{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AjIEWbCDkanS"
      },
      "source": [
        "## Check GPU Availability"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "f4YxNfdGFfhu",
        "outputId": "2f8c24b0-59f6-466a-ac7a-d3cbdd4d3097"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Thu Apr 25 08:41:08 2024       \n",
            "+---------------------------------------------------------------------------------------+\n",
            "| NVIDIA-SMI 535.104.05             Driver Version: 535.104.05   CUDA Version: 12.2     |\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 T4                       Off | 00000000:00:04.0 Off |                    0 |\n",
            "| N/A   68C    P8              12W /  70W |      0MiB / 15360MiB |      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": "markdown",
      "metadata": {
        "id": "DUwSLgpOkiFy"
      },
      "source": [
        "## Install required libraries"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FmkS5lxikB5p",
        "outputId": "b29dfa0b-2189-43a6-95d2-22848c816e88"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m245.2/245.2 kB\u001b[0m \u001b[31m4.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m134.8/134.8 kB\u001b[0m \u001b[31m15.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m388.9/388.9 kB\u001b[0m \u001b[31m30.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m486.2/486.2 kB\u001b[0m \u001b[31m10.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m2.1/2.1 MB\u001b[0m \u001b[31m65.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m110.5/110.5 kB\u001b[0m \u001b[31m13.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m207.3/207.3 kB\u001b[0m \u001b[31m23.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m267.1/267.1 kB\u001b[0m \u001b[31m29.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m62.7/62.7 kB\u001b[0m \u001b[31m8.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m134.8/134.8 kB\u001b[0m \u001b[31m16.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m134.3/134.3 kB\u001b[0m \u001b[31m15.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Building wheel for pathtools (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
            "  Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n",
            "  Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
            "  Building wheel for transformers (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n"
          ]
        }
      ],
      "source": [
        "!pip install trl accelerate peft==0.4.0 -Uqqq\n",
        "!pip install datasets==2.13.1 bitsandbytes==0.41.3 einops==0.7.0 wandb==0.15.8 -Uqqq\n",
        "!pip install git+https://github.com/huggingface/transformers -Uqqq"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "toxLYDhem1xE"
      },
      "source": [
        "## Importing libraries"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "cfveWI2gdztX"
      },
      "outputs": [],
      "source": [
        "import torch\n",
        "from datasets import load_dataset\n",
        "from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig, TrainingArguments, GenerationConfig\n",
        "from peft import LoraConfig, get_peft_model, PeftConfig, PeftModel, prepare_model_for_kbit_training\n",
        "from trl import SFTTrainer\n",
        "import warnings\n",
        "warnings.filterwarnings(\"ignore\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 113,
          "referenced_widgets": [
            "e0df7902801c4dcdad3bbed886c4dfce",
            "401ffa071b4c419abda0505b23ca4ce6",
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            "1dc8084935274931bc80343f957758f8",
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            "dd38503bad7542a999bc888a17db7f44",
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            "d56cb5cb29834580ba7b2480c0246d3a",
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            "db385b8020e040e4bac2ceb1f3671888",
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            "d66baaae2d6849d99fc6e47bcd47bd28",
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            "362878cff74e49b9adb7ddd92640940d",
            "1b92fc16173c401d99de9025e06157b2",
            "347bb878a02048ceab99a8d3a4545aa4"
          ]
        },
        "id": "lfznv6tCmS9t",
        "outputId": "72aa8a9f-07e9-4b5a-bfaa-8db98c795d8c"
      },
      "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": "e0df7902801c4dcdad3bbed886c4dfce"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "from huggingface_hub import notebook_login\n",
        "notebook_login()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "92LnTGhgm4x9"
      },
      "source": [
        "## Load custom Mental Health conv dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 336,
          "referenced_widgets": [
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            "e5828bef356c44b8b260cca35adf4afd",
            "c0bed70ae3f747b5b19d2bb3e8ab2539",
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            "60d9653f39214be08f7e7bc04b2fef39",
            "ce54dd7a44084f1f8b3c3e99cf2a16e4",
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            "24d4382865cc4007bdfbf832729eeb0e",
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            "dfcee317a96c462190f6ac8389c6e668",
            "ec757adbc11a419096a1809c1d854654",
            "b7a5b13bf5774cd893fd95e4e3b32cfb",
            "3028a9e88d354961be6c597cb7275476",
            "91801ecb0c01473c8bbafd085a4b704b",
            "51880d89d27d4dd486607ada901de900",
            "46a5873bce9d41bba2c1845af222b67e",
            "0bf3a49e3c1848c9891dabbf4d5633b4"
          ]
        },
        "id": "1e1lsdwhoyFn",
        "outputId": "7e05d431-6fa4-4e62-c5f7-6570843c1aa4"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading readme:   0%|          | 0.00/2.50k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "809b69e6dbbc4a35be22afbf8b7d307f"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading and preparing dataset None/None to /root/.cache/huggingface/datasets/heliosbrahma___parquet/heliosbrahma--mental_health_chatbot_dataset-d55249bd7649267b/0.0.0/14a00e99c0d15a23649d0db8944380ac81082d4b021f398733dd84f3a6c569a7...\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading data files:   0%|          | 0/1 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "c39db387888e4d3aa908301b7a0300a5"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading data:   0%|          | 0.00/102k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "600ead3466204b18ba35a16d49b4b367"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Extracting data files:   0%|          | 0/1 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "76dbd1e9a9144d98a08afc32b260e38e"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating train split:   0%|          | 0/172 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "aa1ed61a5b7d41f8847a25d9fa55a6d6"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Dataset parquet downloaded and prepared to /root/.cache/huggingface/datasets/heliosbrahma___parquet/heliosbrahma--mental_health_chatbot_dataset-d55249bd7649267b/0.0.0/14a00e99c0d15a23649d0db8944380ac81082d4b021f398733dd84f3a6c569a7. Subsequent calls will reuse this data.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/1 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "24d4382865cc4007bdfbf832729eeb0e"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DatasetDict({\n",
              "    train: Dataset({\n",
              "        features: ['text'],\n",
              "        num_rows: 172\n",
              "    })\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ],
      "source": [
        "data = load_dataset(\"heliosbrahma/mental_health_chatbot_dataset\")\n",
        "data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dBmEXhu1rL8B",
        "outputId": "c2f37eae-7d39-4e9f-94d5-c8cfd83d02d2"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<HUMAN>: What is a panic attack?\n",
            "<ASSISTANT>: Panic attacks come on suddenly and involve intense and often overwhelming fear. They’re accompanied by very challenging physical symptoms, like a racing heartbeat, shortness of breath, or nausea. Unexpected panic attacks occur without an obvious cause. Expected panic attacks are cued by external stressors, like phobias. Panic attacks can happen to anyone, but having more than one may be a sign of panic disorder, a mental health condition characterized by sudden and repeated panic attacks.\n"
          ]
        }
      ],
      "source": [
        "print(data[\"train\"][0]['text'])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dWqkK0UwnIMl"
      },
      "source": [
        "## Model Training"
      ]
    },
    {
      "cell_type": "code",
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        },
        "id": "m6NmgiExnTQX",
        "outputId": "35ecb661-9c00-4238-de62-a71d5b7cba55"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "config.json:   0%|          | 0.00/1.05k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "91e40ec3027e4d5ca83f815481f2e651"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "configuration_falcon.py:   0%|          | 0.00/6.70k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "db7cc7c2afb64ebd802307bdadc8d331"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "A new version of the following files was downloaded from https://huggingface.co/vilsonrodrigues/falcon-7b-instruct-sharded:\n",
            "- configuration_falcon.py\n",
            ". Make sure to double-check they do not contain any added malicious code. To avoid downloading new versions of the code file, you can pin a revision.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "modeling_falcon.py:   0%|          | 0.00/56.9k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
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            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "A new version of the following files was downloaded from https://huggingface.co/vilsonrodrigues/falcon-7b-instruct-sharded:\n",
            "- modeling_falcon.py\n",
            ". Make sure to double-check they do not contain any added malicious code. To avoid downloading new versions of the code file, you can pin a revision.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "model.safetensors.index.json:   0%|          | 0.00/16.9k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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        },
        {
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          "metadata": {}
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          "metadata": {}
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          },
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          },
          "metadata": {}
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          },
          "metadata": {}
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        {
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              "model_id": "6cac60a7d95346659dfc434eb2c8c3f1"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
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              "version_major": 2,
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              "model_id": "a6a69ec4380e4db4a845320461737386"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "aee5c5e6c2cf4c5f97e9d35b6744710a"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Loading checkpoint shards:   0%|          | 0/15 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "ede0966a8e064a2bbacc34cae603c5f9"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "8b0f102684ca4661af888dccb24fe846"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "model_name = \"vilsonrodrigues/falcon-7b-instruct-sharded\" # sharded falcon-7b model\n",
        "\n",
        "bnb_config = BitsAndBytesConfig(\n",
        "    load_in_4bit=True,            # load model in 4-bit precision\n",
        "    bnb_4bit_quant_type=\"nf4\",    # pre-trained model should be quantized in 4-bit NF format\n",
        "    bnb_4bit_use_double_quant=True, # Using double quantization as mentioned in QLoRA paper\n",
        "    bnb_4bit_compute_dtype=torch.float16, # During computation, pre-trained model should be loaded in BF16 format\n",
        ")\n",
        "\n",
        "model = AutoModelForCausalLM.from_pretrained(\n",
        "    model_name,\n",
        "    quantization_config=bnb_config, # Use bitsandbytes config\n",
        "    device_map=\"auto\",  # Specifying device_map=\"auto\" so that HF Accelerate will determine which GPU to put each layer of the model on\n",
        "    trust_remote_code=True, # Set trust_remote_code=True to use falcon-7b model with custom code\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 113,
          "referenced_widgets": [
            "1e2f985a5a5d4bff8c591cd874f5d8c0",
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            "8752105bb6e449bd94f8b49e1590a647",
            "44d0c2a30a794aad863100d785750bde",
            "785135c8af2846b79b15204ba83b5147",
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            "30700de9147b4b32984df01e9ac5b568",
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            "0d505aa7b3014f529c629784abede432",
            "90e9e4f739bf4878b119539bcf1ef3b3",
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            "2db4e2051e8e4ed2b0aaae8556a5635b",
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            "6c89dcb1092240fcabbcadedb8623e15",
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            "d64fa9d753e3441a9ac33095c014412e",
            "5e1e7eb3621e4a4ca58676230ab2c41d",
            "eb7e437e8d834e8a95fa73e39f506c7b",
            "42564d6384f048eebc1bb704172f9a5f",
            "bd98613a4e814341a19019d9cb32e21e",
            "ddea638ca02640a1839d228483462b28",
            "2818f8ce24f54622840b78963d7da407",
            "32d80e64c94e48e1ac6db0b0790c94eb",
            "f4fb58621180413da24c26784ccacc5e",
            "4fa01f5a03884f4d862dd83baec73143"
          ]
        },
        "id": "ZqL9NirAoTDP",
        "outputId": "4eb04618-41b9-401e-f704-47de2fc48fab"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer_config.json:   0%|          | 0.00/287 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "1e2f985a5a5d4bff8c591cd874f5d8c0"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer.json:   0%|          | 0.00/2.73M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "0fa999097b344247ae4f00d066fd51af"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "special_tokens_map.json:   0%|          | 0.00/281 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "c0cc2d0c9d29413c8bae8d32246ec5dd"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True) # Set trust_remote_code=True\n",
        "tokenizer.pad_token = tokenizer.eos_token # Setting pad_token same as eos_token"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "id": "nYwhe7WWf6ee",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "61f5d5fd-dcd0-4a80-e046-5a89a0be058f"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "You are using an old version of the checkpointing format that is deprecated (We will also silently ignore `gradient_checkpointing_kwargs` in case you passed it).Please update to the new format on your modeling file. To use the new format, you need to completely remove the definition of the method `_set_gradient_checkpointing` in your model.\n"
          ]
        }
      ],
      "source": [
        "model = prepare_model_for_kbit_training(model)\n",
        "\n",
        "lora_alpha = 32 # scaling factor for the weight matrices\n",
        "lora_dropout = 0.05 # dropout probability of the LoRA layers\n",
        "lora_rank = 16 # dimension of the low-rank matrices\n",
        "\n",
        "peft_config = LoraConfig(\n",
        "    lora_alpha=lora_alpha,\n",
        "    lora_dropout=lora_dropout,\n",
        "    r=lora_rank,\n",
        "    bias=\"none\",  # setting to 'none' for only training weight params instead of biases\n",
        "    task_type=\"CAUSAL_LM\",\n",
        "    target_modules=[         # Setting names of modules in falcon-7b model that we want to apply LoRA to\n",
        "        \"query_key_value\",\n",
        "        \"dense\",\n",
        "        \"dense_h_to_4h\",\n",
        "        \"dense_4h_to_h\",\n",
        "    ]\n",
        ")\n",
        "\n",
        "peft_model = get_peft_model(model, peft_config)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "id": "elkLI6fTgEpf"
      },
      "outputs": [],
      "source": [
        "output_dir = \"./falcon-7b-sharded-fp16-finetuned-mental-health-conversational\"\n",
        "per_device_train_batch_size = 8 # reduce batch size by 2x if out-of-memory error\n",
        "gradient_accumulation_steps = 8  # increase gradient accumulation steps by 2x if batch size is reduced\n",
        "optim = \"paged_adamw_32bit\" # activates the paging for better memory management\n",
        "save_strategy=\"steps\" # checkpoint save strategy to adopt during training\n",
        "save_steps = 10 # number of updates steps before two checkpoint saves\n",
        "logging_steps = 10  # number of update steps between two logs if logging_strategy=\"steps\"\n",
        "learning_rate = 2e-4  # learning rate for AdamW optimizer\n",
        "max_grad_norm = 0.3 # maximum gradient norm (for gradient clipping)\n",
        "max_steps = 70        # training will happen for 70 steps\n",
        "warmup_ratio = 0.03 # number of steps used for a linear warmup from 0 to learning_rate\n",
        "lr_scheduler_type = \"cosine\"  # learning rate scheduler\n",
        "\n",
        "training_arguments = TrainingArguments(\n",
        "    output_dir=output_dir,\n",
        "    per_device_train_batch_size=per_device_train_batch_size,\n",
        "    gradient_accumulation_steps=gradient_accumulation_steps,\n",
        "    optim=optim,\n",
        "    save_steps=save_steps,\n",
        "    logging_steps=logging_steps,\n",
        "    learning_rate=learning_rate,\n",
        "    fp16=True,\n",
        "    max_grad_norm=max_grad_norm,\n",
        "    max_steps=max_steps,\n",
        "    warmup_ratio=warmup_ratio,\n",
        "    group_by_length=True,\n",
        "    lr_scheduler_type=lr_scheduler_type,\n",
        "    push_to_hub=True,\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "id": "2GywxTnBjVue",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b9c1c5bf-6ea4-45af-fff1-eccd9dc5f6cd"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:datasets.arrow_dataset:Loading cached processed dataset at /root/.cache/huggingface/datasets/heliosbrahma___parquet/heliosbrahma--mental_health_chatbot_dataset-d55249bd7649267b/0.0.0/14a00e99c0d15a23649d0db8944380ac81082d4b021f398733dd84f3a6c569a7/cache-b4b8aebb14f49fff.arrow\n",
            "max_steps is given, it will override any value given in num_train_epochs\n"
          ]
        }
      ],
      "source": [
        "trainer = SFTTrainer(\n",
        "    model=peft_model,\n",
        "    train_dataset=data['train'],\n",
        "    peft_config=peft_config,\n",
        "    dataset_text_field=\"text\",\n",
        "    max_seq_length=1024,\n",
        "    tokenizer=tokenizer,\n",
        "    args=training_arguments,\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "id": "NH3C7OQfjjfU"
      },
      "outputs": [],
      "source": [
        "# upcasting the layer norms in torch.bfloat16 for more stable training\n",
        "for name, module in trainer.model.named_modules():\n",
        "    if \"norm\" in name:\n",
        "        module = module.to(torch.float16)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "id": "nEDSJfzvl3Oz",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "5fbe9c0f-2f5a-4954-eabe-f64de6484100"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[34m\u001b[1mwandb\u001b[0m: Logging into wandb.ai. (Learn how to deploy a W&B server locally: https://wandb.me/wandb-server)\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: You can find your API key in your browser here: https://wandb.ai/authorize\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: Paste an API key from your profile and hit enter, or press ctrl+c to quit: \n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: Appending key for api.wandb.ai to your netrc file: /root/.netrc\n"
          ]
        }
      ],
      "source": [
        "# authenticate WandB for logging metrics\n",
        "import wandb\n",
        "!wandb login"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# torch.cuda.empty_cache()"
      ],
      "metadata": {
        "id": "ccHabsLNtj6S"
      },
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "nYW_j0Vajgh_",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 451
        },
        "outputId": "3406e918-99a6-47e4-e5d8-7173740fa4ac"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\u001b[34m\u001b[1mwandb\u001b[0m: Currently logged in as: \u001b[33maj7287\u001b[0m (\u001b[33mintern_test\u001b[0m). Use \u001b[1m`wandb login --relogin`\u001b[0m to force relogin\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: wandb version 0.16.6 is available!  To upgrade, please run:\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m:  $ pip install wandb --upgrade\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: Tracking run with wandb version 0.15.8\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: Run data is saved locally in \u001b[35m\u001b[1m/content/wandb/run-20240425_085936-y54rqht6\u001b[0m\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: Run \u001b[1m`wandb offline`\u001b[0m to turn off syncing.\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: Syncing run \u001b[33micy-vortex-16\u001b[0m\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: ⭐️ View project at \u001b[34m\u001b[4mhttps://wandb.ai/intern_test/huggingface\u001b[0m\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: 🚀 View run at \u001b[34m\u001b[4mhttps://wandb.ai/intern_test/huggingface/runs/y54rqht6\u001b[0m\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='80' max='120' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [ 80/120 1:19:57 < 41:00, 0.02 it/s, Epoch 28.73/60]\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>1.670900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>20</td>\n",
              "      <td>1.314300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>30</td>\n",
              "      <td>0.964100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>40</td>\n",
              "      <td>0.533800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>50</td>\n",
              "      <td>0.219800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>60</td>\n",
              "      <td>0.081400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>70</td>\n",
              "      <td>0.047600</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>"
            ]
          },
          "metadata": {}
        }
      ],
      "source": [
        "peft_model.config.use_cache = False\n",
        "trainer.train()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "hBtLstduL6LI"
      },
      "outputs": [],
      "source": [
        "trainer.push_to_hub(\"therapx\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Gsxub1fgUMxt"
      },
      "source": [
        "## Inference Pipeline"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "VOKiDZlK6DHO"
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      "source": [
        "# Loading original model\n",
        "model_name = \"ybelkada/falcon-7b-sharded-bf16\"\n",
        "\n",
        "bnb_config = BitsAndBytesConfig(\n",
        "    load_in_4bit=True,\n",
        "    bnb_4bit_quant_type=\"nf4\",\n",
        "    bnb_4bit_use_double_quant=True,\n",
        "    bnb_4bit_compute_dtype=torch.float16,\n",
        ")\n",
        "\n",
        "model = AutoModelForCausalLM.from_pretrained(\n",
        "    model_name,\n",
        "    quantization_config=bnb_config,\n",
        "    device_map=\"auto\",\n",
        "    trust_remote_code=True,\n",
        ")\n",
        "\n",
        "tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)\n",
        "tokenizer.pad_token = tokenizer.eos_token"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "qnP9C3zZ5pt1"
      },
      "outputs": [],
      "source": [
        "# Loading PEFT model\n",
        "PEFT_MODEL = \"heliosbrahma/falcon-7b-sharded-bf16-finetuned-mental-health-conversational\"\n",
        "\n",
        "config = PeftConfig.from_pretrained(PEFT_MODEL)\n",
        "peft_base_model = AutoModelForCausalLM.from_pretrained(\n",
        "    config.base_model_name_or_path,\n",
        "    return_dict=True,\n",
        "    quantization_config=bnb_config,\n",
        "    device_map=\"auto\",\n",
        "    trust_remote_code=True,\n",
        ")\n",
        "\n",
        "peft_model = PeftModel.from_pretrained(peft_base_model, PEFT_MODEL)\n",
        "\n",
        "peft_tokenizer = AutoTokenizer.from_pretrained(config.base_model_name_or_path)\n",
        "peft_tokenizer.pad_token = peft_tokenizer.eos_token"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "TfvBS3yl54lV"
      },
      "outputs": [],
      "source": [
        "# Function to generate responses from both original model and PEFT model and compare their answers.\n",
        "def generate_answer(query):\n",
        "  system_prompt = \"\"\"Answer the following question truthfully.\n",
        "  If you don't know the answer, respond 'Sorry, I don't know the answer to this question.'.\n",
        "  If the question is too complex, respond 'Kindly, consult a psychiatrist for further queries.'.\"\"\"\n",
        "\n",
        "  user_prompt = f\"\"\"<HUMAN>: {query}\n",
        "  <ASSISTANT>: \"\"\"\n",
        "\n",
        "  final_prompt = system_prompt + \"\\n\" + user_prompt\n",
        "\n",
        "  device = \"cuda:0\"\n",
        "  dashline = \"-\".join(\"\" for i in range(50))\n",
        "\n",
        "  encoding = tokenizer(final_prompt, return_tensors=\"pt\").to(device)\n",
        "  outputs = model.generate(input_ids=encoding.input_ids, generation_config=GenerationConfig(max_new_tokens=256, pad_token_id = tokenizer.eos_token_id, \\\n",
        "                                                                                                                     eos_token_id = tokenizer.eos_token_id, attention_mask = encoding.attention_mask, \\\n",
        "                                                                                                                     temperature=0.4, top_p=0.6, repetition_penalty=1.3, num_return_sequences=1,))\n",
        "  text_output = tokenizer.decode(outputs[0], skip_special_tokens=True)\n",
        "\n",
        "  print(dashline)\n",
        "  print(f'ORIGINAL MODEL RESPONSE:\\n{text_output}')\n",
        "  print(dashline)\n",
        "\n",
        "  peft_encoding = peft_tokenizer(final_prompt, return_tensors=\"pt\").to(device)\n",
        "  peft_outputs = peft_model.generate(input_ids=peft_encoding.input_ids, generation_config=GenerationConfig(max_new_tokens=256, pad_token_id = peft_tokenizer.eos_token_id, \\\n",
        "                                                                                                                     eos_token_id = peft_tokenizer.eos_token_id, attention_mask = peft_encoding.attention_mask, \\\n",
        "                                                                                                                     temperature=0.4, top_p=0.6, repetition_penalty=1.3, num_return_sequences=1,))\n",
        "  peft_text_output = peft_tokenizer.decode(peft_outputs[0], skip_special_tokens=True)\n",
        "\n",
        "  print(f'PEFT MODEL RESPONSE:\\n{peft_text_output}')\n",
        "  print(dashline)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Mi3WdVFx6Pdw"
      },
      "source": [
        "## Compare responses between Original model and PEFT model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Mnwo_FbQ6LeF"
      },
      "outputs": [],
      "source": [
        "query = \"How can I prevent anxiety and depression?\"\n",
        "generate_answer(query)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "4gM7DBHXQjpI"
      },
      "outputs": [],
      "source": [
        "query = \"How to take care of mental health?\"\n",
        "generate_answer(query)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "2krOSjIMRUAS"
      },
      "outputs": [],
      "source": [
        "query = \"What is the warning sign of depression?\"\n",
        "generate_answer(query)"
      ]
    }
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