instruction: \\nWrite a concise summary of ...  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# looking at the train df , new text column is created\n",
    "train_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "d2fe6f72",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-29T06:06:34.866762Z",
     "iopub.status.busy": "2023-11-29T06:06:34.866156Z",
     "iopub.status.idle": "2023-11-29T06:06:34.870498Z",
     "shell.execute_reply": "2023-11-29T06:06:34.869687Z"
    },
    "papermill": {
     "duration": 0.017257,
     "end_time": "2023-11-29T06:06:34.872643",
     "exception": false,
     "start_time": "2023-11-29T06:06:34.855386",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "### Instruction: \n",
      "Write a concise summary of the below input text. \n",
      "### Input: \n",
      "#Person1#: Hi, Mr. Smith. I'm Doctor Hawkins. Why are you here today?\n",
      "#Person2#: I found it would be a good idea to get a check-up.\n",
      "#Person1#: Yes, well, you haven't had one for 5 years. You should have one every year.\n",
      "#Person2#: I know. I figure as long as there is nothing wrong, why go see the doctor?\n",
      "#Person1#: Well, the best way to avoid serious illnesses is to find out about them early. So try to come at least once a year for your own good.\n",
      "#Person2#: Ok.\n",
      "#Person1#: Let me see here. Your eyes and ears look fine. Take a deep breath, please. Do you smoke, Mr. Smith?\n",
      "#Person2#: Yes.\n",
      "#Person1#: Smoking is the leading cause of lung cancer and heart disease, you know. You really should quit.\n",
      "#Person2#: I've tried hundreds of times, but I just can't seem to kick the habit.\n",
      "#Person1#: Well, we have classes and some medications that might help. I'll give you more information before you leave.\n",
      "#Person2#: Ok, thanks doctor.\n",
      "### Response :\n",
      "Mr. Smith's getting a check-up, and Doctor Hawkins advises him to have one every year. Hawkins'll give some information about their classes and medications to help Mr. Smith quit smoking.\n"
     ]
    }
   ],
   "source": [
    "# looking at one of the train text column format\n",
    "print(train_df['text'][0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "31bf9680",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-29T06:06:34.893989Z",
     "iopub.status.busy": "2023-11-29T06:06:34.893691Z",
     "iopub.status.idle": "2023-11-29T06:06:34.903623Z",
     "shell.execute_reply": "2023-11-29T06:06:34.902784Z"
    },
    "papermill": {
     "duration": 0.02284,
     "end_time": "2023-11-29T06:06:34.905662",
     "exception": false,
     "start_time": "2023-11-29T06:06:34.882822",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>dialogue</th>\n",
       "      <th>summary</th>\n",
       "      <th>topic</th>\n",
       "      <th>text</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>test_0_1</td>\n",
       "      <td>#Person1#: Ms. Dawson, I need you to take a di...</td>\n",
       "      <td>Ms. Dawson helps #Person1# to write a memo to ...</td>\n",
       "      <td>communication method</td>\n",
       "      <td>### Instruction: \\nWrite a concise summary of ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>test_0_2</td>\n",
       "      <td>#Person1#: Ms. Dawson, I need you to take a di...</td>\n",
       "      <td>In order to prevent employees from wasting tim...</td>\n",
       "      <td>company policy</td>\n",
       "      <td>### Instruction: \\nWrite a concise summary of ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>test_0_3</td>\n",
       "      <td>#Person1#: Ms. Dawson, I need you to take a di...</td>\n",
       "      <td>Ms. Dawson takes a dictation for #Person1# abo...</td>\n",
       "      <td>dictation</td>\n",
       "      <td>### Instruction: \\nWrite a concise summary of ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>test_1_1</td>\n",
       "      <td>#Person1#: You're finally here! What took so l...</td>\n",
       "      <td>#Person2# arrives late because of traffic jam....</td>\n",
       "      <td>public transportation</td>\n",
       "      <td>### Instruction: \\nWrite a concise summary of ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>test_1_2</td>\n",
       "      <td>#Person1#: You're finally here! What took so l...</td>\n",
       "      <td>#Person2# decides to follow #Person1#'s sugges...</td>\n",
       "      <td>transportation</td>\n",
       "      <td>### Instruction: \\nWrite a concise summary of ...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         id                                           dialogue  \\\n",
       "0  test_0_1  #Person1#: Ms. Dawson, I need you to take a di...   \n",
       "1  test_0_2  #Person1#: Ms. Dawson, I need you to take a di...   \n",
       "2  test_0_3  #Person1#: Ms. Dawson, I need you to take a di...   \n",
       "3  test_1_1  #Person1#: You're finally here! What took so l...   \n",
       "4  test_1_2  #Person1#: You're finally here! What took so l...   \n",
       "\n",
       "                                             summary                  topic  \\\n",
       "0  Ms. Dawson helps #Person1# to write a memo to ...   communication method   \n",
       "1  In order to prevent employees from wasting tim...         company policy   \n",
       "2  Ms. Dawson takes a dictation for #Person1# abo...              dictation   \n",
       "3  #Person2# arrives late because of traffic jam....  public transportation   \n",
       "4  #Person2# decides to follow #Person1#'s sugges...         transportation   \n",
       "\n",
       "                                                text  \n",
       "0  ### Instruction: \\nWrite a concise summary of ...  \n",
       "1  ### Instruction: \\nWrite a concise summary of ...  \n",
       "2  ### Instruction: \\nWrite a concise summary of ...  \n",
       "3  ### Instruction: \\nWrite a concise summary of ...  \n",
       "4  ### Instruction: \\nWrite a concise summary of ...  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# looking at the test df , new text column is created\n",
    "test_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "9b41f01f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-29T06:06:34.928470Z",
     "iopub.status.busy": "2023-11-29T06:06:34.928208Z",
     "iopub.status.idle": "2023-11-29T06:06:34.932713Z",
     "shell.execute_reply": "2023-11-29T06:06:34.931843Z"
    },
    "papermill": {
     "duration": 0.018213,
     "end_time": "2023-11-29T06:06:34.934671",
     "exception": false,
     "start_time": "2023-11-29T06:06:34.916458",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "### Instruction: \n",
      "Write a concise summary of the below input text. \n",
      "### Input: \n",
      "#Person1#: Ms. Dawson, I need you to take a dictation for me.\n",
      "#Person2#: Yes, sir...\n",
      "#Person1#: This should go out as an intra-office memorandum to all employees by this afternoon. Are you ready?\n",
      "#Person2#: Yes, sir. Go ahead.\n",
      "#Person1#: Attention all staff... Effective immediately, all office communications are restricted to email correspondence and official memos. The use of Instant Message programs by employees during working hours is strictly prohibited.\n",
      "#Person2#: Sir, does this apply to intra-office communications only? Or will it also restrict external communications?\n",
      "#Person1#: It should apply to all communications, not only in this office between employees, but also any outside communications.\n",
      "#Person2#: But sir, many employees use Instant Messaging to communicate with their clients.\n",
      "#Person1#: They will just have to change their communication methods. I don't want any - one using Instant Messaging in this office. It wastes too much time! Now, please continue with the memo. Where were we?\n",
      "#Person2#: This applies to internal and external communications.\n",
      "#Person1#: Yes. Any employee who persists in using Instant Messaging will first receive a warning and be placed on probation. At second offense, the employee will face termination. Any questions regarding this new policy may be directed to department heads.\n",
      "#Person2#: Is that all?\n",
      "#Person1#: Yes. Please get this memo typed up and distributed to all employees before 4 pm.\n",
      "### Response :\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# looking at one of the test text column format without output data\n",
    "print(test_df['text'][0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "5c338dc7",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-29T06:06:34.956455Z",
     "iopub.status.busy": "2023-11-29T06:06:34.956202Z",
     "iopub.status.idle": "2023-11-29T06:06:35.011820Z",
     "shell.execute_reply": "2023-11-29T06:06:35.010915Z"
    },
    "papermill": {
     "duration": 0.068783,
     "end_time": "2023-11-29T06:06:35.013859",
     "exception": false,
     "start_time": "2023-11-29T06:06:34.945076",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# coverting the dataframe to huggingface dataset for easy finetuning\n",
    "from datasets import Dataset\n",
    "dataset = Dataset.from_pandas(train_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "d6964c5f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-29T06:06:35.036632Z",
     "iopub.status.busy": "2023-11-29T06:06:35.036343Z",
     "iopub.status.idle": "2023-11-29T06:06:35.041736Z",
     "shell.execute_reply": "2023-11-29T06:06:35.040920Z"
    },
    "papermill": {
     "duration": 0.018994,
     "end_time": "2023-11-29T06:06:35.043661",
     "exception": false,
     "start_time": "2023-11-29T06:06:35.024667",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Dataset({\n",
       "    features: ['id', 'dialogue', 'summary', 'topic', 'text'],\n",
       "    num_rows: 12460\n",
       "})"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# looking at the dataset\n",
    "dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "791346b3",
   "metadata": {
    "papermill": {
     "duration": 0.01027,
     "end_time": "2023-11-29T06:06:35.064497",
     "exception": false,
     "start_time": "2023-11-29T06:06:35.054227",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### Loading the sharded Llama-2 model in Quantized format"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "4f1f20e4",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-29T06:06:35.087002Z",
     "iopub.status.busy": "2023-11-29T06:06:35.086679Z",
     "iopub.status.idle": "2023-11-29T06:08:16.347651Z",
     "shell.execute_reply": "2023-11-29T06:08:16.346649Z"
    },
    "papermill": {
     "duration": 101.274907,
     "end_time": "2023-11-29T06:08:16.350100",
     "exception": false,
     "start_time": "2023-11-29T06:06:35.075193",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
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      "text/plain": [
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     },
     "metadata": {},
     "output_type": "display_data"
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    {
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      "text/plain": [
       "Loading checkpoint shards:   0%|          | 0/14 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "8fcde0d82e8b45a0a8dac23685c58782",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Downloading generation_config.json:   0%|          | 0.00/132 [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import torch\n",
    "from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig, AutoTokenizer\n",
    "\n",
    "# sharded model path in hugging face\n",
    "model_name = \"TinyPixel/Llama-2-7B-bf16-sharded\"\n",
    "\n",
    "# Quantization config\n",
    "bnb_config = BitsAndBytesConfig(\n",
    "    load_in_4bit=True,\n",
    "    bnb_4bit_quant_type=\"nf4\",\n",
    "    bnb_4bit_compute_dtype=\"float16\",\n",
    ")\n",
    "\n",
    "# loading the model with quantization config\n",
    "model = AutoModelForCausalLM.from_pretrained(\n",
    "    model_name,\n",
    "    quantization_config=bnb_config,\n",
    "    trust_remote_code=True,\n",
    "    device_map='auto'\n",
    ")\n",
    "model.config.use_cache = False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "01947deb",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-29T06:08:16.380437Z",
     "iopub.status.busy": "2023-11-29T06:08:16.379598Z",
     "iopub.status.idle": "2023-11-29T06:08:17.871558Z",
     "shell.execute_reply": "2023-11-29T06:08:17.870749Z"
    },
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     "duration": 1.509529,
     "end_time": "2023-11-29T06:08:17.874041",
     "exception": false,
     "start_time": "2023-11-29T06:08:16.364512",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "9ce05badb1634a2db07f0cd0c45805ee",
       "version_major": 2,
       "version_minor": 0
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      "text/plain": [
       "Downloading tokenizer_config.json:   0%|          | 0.00/676 [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "7ec0f55bc1c640de8c17ba04bf202863",
       "version_major": 2,
       "version_minor": 0
      },
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       "Downloading tokenizer.model:   0%|          | 0.00/500k [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "52107d63992d4a2383318491e45d6a4b",
       "version_major": 2,
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       "Downloading tokenizer.json:   0%|          | 0.00/1.84M [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "74161b9207f64e119f01152c6b7850ef",
       "version_major": 2,
       "version_minor": 0
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      "text/plain": [
       "Downloading (…)cial_tokens_map.json:   0%|          | 0.00/411 [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Creating the Llama-2 tokenizer\n",
    "\n",
    "tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True , return_token_type_ids=False)\n",
    "tokenizer.pad_token = tokenizer.eos_token"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bd8b23a2",
   "metadata": {
    "papermill": {
     "duration": 0.014976,
     "end_time": "2023-11-29T06:08:17.903862",
     "exception": false,
     "start_time": "2023-11-29T06:08:17.888886",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### QLoRA Configuration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "d42bda17",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-29T06:08:17.935536Z",
     "iopub.status.busy": "2023-11-29T06:08:17.934451Z",
     "iopub.status.idle": "2023-11-29T06:08:17.963149Z",
     "shell.execute_reply": "2023-11-29T06:08:17.962215Z"
    },
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     "duration": 0.046711,
     "end_time": "2023-11-29T06:08:17.965146",
     "exception": false,
     "start_time": "2023-11-29T06:08:17.918435",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "from peft import LoraConfig, get_peft_model\n",
    "\n",
    "lora_alpha = 16\n",
    "lora_dropout = 0.05 \n",
    "lora_r = 8 # rank\n",
    "\n",
    "# Parameter efficient finetuning for LoRA configuration\n",
    "\n",
    "peft_config = LoraConfig(\n",
    "    lora_alpha=lora_alpha,\n",
    "    lora_dropout=lora_dropout,\n",
    "    target_modules= [\"q_proj\",\"v_proj\"], # we will only create adopters for q, v metrices of attention module\n",
    "    r=lora_r,\n",
    "    bias=\"none\",\n",
    "    task_type=\"CAUSAL_LM\"\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ef12e5bd",
   "metadata": {
    "papermill": {
     "duration": 0.014474,
     "end_time": "2023-11-29T06:08:17.995017",
     "exception": false,
     "start_time": "2023-11-29T06:08:17.980543",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### Finetuning Process"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "e1a0f5e6",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-29T06:08:18.025033Z",
     "iopub.status.busy": "2023-11-29T06:08:18.024686Z",
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     "shell.execute_reply": "2023-11-29T06:08:18.296773Z"
    },
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     "duration": 0.290996,
     "end_time": "2023-11-29T06:08:18.300202",
     "exception": false,
     "start_time": "2023-11-29T06:08:18.009206",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# defining the model fine tuning arguments\n",
    "# arguments are self explanatory\n",
    "\n",
    "from transformers import TrainingArguments, Trainer\n",
    "\n",
    "training_arguments = TrainingArguments(\n",
    "        output_dir=\"llama2_qlora_finetuned\",\n",
    "        per_device_train_batch_size=4,\n",
    "        gradient_accumulation_steps=4,\n",
    "        optim=\"paged_adamw_8bit\",\n",
    "        learning_rate=2e-4,\n",
    "        lr_scheduler_type=\"linear\",\n",
    "        save_strategy=\"epoch\",\n",
    "        logging_steps=10,\n",
    "        num_train_epochs=3,\n",
    "        max_steps=100,\n",
    "        fp16=True,\n",
    "        push_to_hub=False,\n",
    "        report_to=\"none\"\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "40d1a062",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-29T06:08:18.330602Z",
     "iopub.status.busy": "2023-11-29T06:08:18.330113Z",
     "iopub.status.idle": "2023-11-29T06:08:32.591282Z",
     "shell.execute_reply": "2023-11-29T06:08:32.590482Z"
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     "start_time": "2023-11-29T06:08:18.314947",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "b64cc2cde79d430eb615c4b40fe17573",
       "version_major": 2,
       "version_minor": 0
      },
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       "  0%|          | 0/13 [00:00<?, ?ba/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# creating trainer with the training agruments\n",
    "\n",
    "from trl import SFTTrainer\n",
    "trainer = SFTTrainer(\n",
    "        model=model,\n",
    "        train_dataset=dataset,\n",
    "        peft_config=peft_config, # passing peft config\n",
    "        dataset_text_field=\"text\", # mentioned the required column\n",
    "        args=training_arguments, # training agruments\n",
    "        tokenizer=tokenizer, # tokenizer \n",
    "        packing=False,\n",
    "        max_seq_length=512\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "47f90757",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-29T06:08:32.625001Z",
     "iopub.status.busy": "2023-11-29T06:08:32.624648Z",
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     "shell.execute_reply": "2023-11-29T06:08:32.632769Z"
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     "exception": false,
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# upcasting the layer norms in float 32 for more stable training\n",
    "\n",
    "for name, module in trainer.model.named_modules():\n",
    "    if \"norm\" in name:\n",
    "        module = module.to(torch.float32)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "76a7a105",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-29T06:08:32.665272Z",
     "iopub.status.busy": "2023-11-29T06:08:32.664984Z",
     "iopub.status.idle": "2023-11-29T07:13:54.362437Z",
     "shell.execute_reply": "2023-11-29T07:13:54.361463Z"
    },
    "papermill": {
     "duration": 3921.716159,
     "end_time": "2023-11-29T07:13:54.365925",
     "exception": false,
     "start_time": "2023-11-29T06:08:32.649766",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "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"
     ]
    },
    {
     "data": {
      "text/html": [
       "\n",
       "    <div>\n",
       "      \n",
       "      <progress value='100' max='100' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
       "      [100/100 1:04:38, Epoch 0/1]\n",
       "    </div>\n",
       "    <table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       " <tr style=\"text-align: left;\">\n",
       "      <th>Step</th>\n",
       "      <th>Training Loss</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>45.265700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>20</td>\n",
       "      <td>6.211300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>30</td>\n",
       "      <td>6.260500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>40</td>\n",
       "      <td>37.280800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>50</td>\n",
       "      <td>13.207300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>60</td>\n",
       "      <td>6.233200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>70</td>\n",
       "      <td>6.101700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>80</td>\n",
       "      <td>7.695300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>90</td>\n",
       "      <td>493.320100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>100</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table><p>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "TrainOutput(global_step=100, training_loss=62.157594718933105, metrics={'train_runtime': 3921.3378, 'train_samples_per_second': 0.408, 'train_steps_per_second': 0.026, 'total_flos': 1.2988137631481856e+16, 'train_loss': 62.157594718933105, 'epoch': 0.13})"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# starting the finetuning process\n",
    "\n",
    "trainer.train()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a5b14e8f",
   "metadata": {
    "papermill": {
     "duration": 0.015028,
     "end_time": "2023-11-29T07:13:54.396575",
     "exception": false,
     "start_time": "2023-11-29T07:13:54.381547",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### Save the LoRA adopters / you can even push these adopters to hugging face model hub for future inference"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "11070f0c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-29T07:13:54.434023Z",
     "iopub.status.busy": "2023-11-29T07:13:54.433300Z",
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     "shell.execute_reply": "2023-11-29T07:13:54.479918Z"
    },
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     "duration": 0.066393,
     "end_time": "2023-11-29T07:13:54.483365",
     "exception": false,
     "start_time": "2023-11-29T07:13:54.416972",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "model_to_save = trainer.model.module if hasattr(trainer.model, 'module') else trainer.model  # Take care of distributed/parallel training\n",
    "model_to_save.save_pretrained(\"outputs\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "c967ea41",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-29T07:13:54.516202Z",
     "iopub.status.busy": "2023-11-29T07:13:54.515517Z",
     "iopub.status.idle": "2023-11-29T07:13:54.615783Z",
     "shell.execute_reply": "2023-11-29T07:13:54.614772Z"
    },
    "papermill": {
     "duration": 0.119262,
     "end_time": "2023-11-29T07:13:54.618450",
     "exception": false,
     "start_time": "2023-11-29T07:13:54.499188",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# adding back the LoRA adopters to the base Llama-2 model\n",
    "\n",
    "lora_config = LoraConfig.from_pretrained('outputs')\n",
    "model = get_peft_model(model, lora_config)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8437f2e2",
   "metadata": {
    "papermill": {
     "duration": 0.015252,
     "end_time": "2023-11-29T07:13:54.649480",
     "exception": false,
     "start_time": "2023-11-29T07:13:54.634228",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### Inference using Llama2 + QLoRA adopters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "f5017ec5",
   "metadata": {
    "execution": {
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     "start_time": "2023-11-29T07:13:54.664851",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "### Instruction: \n",
      "Write a concise summary of the below input text. \n",
      "### Input: \n",
      "#Person1#: Ms. Dawson, I need you to take a dictation for me.\n",
      "#Person2#: Yes, sir...\n",
      "#Person1#: This should go out as an intra-office memorandum to all employees by this afternoon. Are you ready?\n",
      "#Person2#: Yes, sir. Go ahead.\n",
      "#Person1#: Attention all staff... Effective immediately, all office communications are restricted to email correspondence and official memos. The use of Instant Message programs by employees during working hours is strictly prohibited.\n",
      "#Person2#: Sir, does this apply to intra-office communications only? Or will it also restrict external communications?\n",
      "#Person1#: It should apply to all communications, not only in this office between employees, but also any outside communications.\n",
      "#Person2#: But sir, many employees use Instant Messaging to communicate with their clients.\n",
      "#Person1#: They will just have to change their communication methods. I don't want any - one using Instant Messaging in this office. It wastes too much time! Now, please continue with the memo. Where were we?\n",
      "#Person2#: This applies to internal and external communications.\n",
      "#Person1#: Yes. Any employee who persists in using Instant Messaging will first receive a warning and be placed on probation. At second offense, the employee will face termination. Any questions regarding this new policy may be directed to department heads.\n",
      "#Person2#: Is that all?\n",
      "#Person1#: Yes. Please get this memo typed up and distributed to all employees before 4 pm.\n",
      "### Response :\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# perform inference on the first row of the test dataset\n",
    "text = test_df['text'][0]\n",
    "print(text)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "37367fd8",
   "metadata": {
    "papermill": {
     "duration": 0.01532,
     "end_time": "2023-11-29T07:13:54.719704",
     "exception": false,
     "start_time": "2023-11-29T07:13:54.704384",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### <b>Update:</b>\n",
    "\n",
    "Added repetition_penalty=1.2 to avoid the repetion of input task as input"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "bc9c2f35",
   "metadata": {
    "execution": {
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    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "### Instruction: \n",
      "Write a concise summary of the below input text. \n",
      "### Input: \n",
      "#Person1#: Ms. Dawson, I need you to take a dictation for me.\n",
      "#Person2#: Yes, sir...\n",
      "#Person1#: This should go out as an intra-office memorandum to all employees by this afternoon. Are you ready?\n",
      "#Person2#: Yes, sir. Go ahead.\n",
      "#Person1#: Attention all staff... Effective immediately, all office communications are restricted to email correspondence and official memos. The use of Instant Message programs by employees during working hours is strictly prohibited.\n",
      "#Person2#: Sir, does this apply to intra-office communications only? Or will it also restrict external communications?\n",
      "#Person1#: It should apply to all communications, not only in this office between employees, but also any outside communications.\n",
      "#Person2#: But sir, many employees use Instant Messaging to communicate with their clients.\n",
      "#Person1#: They will just have to change their communication methods. I don't want any - one using Instant Messaging in this office. It wastes too much time! Now, please continue with the memo. Where were we?\n",
      "#Person2#: This applies to internal and external communications.\n",
      "#Person1#: Yes. Any employee who persists in using Instant Messaging will first receive a warning and be placed on probation. At second offense, the employee will face termination. Any questions regarding this new policy may be directed to department heads.\n",
      "#Person2#: Is that all?\n",
      "#Person1#: Yes. Please get this memo typed up and distributed to all employees before 4 pm.\n",
      "### Response :\n",
      "The company has decided to ban instant messenger from its offices because they waste too much time. All messages must now be sent via e-mail or through formal memos. Employees can still send IMs to each other if necessary, however, these conversations cannot last longer than five minutes. If someone needs more information about something then he/she should call them instead of sending multiple short messages back and forth which takes forever when done over instant message services like Skype etc..\n",
      "##\n"
     ]
    }
   ],
   "source": [
    "inputs = tokenizer(text, return_tensors=\"pt\")\n",
    "outputs = model.generate(input_ids=inputs['input_ids'], attention_mask=inputs['attention_mask'], max_new_tokens=100 ,repetition_penalty=1.2)\n",
    "print(tokenizer.decode(outputs[0], skip_special_tokens=True))"
   ]
  }
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