calGPT-Falcon-finetuned.ipynb

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  },
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# Connect to Google Drive"
      ],
      "metadata": {
        "id": "ylYGVQ3cWGKE"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/gdrive')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kxGUGO2BWotH",
        "outputId": "6eb171e1-02e6-4c19-e5ac-49c822668b67"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/gdrive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Check GPU Availability"
      ],
      "metadata": {
        "id": "XfqW2kdnWIDL"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jagB9yfGyrDF",
        "outputId": "dcf50f56-da6d-4acd-f875-f2ab363f52ea"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Thu Aug 10 02:11:35 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 T4            Off  | 00000000:00:04.0 Off |                    0 |\n",
            "| N/A   53C    P8    13W /  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",
      "source": [
        "# Install Required Packages and Repo"
      ],
      "metadata": {
        "id": "LO9A3-qCWJ-f"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install trl transformers accelerate git+https://github.com/huggingface/peft.git -Uqqq\n",
        "!pip install datasets bitsandbytes einops wandb -Uqqq"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9gMqx00jyxpf",
        "outputId": "7414d5b2-c8d6-4214-f508-b00f9164f41e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "  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",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m88.1/88.1 kB\u001b[0m \u001b[31m1.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m7.4/7.4 MB\u001b[0m \u001b[31m16.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m244.2/244.2 kB\u001b[0m \u001b[31m23.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m519.3/519.3 kB\u001b[0m \u001b[31m25.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m268.8/268.8 kB\u001b[0m \u001b[31m27.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m7.8/7.8 MB\u001b[0m \u001b[31m42.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.3/1.3 MB\u001b[0m \u001b[31m55.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m115.3/115.3 kB\u001b[0m \u001b[31m12.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m194.1/194.1 kB\u001b[0m \u001b[31m19.9 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[31m16.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Building wheel for peft (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m92.6/92.6 MB\u001b[0m \u001b[31m9.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m42.2/42.2 kB\u001b[0m \u001b[31m4.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m2.1/2.1 MB\u001b[0m \u001b[31m62.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m188.5/188.5 kB\u001b[0m \u001b[31m9.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m215.6/215.6 kB\u001b[0m \u001b[31m20.7 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[31m5.9 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"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Import Libraries"
      ],
      "metadata": {
        "id": "3nMMXqMZUJGc"
      }
    },
    {
      "cell_type": "code",
      "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\")\n"
      ],
      "metadata": {
        "id": "LUyE2LT6y88O"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Huggingface Login"
      ],
      "metadata": {
        "id": "ztcvvaD6WM7t"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from huggingface_hub import notebook_login\n",
        "notebook_login()"
      ],
      "metadata": {
        "id": "_aNA8Or4y-pX",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 145,
          "referenced_widgets": [
            "5aefcb0d6d4049eb9e101a0d866e0880",
            "35a99b79376140c0bc1d6523b5945de9",
            "5db08f94dd0a4228867868ebe445d709",
            "aead3beadf2648ae98224f21b388e86f",
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            "e63d708bb5d94ed194f6bcdb74850df1",
            "10782d31e2e147388112bfc4c5e78053",
            "27bcf2bd919a483e96f6ed1079dc48be",
            "056db7c49ec141a1b99b4aa81c67a19c",
            "b5cbde03b27240ceac87aa2aea6a9218",
            "41e23fde2107468c93d00644a62ce178",
            "450201a0e7df46a180abb99b4f020710",
            "90082d548dc243dd8f49aebb6fbe93cd",
            "d223ae6620704ce2a854783cabe4f855",
            "7a69aa0ec5774ea491c4a565fe06917f",
            "7e9d742eb272435b91e933b8cf9216c6",
            "43dc537d786748bf96c89cabb71a2318",
            "3b7ab4c3c26c4593a47072c597ce064c",
            "88986e8d6e0f49d59e906f3d91aa7fc1",
            "5bbf14035c2c428d844736e178a65d71",
            "99b8e45c42c0472fbc483ac521db9410",
            "eaecb6ee008a44699dd6352627d14330",
            "40a19d6fdbc74d5db346b9da3cc1cd8e",
            "d6b293dcc2644826b1552bdff99a26b2",
            "cb01f7de13cf4387825effd7cc3cc306",
            "b4e0a256bdea4bb5844e45ca35ab8f20",
            "5e473d72d7ec4af687a9a209ea7a07c8",
            "a86b5a328ee44dd58f7cee77b25f8d66",
            "41ab46d403ab4cc189fc1395114eaa61",
            "089c947d6cf14d8ea1e6e2eb36251e3c",
            "3abe3d28870a4c208223010058b57e4b",
            "3a1d031fe2ff480383b84690ac46b76b"
          ]
        },
        "outputId": "ca5a2035-8313-4021-f9b4-736d55deffe8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "VBox(children=(HTML(value='<center> <img\\nsrc=https://huggingface.co/front/assets/huggingface_logo-noborder.sv…"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "5aefcb0d6d4049eb9e101a0d866e0880"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Download Dataset"
      ],
      "metadata": {
        "id": "eeilcfOMWP67"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data = load_dataset(\"ZahrizhalAli/mental_health_conversational_dataset\")\n",
        "data"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 281,
          "referenced_widgets": [
            "d17ff863c8b9465bb4819eaa49e99f8d",
            "aa11e3861a0f418dbd6c31f36bd880d6",
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            "b34ca806e9b742a5816b8697926c5f2e",
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            "26bd293108df47998b060c0f8ed2d528",
            "44c12bb4071d4bc680eacabc44805c17",
            "8fd724ab103d44df951bc3185b5b0234",
            "d7290e46c2314785aa234f07791f43f8",
            "e7c718f90f7d4f12b297b9bdb8c743ff",
            "d19a7b4fbddc45c28a2f7aeb9fbf69d2",
            "0341aac4e4514c99a013f672f4436088",
            "867b070bdcad446e925882aef6637f58",
            "ee7b4d91112d48b09529a6ffa44218bb",
            "8509a06beb304227b8fee84f11486964",
            "3595195c78524a36a05cb2452e9b3fd2",
            "498fed7a8b53475785a8eda1850994a2",
            "abf7af0726e84e658d13b2f8c26d12f6",
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            "7f4e0359cabc484c96cfc87895ce86d9",
            "1ba3272f861541a6b9d49eadf2bd66d4",
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            "1aa1ea347ef148b489fc7bb644f80e03",
            "65bb35e644964091867b4f4be1f94688",
            "5eeb1b918f824fecb5e3b95ea522eee2",
            "f5a04fb3da86413dbba03617a3fc781e",
            "f9afe65cd2eb426da49138618199aafb",
            "5c049bb00a964f62912f6704edfdb695",
            "98a29c8f41d14e9fbea25ef181f0a0e8",
            "ea8f9e75e41d45cab70e730730d14d49",
            "fcdf6b9c569044fa813798bcbf79bab0",
            "8a3683193c9b4d0c94f4459d0232c7ea",
            "9ea39d9a5eca4fca9fdf7553bf5362ec",
            "5ce6846d67704744b1f0697faf08cd4b",
            "2f86460f5dab44bf8ee9f0c2dbed88b5",
            "ae32f54c26c04fdfb30194716805e522",
            "8b714856dcb94742ba63b336dbc207ef",
            "15a97316e6fc40c8baed8193c8293609",
            "7dd5a3fc6c43469cb9a8c9e03cdc82c8",
            "bc52d78a7cf2493f8262be70eff82dd4",
            "d598e3f91a264db4b075d1a56aa9fa99",
            "8b09485cd4674c6781f0438472b70144",
            "01b7b202e4ea4c7fa179b7e42de4764e",
            "656b0a8d847e4492a5c9ae40ae119e80",
            "2e26236678004f31b1cd2f69ad47099b",
            "8b16f01da59f4f8bb049d5a65dc7e844",
            "0d5025782ab74c12b7155eab56961919",
            "c583ebff72ac4c62bfffd58d85eae696",
            "9af9c5fa8ba8416c8d55a2799b752425",
            "a223d93c51be47cb9958c7e3ebf7770b",
            "876614ba21a44d55ab6b86a13fe6581d",
            "4ed48f0c0e7346e4b9594e0f6969d994"
          ]
        },
        "id": "4VxTWaPBzZyi",
        "outputId": "ab1ee697-d41f-4b30-d8f4-c4d9696bd550"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading readme:   0%|          | 0.00/2.51k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "d17ff863c8b9465bb4819eaa49e99f8d"
            }
          },
          "metadata": {}
        },
        {
          "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": "d19a7b4fbddc45c28a2f7aeb9fbf69d2"
            }
          },
          "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": "1ba3272f861541a6b9d49eadf2bd66d4"
            }
          },
          "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": "fcdf6b9c569044fa813798bcbf79bab0"
            }
          },
          "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": "8b09485cd4674c6781f0438472b70144"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DatasetDict({\n",
              "    train: Dataset({\n",
              "        features: ['text'],\n",
              "        num_rows: 172\n",
              "    })\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(data[\"train\"][0]['text'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_CfsWQ84Uhx9",
        "outputId": "51a8b737-52f1-449c-e7dc-0c3e191b5ae0"
      },
      "execution_count": null,
      "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"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Load Pre-trained Model"
      ],
      "metadata": {
        "id": "srAzeKn8WSF1"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model_name = \"ybelkada/falcon-7b-sharded-bf16\" # 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.bfloat16, # 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",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49,
          "referenced_widgets": [
            "fd81727d43e4483ab093c84ea63b2419",
            "576cd22acf3c43909b58013eebd779b3",
            "26220480361b426b92ba48cebd494c47",
            "9d67ab9197564ff3bd9ab9da4e6a25b3",
            "deebaa0f8ba943d6bcc83cad01e5b06d",
            "63b8179fc7e344c59e66fa997bc2cf5a",
            "fba5a8288bac4a98875f41ddced95a05",
            "27ab97d344674f6fa3c59e1e90030ff9",
            "22ae5321038443bead367abb347ec1cd",
            "46a8d44e7b6245b6a3c8b01542131fe5",
            "3eae467bc3e041de87318391cc171005"
          ]
        },
        "id": "olCjCDgMz4ef",
        "outputId": "5a938c08-1ec6-417a-9c55-d132109b32a0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Loading checkpoint shards:   0%|          | 0/8 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "fd81727d43e4483ab093c84ea63b2419"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "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"
      ],
      "metadata": {
        "id": "pyAMxNk3060E"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "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 = 32 # 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)"
      ],
      "metadata": {
        "id": "xlzwduXuo8Dg"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "output_dir = \"/content/gdrive/MyDrive/LLM/falcon-7b-sharded-bf16-finetuned-mental-health-conversational\"\n",
        "per_device_train_batch_size = 2 # reduce batch size by 2x if out-of-memory error\n",
        "gradient_accumulation_steps = 2  # 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 = 320        # training will happen for 320 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",
        "    bf16=False,\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",
        "    tf32=False,\n",
        ")"
      ],
      "metadata": {
        "id": "CeroeMG8Vc8v"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "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",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dPlM64CoYESl",
        "outputId": "e9b6a116-cc65-408b-ef9d-477b4209d139"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/content/gdrive/MyDrive/LLM/falcon-7b-sharded-bf16-finetuned-mental-health-conversational is already a clone of https://huggingface.co/ZahrizhalAli/falcon-7b-sharded-bf16-finetuned-mental-health-conversational. Make sure you pull the latest changes with `repo.git_pull()`.\n",
            "WARNING:huggingface_hub.repository:/content/gdrive/MyDrive/LLM/falcon-7b-sharded-bf16-finetuned-mental-health-conversational is already a clone of https://huggingface.co/ZahrizhalAli/falcon-7b-sharded-bf16-finetuned-mental-health-conversational. Make sure you pull the latest changes with `repo.git_pull()`.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "for name, module in trainer.model.named_modules():\n",
        "    if \"norm\" in name:\n",
        "        module = module.to(torch.float32)"
      ],
      "metadata": {
        "id": "B4U9k0UWhZST"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Begin Fine Tuning"
      ],
      "metadata": {
        "id": "6j-VMRMBWYcL"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "peft_model.config.use_cache = False\n",
        "trainer.train()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "r7QPHXL_Y5WC",
        "outputId": "f9771954-3c17-4251-f4df-02650ea26f2e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='320' max='320' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [320/320 1:19:46, Epoch 7/8]\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>1.692900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>20</td>\n",
              "      <td>1.465100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>30</td>\n",
              "      <td>1.376800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>40</td>\n",
              "      <td>1.488300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>50</td>\n",
              "      <td>1.254500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>60</td>\n",
              "      <td>1.156100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>70</td>\n",
              "      <td>1.153100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>80</td>\n",
              "      <td>1.150900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>90</td>\n",
              "      <td>1.144400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>100</td>\n",
              "      <td>0.796400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>110</td>\n",
              "      <td>0.861400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>120</td>\n",
              "      <td>0.722200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>130</td>\n",
              "      <td>0.831800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>140</td>\n",
              "      <td>0.541000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>150</td>\n",
              "      <td>0.464600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>160</td>\n",
              "      <td>0.466800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>170</td>\n",
              "      <td>0.462000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>180</td>\n",
              "      <td>0.351300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>190</td>\n",
              "      <td>0.245400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>200</td>\n",
              "      <td>0.284500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>210</td>\n",
              "      <td>0.222900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>220</td>\n",
              "      <td>0.234500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>230</td>\n",
              "      <td>0.145600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>240</td>\n",
              "      <td>0.131400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>250</td>\n",
              "      <td>0.167400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>260</td>\n",
              "      <td>0.133400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>270</td>\n",
              "      <td>0.099200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>280</td>\n",
              "      <td>0.102300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>290</td>\n",
              "      <td>0.109500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>300</td>\n",
              "      <td>0.077600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>310</td>\n",
              "      <td>0.090000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>320</td>\n",
              "      <td>0.078300</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "TrainOutput(global_step=320, training_loss=0.6094261385500431, metrics={'train_runtime': 4813.7017, 'train_samples_per_second': 0.266, 'train_steps_per_second': 0.066, 'total_flos': 5858851030439424.0, 'train_loss': 0.6094261385500431, 'epoch': 7.44})"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# trainer.push_to_hub()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0bUwVf6Vo2Ln",
        "outputId": "a13a464d-e6cc-453d-d5c9-d4b6232ad4e5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Several commits (3) will be pushed upstream.\n",
            "WARNING:huggingface_hub.repository:Several commits (3) will be pushed upstream.\n",
            "The progress bars may be unreliable.\n",
            "WARNING:huggingface_hub.repository:The progress bars may be unreliable.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Inference Model"
      ],
      "metadata": {
        "id": "zprcf82rdVD3"
      }
    },
    {
      "cell_type": "code",
      "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"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 697,
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            "A new version of the following files was downloaded from https://huggingface.co/tiiuae/falcon-7b:\n",
            "- configuration_RW.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"
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            "A new version of the following files was downloaded from https://huggingface.co/tiiuae/falcon-7b:\n",
            "- modelling_RW.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"
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    {
      "cell_type": "code",
      "source": [
        "# Loading PEFT model\n",
        "PEFT_MODEL = \"ZahrizhalAli/calgpt-falcon-7b-finetuned-mental-health\"\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"
      ],
      "metadata": {
        "id": "91NXyrXrj12P",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 81,
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        "outputId": "72a83b8c-41f1-43d7-95be-d132222a3251"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)/adapter_config.json:   0%|          | 0.00/504 [00:00<?, ?B/s]"
            ],
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          "metadata": {}
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        {
          "output_type": "display_data",
          "data": {
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              "Loading checkpoint shards:   0%|          | 0/8 [00:00<?, ?it/s]"
            ],
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    },
    {
      "cell_type": "code",
      "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\"\"\": {query}\n",
        "  : \"\"\"\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)"
      ],
      "metadata": {
        "id": "YBPOdM5_kCBo"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "generate_answer('What to do if you have mental illnes?')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XeSqbznV5Byv",
        "outputId": "37830de6-e612-40ef-b8c8-9f8b4c1a17db"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "-------------------------------------------------\n",
            "ORIGINAL MODEL RESPONSE:\n",
            "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",
            ": What to do if you have mental illnes?\n",
            "  : 1. Consult a psychiatrist.\n",
            "  : 2. Consult a psychologist.\n",
            "  : 3. Consult a psychoanalyst.\n",
            "  : 4. Consult a psychotherapist.\n",
            "  : 5. Consult a psychotherapist.\n",
            ": What to do if you have mental illnes?\n",
            "  : 1. Consult a psychiatrist.\n",
            "  : 2. Consult a psychologist.\n",
            "  : 3. Consult a psychoanalyst.\n",
            "  : 4. Consult a psychotherapist.\n",
            "  : 5. Consult a psychotherapist.\n",
            ": What to do if you have mental illnes?\n",
            "  : 1. Consult a psychiatrist.\n",
            "  : 2. Consult a psychologist.\n",
            "  : 3. Consult a psychoanalyst.\n",
            "  : 4. Consult a psychotherapist.\n",
            "  : 5. Consult a psychotherapist.\n",
            ": What to do if you have mental illnes?\n",
            "  : 1. Consult a psychiatrist.\n",
            "  : 2. Consult a psychologist.\n",
            "  : 3. Consult a psychoanalyst.\n",
            "  : 4. Consult a psychotherapist.\n",
            "  : 5. Consult a psychotherapist.\n",
            ": What to do if you have mental illnes?\n",
            "  : 1. Consult a\n",
            "-------------------------------------------------\n",
            "PEFT MODEL RESPONSE:\n",
            "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",
            ": What to do if you have mental illnes?\n",
            "  : 1. Consult a Mental Health Professional: It's essential to seek guidance from a qualified mental health professional, such as a psychologist, counselor, or therapist. They can provide personalized support and treatment tailored to your specific needs.\n",
            "\n",
            "2. Educate Yourself: Learning about mental illnesses can be helpful in understanding what you're going through and how to manage them effectively. Books, podcasts, and online resources are excellent sources of information.\n",
            "\n",
            "3. Talk to Someone Trusted: Reach out to friends, family members, or someone close who can offer empathy and emotional support. Sometimes sharing your feelings with others can bring relief.\n",
            "\n",
            "4. Practice Self-Care: Engage in activities that promote relaxation and mindfulness. This might include meditation, exercise, hobbies, spending time outdoors, or doing things you love.\n",
            "\n",
            "5. Set Realistic Goals: Work on improving one aspect of your mental health at a time, rather than trying to address multiple issues simultaneously. Small incremental changes can lead to significant progress.\n",
            "\n",
            "6. Avoid Stigmatization: Remember, seeking help is a sign of strength, and there is no shame in struggling with mental illness. However, some people may react negatively, so it's crucial to educate those around you about the importance of mental\n",
            "-------------------------------------------------\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "Cp5GBydC5Hn4"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}