2-FineTuning.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/jlopetegui98/RebornToBeWilde/blob/main/2-FineTuning.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "K2VtgoCecKVH"
      },
      "source": [
        "**Fine tuning Mistral-7B (instruction) with Oscar Wilde texts**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "a9sqgb94cKVJ",
        "outputId": "5430df8b-0414-446c-9d9d-fbd01df84c8b"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ],
      "source": [
        "# uncomment the following line to run in colab\n",
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KpSpCUyWcKVK",
        "outputId": "ef5e2091-c7ed-4696-838e-2ef384c92543"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m105.0/105.0 MB\u001b[0m \u001b[31m15.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  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",
            "  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[32m280.0/280.0 kB\u001b[0m \u001b[31m4.9 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",
            "  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 accelerate (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m155.3/155.3 kB\u001b[0m \u001b[31m3.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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            "\u001b[?25h  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
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            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m71.5/71.5 kB\u001b[0m \u001b[31m9.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m77.0/77.0 kB\u001b[0m \u001b[31m10.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Building wheel for ffmpy (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
            "lida 0.0.10 requires kaleido, which is not installed.\n",
            "ibis-framework 7.1.0 requires pyarrow<15,>=2, but you have pyarrow 15.0.0 which is incompatible.\n",
            "torchaudio 2.1.0+cu121 requires torch==2.1.0, but you have torch 2.2.0 which is incompatible.\n",
            "torchdata 0.7.0 requires torch==2.1.0, but you have torch 2.2.0 which is incompatible.\n",
            "torchtext 0.16.0 requires torch==2.1.0, but you have torch 2.2.0 which is incompatible.\n",
            "torchvision 0.16.0+cu121 requires torch==2.1.0, but you have torch 2.2.0 which is incompatible.\u001b[0m\u001b[31m\n",
            "\u001b[0m"
          ]
        }
      ],
      "source": [
        "# uncomment the following line to run in colab\n",
        "!pip install -q -U bitsandbytes\n",
        "!pip install -q -U git+https://github.com/huggingface/transformers.git\n",
        "!pip install -q -U git+https://github.com/huggingface/peft.git\n",
        "!pip install -q -U git+https://github.com/huggingface/accelerate.git\n",
        "!pip install -q trl xformers wandb datasets einops gradio sentencepiece"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-usabpdicKVK",
        "outputId": "35447b65-1e29-4364-905e-8908ae985e61"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Sun Feb 18 23:44:29 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   36C    P8               9W /  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": [
        "# uncomment the following line to run in colab\n",
        "!nvidia-smi"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "kQkCe0eqcKVK"
      },
      "outputs": [],
      "source": [
        "from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig,HfArgumentParser,TrainingArguments,pipeline, logging, TextStreamer\n",
        "from peft import LoraConfig, PeftModel, prepare_model_for_kbit_training, get_peft_model\n",
        "import os,torch, platform, gradio, warnings\n",
        "from datasets import load_dataset, Dataset\n",
        "from trl import SFTTrainer\n",
        "from huggingface_hub import notebook_login\n",
        "import json\n",
        "import utils"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from utils import *"
      ],
      "metadata": {
        "id": "zxS7L2k8g_iZ"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "id": "VyWU1zL0cKVK"
      },
      "outputs": [],
      "source": [
        "# model to fine-tune\n",
        "model_name = \"mistralai/Mistral-7B-Instruct-v0.1\"\n",
        "dataset_name = \"finetuning_instructions_dataset_wilde\""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "id": "Vzr3K1jccKVL"
      },
      "outputs": [],
      "source": [
        "# path to the data\n",
        "# root_path = '.' # comment if using colab\n",
        "root_path = './drive/MyDrive/RebornToBeWilde' # uncomment if using colab\n",
        "dir_data = f'{root_path}/data/{dataset_name}.json'\n",
        "models_path = f'{root_path}/models'"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VpeBnkkxcKVL",
        "outputId": "6c57116a-fddd-4d35-b46d-ad08d1b61f07"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DatasetDict({\n",
              "    train: Dataset({\n",
              "        features: ['text'],\n",
              "        num_rows: 7654\n",
              "    })\n",
              "    test: Dataset({\n",
              "        features: ['text'],\n",
              "        num_rows: 851\n",
              "    })\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ],
      "source": [
        "# load the dataset\n",
        "data_dict = json.load(open(dir_data))\n",
        "dataset = Dataset.from_dict(data_dict)\n",
        "dataset = dataset.train_test_split(test_size = 0.1, seed = 42)\n",
        "dataset"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import random"
      ],
      "metadata": {
        "id": "0HrTljpEo0QI"
      },
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "dataset_red = dataset['train'].filter(lambda x:random.random()<0.2)\n",
        "len(dataset_red)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 67,
          "referenced_widgets": [
            "744c3f3875664e58b2d897810068cc75",
            "f6982316ce3f4cca8afb4807cf879151",
            "3f79c189ed384148bd12c031c1232da3",
            "2467dfa666d848f29353d5d56638b5e9",
            "8257cef7b5684422bf22c2a19a2ebc3e",
            "9599d8affd4c4f9e8e6328e2a0d91a57",
            "a9e715224d7f412fa367d1a6637ff018",
            "83bf72ae820b48299e4cf9c0ebd6b5c9",
            "bfab2a1672804949b2102ded1dc257bf",
            "82050b6103444e1f9b0d14e6a2313556",
            "a7e7db360ef34fe8821d6f49389503ac"
          ]
        },
        "id": "2W6e6ZMzof4B",
        "outputId": "8a148192-ad02-451d-83ba-30ffcb9a374f"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Filter:   0%|          | 0/7654 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "744c3f3875664e58b2d897810068cc75"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1533"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4xOKSA8-cKVL",
        "outputId": "1a91a562-6190-4c50-9b8f-b73c4c2af449"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<s>[INST]These are the first lines of a literary work. Continue the text below, closely following its style and content. while I have hardly seen Some twenty summers cast their green for Autumn’s gaudy liveries. But you can read the Hieroglyphs on the great sandstone obelisks, And you have talked with Basilisks, and you have looked on Hippogriffs. O tell me, were you standing by when Isis to Osiris knelt? And did you watch the Egyptian melt her union for Antony And drink the jewel-drunken wine and bend her head in mimic awe To see the huge proconsul draw the salted tunny from the brine? And did you mark the Cyprian kiss white Adon on his catafalque? And did [/INST] you follow Amenalk, the God of Heliopolis? And did you talk with Thoth, and did you hear the moon-horned Io weep? And know the painted kings who sleep beneath the wedge-shaped Pyramid? * * * * * LIFT up your large black satin eyes which are like cushions where one sinks! Fawn at my feet, fantastic Sphinx! and sing me all your memories! Sing to me of the Jewish maid who wandered with the Holy Child, And how you led them through the wild, and how they slept beneath your shade. Sing to me of that odorous green eve when crouching by the marge You heard from Adrian’s gilded barge the laughter of Antinous And lapped the stream and fed your drouth and watched with hot and hungry stare The ivory body of that rare young slave with his pomegranate mouth! Sing to me of the Labyrinth in which the twi-formed bull was stalled! Sing to me of the night you crawled across the temple’s granite plinth When through the purple corridors the screaming scarlet Ibis flew In</s>\n"
          ]
        }
      ],
      "source": [
        "# example of the dataset\n",
        "print(dataset['train']['text'][0])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000,
          "referenced_widgets": [
            "a55755b449cd461a8eb7306014fa1910",
            "e068720d11ca46fea37cb702c961047b",
            "9989815bcca542c283acd0f31a5fdebf",
            "281bfd06c0cd40fbaedabb8a74018f64",
            "c8e1ea1961fc48a9b4f284312491103f",
            "70d2bb061aad44299e82629921575198",
            "486489b33fa34c44a2933d0372ff4235",
            "2e1cbdcccc6c4caaabfb91098fd25196",
            "daade59b73eb4a85a64c6ee8c8731c8f",
            "86cc684c7fdf4869b8a859e2b9ca3dc4",
            "72b935df3f124f6394925129863815a3",
            "50ebc052c20c4664b4080541d7197bf1",
            "159d00a92aae477a94657d1c3fc74b9e",
            "ccb6de8896c3497bb6e4670cda7d8bd5",
            "9f21835077024b68a2a938474f6952f7",
            "70886a30ce544987bec15718adf104ef",
            "3e51a18408c94a76b6459a1738a3e6c7",
            "d61bb044d36449db8807e69867ddb6b2",
            "77c074a8e2be4945ac17342a36884c2d",
            "1f295d0844e24811933fbbc32c789c95",
            "2ccc1aefada740b2a929035da874f26c",
            "cb1967fe5cb34c6eb7e24816663df5a8",
            "15d412cb754041e5902dfbfc195231f0",
            "e46ddd32ea854cc784c57936923285f7",
            "25e9505f6cb54b948e6a1ba32a481e73",
            "11adb1dd5ebb4ab4af105f6174f26438",
            "583b4343bc8045c58785635fa67d6868",
            "adc66caa230f42d09c863e3d330a1167",
            "481ce30aedc540a58b62e4f94376d273",
            "58d649fd46e149efb65e48ed293746f9",
            "b2929b3fa5be44fab570d49cd72aee9f",
            "29ffe4a82fb2430c97bdd015438776a3",
            "6c4f5533439c457ab79ac2296848c1cd",
            "a6a420276f5e40c9947531cbcde3ede3",
            "63e36bc6246f438e97b707d8d69dce54",
            "3756de81d55240758c25f254ef006dfc",
            "a9b850feca404a1f86a45c014a6cb030",
            "c9d5d14e2ff643879f9715f9a5937a74",
            "f5d7582b36ff4839b5b71089f6306891",
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            "c2cd27ed0065483aa04742c0fe3b9c9b",
            "d0e9ea3104bb453998b0edf69ecff1db",
            "5507352a3b504fe4a61049188bd6d7b2",
            "dc540dd7e93144368f4e5c2ad2055962",
            "48e8cb142b98485b96c1e8a5646b900c",
            "223e2a92b600459c8d927eb250500dd2",
            "b513248bd60a4c17b807eac1439568a3",
            "2f1f7d5ab017452aad9d34bf2cede44a",
            "93ca1030a67348d0b3eca7314e813c91",
            "66de82e49dd744659de9bf5a9646123b",
            "6db09250a27e43cf99187abb49279608",
            "708dc3193d8a4d289916e3c284921943",
            "64eec13ea8cd4414ba94f08974a2360c",
            "706fbbdaa3e1438ab6c455eb196bc1a6",
            "4f172fa586f643f189fd802b818ab1da",
            "65a65526290d46f384320ed6a8c3c3fc",
            "888058b31c504d1e98095114400bce50",
            "70e7d311b7f543719575f7e9bd078db6",
            "dd0c4353858543b18d8d8e49451f29e7",
            "d5531617738b44c28cc722c9faf435b4",
            "f94edba605ba48df8f1b5e2e60ab8e3b",
            "b577f8b7456342c5b7f9e34053cb5cd3",
            "df58f44625624e4c9e1ac7730b246868",
            "d4f9394eadd942d4b34a74334626b237",
            "cd2b989cea444f84a0269692f5f9aea1",
            "89890bd5065b4a9b8b9d06e62c815201",
            "70b9331e1a734c1995cfc20f4b4c4574",
            "e441b552086849bc92526e64bbba672f",
            "c3ce9b41be8b466e8c635665e18bcaf8",
            "fd528174294c488689c226d1eb551ea8",
            "fed0de1cb22846c09fd96b3f07c44421",
            "197341d21b904d47ba71009c890a734c",
            "8b2557bdef154e79aa867c848ee897a4"
          ]
        },
        "id": "j53qtgGGcKVL",
        "outputId": "acd2e859-6841-4b7e-9b9e-b00ebd160a0b"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:88: UserWarning: \n",
            "The secret `HF_TOKEN` does not exist in your Colab secrets.\n",
            "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n",
            "You will be able to reuse this secret in all of your notebooks.\n",
            "Please note that authentication is recommended but still optional to access public models or datasets.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "a55755b449cd461a8eb7306014fa1910"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
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            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
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            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
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            ],
            "application/vnd.jupyter.widget-view+json": {
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            }
          },
          "metadata": {}
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        {
          "output_type": "display_data",
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            "application/vnd.jupyter.widget-view+json": {
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            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Loading checkpoint shards:   0%|          | 0/2 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
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            }
          },
          "metadata": {}
        },
        {
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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            }
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "PeftModelForCausalLM(\n",
              "  (base_model): LoraModel(\n",
              "    (model): MistralForCausalLM(\n",
              "      (model): MistralModel(\n",
              "        (embed_tokens): Embedding(32000, 4096)\n",
              "        (layers): ModuleList(\n",
              "          (0-31): 32 x MistralDecoderLayer(\n",
              "            (self_attn): MistralSdpaAttention(\n",
              "              (q_proj): lora.Linear4bit(\n",
              "                (base_layer): Linear4bit(in_features=4096, out_features=4096, bias=False)\n",
              "                (lora_dropout): ModuleDict(\n",
              "                  (default): Dropout(p=0.05, inplace=False)\n",
              "                )\n",
              "                (lora_A): ModuleDict(\n",
              "                  (default): Linear(in_features=4096, out_features=16, bias=False)\n",
              "                )\n",
              "                (lora_B): ModuleDict(\n",
              "                  (default): Linear(in_features=16, out_features=4096, bias=False)\n",
              "                )\n",
              "                (lora_embedding_A): ParameterDict()\n",
              "                (lora_embedding_B): ParameterDict()\n",
              "              )\n",
              "              (k_proj): lora.Linear4bit(\n",
              "                (base_layer): Linear4bit(in_features=4096, out_features=1024, bias=False)\n",
              "                (lora_dropout): ModuleDict(\n",
              "                  (default): Dropout(p=0.05, inplace=False)\n",
              "                )\n",
              "                (lora_A): ModuleDict(\n",
              "                  (default): Linear(in_features=4096, out_features=16, bias=False)\n",
              "                )\n",
              "                (lora_B): ModuleDict(\n",
              "                  (default): Linear(in_features=16, out_features=1024, bias=False)\n",
              "                )\n",
              "                (lora_embedding_A): ParameterDict()\n",
              "                (lora_embedding_B): ParameterDict()\n",
              "              )\n",
              "              (v_proj): lora.Linear4bit(\n",
              "                (base_layer): Linear4bit(in_features=4096, out_features=1024, bias=False)\n",
              "                (lora_dropout): ModuleDict(\n",
              "                  (default): Dropout(p=0.05, inplace=False)\n",
              "                )\n",
              "                (lora_A): ModuleDict(\n",
              "                  (default): Linear(in_features=4096, out_features=16, bias=False)\n",
              "                )\n",
              "                (lora_B): ModuleDict(\n",
              "                  (default): Linear(in_features=16, out_features=1024, bias=False)\n",
              "                )\n",
              "                (lora_embedding_A): ParameterDict()\n",
              "                (lora_embedding_B): ParameterDict()\n",
              "              )\n",
              "              (o_proj): lora.Linear4bit(\n",
              "                (base_layer): Linear4bit(in_features=4096, out_features=4096, bias=False)\n",
              "                (lora_dropout): ModuleDict(\n",
              "                  (default): Dropout(p=0.05, inplace=False)\n",
              "                )\n",
              "                (lora_A): ModuleDict(\n",
              "                  (default): Linear(in_features=4096, out_features=16, bias=False)\n",
              "                )\n",
              "                (lora_B): ModuleDict(\n",
              "                  (default): Linear(in_features=16, out_features=4096, bias=False)\n",
              "                )\n",
              "                (lora_embedding_A): ParameterDict()\n",
              "                (lora_embedding_B): ParameterDict()\n",
              "              )\n",
              "              (rotary_emb): MistralRotaryEmbedding()\n",
              "            )\n",
              "            (mlp): MistralMLP(\n",
              "              (gate_proj): lora.Linear4bit(\n",
              "                (base_layer): Linear4bit(in_features=4096, out_features=14336, bias=False)\n",
              "                (lora_dropout): ModuleDict(\n",
              "                  (default): Dropout(p=0.05, inplace=False)\n",
              "                )\n",
              "                (lora_A): ModuleDict(\n",
              "                  (default): Linear(in_features=4096, out_features=16, bias=False)\n",
              "                )\n",
              "                (lora_B): ModuleDict(\n",
              "                  (default): Linear(in_features=16, out_features=14336, bias=False)\n",
              "                )\n",
              "                (lora_embedding_A): ParameterDict()\n",
              "                (lora_embedding_B): ParameterDict()\n",
              "              )\n",
              "              (up_proj): Linear4bit(in_features=4096, out_features=14336, bias=False)\n",
              "              (down_proj): Linear4bit(in_features=14336, out_features=4096, bias=False)\n",
              "              (act_fn): SiLU()\n",
              "            )\n",
              "            (input_layernorm): MistralRMSNorm()\n",
              "            (post_attention_layernorm): MistralRMSNorm()\n",
              "          )\n",
              "        )\n",
              "        (norm): MistralRMSNorm()\n",
              "      )\n",
              "      (lm_head): Linear(in_features=4096, out_features=32000, bias=False)\n",
              "    )\n",
              "  )\n",
              ")"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ],
      "source": [
        "# Load base model(Mistral 7B-Instruct)\n",
        "# quantization config\n",
        "model = load_model(model_name, adapt = True)\n",
        "# print the model\n",
        "model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 145,
          "referenced_widgets": [
            "abc97bd0026f44588ce3010d1df3fdfd",
            "7666443aaf644bdea6968befcea54b06",
            "ad9d6f44f43c4dc2bbd8d6617fdab318",
            "6d60360f45ae4cc1b4368f6bd77a2aea",
            "ec92a2359b7345208d33fae357b939d2",
            "dcb6c375240842f5bfc87f8aaecedde5",
            "413a908c5d224b2285742068689a0923",
            "b70bba6dc17b4d35bcee106d8ec2fc06",
            "a2c6ea47ef904ecda87cba303ca9cf52",
            "fd546fff737f4e839e96ebd1ea196b09",
            "3cd61ce45da14fb8a11bcb3edb26d1f1",
            "d055743e9fc74a1195f14e30c50e891d",
            "8947f289085241f194bbe12101dd88f8",
            "93e0b37d3e4b4e139df28c8efc07ee91",
            "4d913ec83daa4539959e031b993f0fdd",
            "5e14c51d7c274c70a95fc4de9a3b0bec",
            "334b0139fc964dc683cb8f573cb695f2",
            "fbdc14c170b24857b35bae74253aa375",
            "0f777e3890f14a4492c5db7be4c9dda4",
            "766cf83efcb347498c2c5b3100e78567",
            "2551ddf5e27d40f5980b6a93b465a19f",
            "7c15d27ccece4ca9a4d3064c3d494f64",
            "c3d713999cb84c20a4f33051e219b8d0",
            "3fe72646a3744bc18376983db5375993",
            "9083d06c5a2c48b7b5fee4a8361151b1",
            "08bba8d5e498499eb12b1c535a50f4f2",
            "f2766ab45f43427d90593b0dffc7c86a",
            "65f43e5eb47c48d09d68d2f24ddfbda5",
            "ec17f945322e4ed5a91e8f1b9bcb841b",
            "1b3effffd5a04f5d8cb4d1e32be1e44b",
            "b5787dcdf49d44a9a302d2e840b41650",
            "74fc70fd59e149c7b08c54369a60df79",
            "f9c46c23c45b499fb1508dae8eb996b8",
            "ffee7024081445c8ab8cc18b8b6b564d",
            "96bd0bf4a0394f6caf993135ff39a40e",
            "872f5a1b4b8a40c1b96f332181d78026",
            "ade902487a1644469430ccd9b6b8ca8b",
            "d18cec80aa3846abaa798c58f1344d58",
            "1dffe12a4fab455ca05c7220b1c2bedc",
            "c41e674647404c8aad74376d3f312247",
            "336436fe6daf45399101d88d7b2dee81",
            "68b9ce05c2b84da8ab4ac34d32e4748c",
            "a60fbdc38c334483ba30e1d68632648f",
            "aa4f53313e6d4fd2a2ab8936cfdeb812"
          ]
        },
        "id": "HolBWiZ0cKVL",
        "outputId": "4d4eac2c-ae16-4586-e976-a802700eeac0"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer_config.json:   0%|          | 0.00/1.47k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "abc97bd0026f44588ce3010d1df3fdfd"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer.model:   0%|          | 0.00/493k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "d055743e9fc74a1195f14e30c50e891d"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer.json:   0%|          | 0.00/1.80M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "c3d713999cb84c20a4f33051e219b8d0"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "special_tokens_map.json:   0%|          | 0.00/72.0 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "ffee7024081445c8ab8cc18b8b6b564d"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Load tokenizer\n",
        "tokenizer = load_tokenizer(model_name)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "peft_config = LoraConfig(\n",
        "        r=16,\n",
        "        lora_alpha=16,\n",
        "        lora_dropout=0.05,\n",
        "        bias=\"none\",\n",
        "        task_type=\"CAUSAL_LM\",\n",
        "        target_modules=[\"q_proj\", \"k_proj\", \"v_proj\", \"o_proj\",\"gate_proj\"]\n",
        "    )"
      ],
      "metadata": {
        "id": "SxFBt-C5iS9n"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 190,
          "referenced_widgets": [
            "7af70a6430704ae78b71c69c8c868578",
            "346c26e7bbbc49eebe578cb26aede623",
            "c6e63f20d88f4ddc85c77843530bb9be",
            "6c415f1530d34b89a2ea99e7481055fc",
            "c5f507d6b36742c7b3e50bf32babc9fd",
            "9b4b6f62700a41768fe10baeb8489c37",
            "0ba0231c05e64c2e9e8cc950c21576be",
            "7a3c9b0e6e3a4586be52fadb5c1e6ce4",
            "c99dc1459e1b44b89a9d209be0537321",
            "3db97648e5804348a49e44632de420ff",
            "d5d504f3d47b47b9a01e70cdbb410194",
            "b39950866ed547128e06147e3e07c0c7",
            "2a243d74b0a646df8f13971cddc57035",
            "2ac663ca96bc467f82699282edbaa781",
            "8c2cfcf648994303978017cb60ccf67d",
            "8e71c791f8d94303b77f99e8e034d6fa",
            "4756fe9d5d3f4a119724412188b451be",
            "309af1c828454b4c95732689232f7c73",
            "1170bfd482a94e48ba5832e4ffafa180",
            "9e9567571f114516b9c1cfd716708208",
            "607082610f924c2e88709738a3061b19",
            "23ada3d30fbc4fbabee986a1bb1e3b0b"
          ]
        },
        "id": "HI6wGK5zcKVM",
        "outputId": "7df1dd59-fd2e-4a14-8658-61cc03bd613e"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/1533 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "7af70a6430704ae78b71c69c8c868578"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/851 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "b39950866ed547128e06147e3e07c0c7"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/trl/trainer/sft_trainer.py:294: UserWarning: You passed a tokenizer with `padding_side` not equal to `right` to the SFTTrainer. This might lead to some unexpected behaviour due to overflow issues when training a model in half-precision. You might consider adding `tokenizer.padding_side = 'right'` to your code.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/accelerate/accelerator.py:437: FutureWarning: Passing the following arguments to `Accelerator` is deprecated and will be removed in version 1.0 of Accelerate: dict_keys(['dispatch_batches', 'split_batches', 'even_batches', 'use_seedable_sampler']). Please pass an `accelerate.DataLoaderConfiguration` instead: \n",
            "dataloader_config = DataLoaderConfiguration(dispatch_batches=None, split_batches=False, even_batches=True, use_seedable_sampler=True)\n",
            "  warnings.warn(\n"
          ]
        }
      ],
      "source": [
        "#Hyperparamters for fine tuning\n",
        "training_arguments = TrainingArguments(\n",
        "    output_dir= \"./results\",\n",
        "    num_train_epochs= 2,\n",
        "    per_device_train_batch_size= 4,\n",
        "    gradient_accumulation_steps= 2,\n",
        "    optim = \"paged_adamw_8bit\",\n",
        "    save_steps= 1000,\n",
        "    logging_steps= 30,\n",
        "    learning_rate= 2e-4,\n",
        "    weight_decay= 0.001,\n",
        "    fp16= False,\n",
        "    bf16= False,\n",
        "    max_grad_norm= 0.3,\n",
        "    max_steps= -1,\n",
        "    warmup_ratio= 0.3,\n",
        "    group_by_length= True,\n",
        "    lr_scheduler_type= \"constant\",\n",
        "    # report_to=\"wandb\"\n",
        ")\n",
        "# Setting sft parameters\n",
        "trainer = SFTTrainer(\n",
        "    model=model,\n",
        "    train_dataset=dataset_red,\n",
        "    eval_dataset=dataset['test'],\n",
        "    peft_config=peft_config,\n",
        "    max_seq_length= 512,\n",
        "    dataset_text_field=\"text\",\n",
        "    tokenizer=tokenizer,\n",
        "    args=training_arguments,\n",
        "    packing= False,\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 701
        },
        "id": "mlWAAsygcKVM",
        "outputId": "84074dcc-dee2-46d3-87d1-e0750d90e356"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "\n",
              "        window._wandbApiKey = new Promise((resolve, reject) => {\n",
              "            function loadScript(url) {\n",
              "            return new Promise(function(resolve, reject) {\n",
              "                let newScript = document.createElement(\"script\");\n",
              "                newScript.onerror = reject;\n",
              "                newScript.onload = resolve;\n",
              "                document.body.appendChild(newScript);\n",
              "                newScript.src = url;\n",
              "            });\n",
              "            }\n",
              "            loadScript(\"https://cdn.jsdelivr.net/npm/postmate/build/postmate.min.js\").then(() => {\n",
              "            const iframe = document.createElement('iframe')\n",
              "            iframe.style.cssText = \"width:0;height:0;border:none\"\n",
              "            document.body.appendChild(iframe)\n",
              "            const handshake = new Postmate({\n",
              "                container: iframe,\n",
              "                url: 'https://wandb.ai/authorize'\n",
              "            });\n",
              "            const timeout = setTimeout(() => reject(\"Couldn't auto authenticate\"), 5000)\n",
              "            handshake.then(function(child) {\n",
              "                child.on('authorize', data => {\n",
              "                    clearTimeout(timeout)\n",
              "                    resolve(data)\n",
              "                });\n",
              "            });\n",
              "            })\n",
              "        });\n",
              "    "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\u001b[34m\u001b[1mwandb\u001b[0m: Appending key for api.wandb.ai to your netrc file: /root/.netrc\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "Tracking run with wandb version 0.16.3"
            ]
          },
          "metadata": {}
        },
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          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
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            "text/html": [
              "Run data is saved locally in <code>/content/wandb/run-20240218_235407-e1ffh57h</code>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "Syncing run <strong><a href='https://wandb.ai/javier-lopetegui-gonzalez/huggingface/runs/e1ffh57h' target=\"_blank\">thriving-kumquat-2</a></strong> to <a href='https://wandb.ai/javier-lopetegui-gonzalez/huggingface' target=\"_blank\">Weights & Biases</a> (<a href='https://wandb.me/run' target=\"_blank\">docs</a>)<br/>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              " View project at <a href='https://wandb.ai/javier-lopetegui-gonzalez/huggingface' target=\"_blank\">https://wandb.ai/javier-lopetegui-gonzalez/huggingface</a>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              " View run at <a href='https://wandb.ai/javier-lopetegui-gonzalez/huggingface/runs/e1ffh57h' target=\"_blank\">https://wandb.ai/javier-lopetegui-gonzalez/huggingface/runs/e1ffh57h</a>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='384' max='384' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [384/384 4:39:08, Epoch 2/2]\n",
              "    </div>\n",
              "    <table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              " <tr style=\"text-align: left;\">\n",
              "      <th>Step</th>\n",
              "      <th>Training Loss</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>30</td>\n",
              "      <td>2.641200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>60</td>\n",
              "      <td>2.463200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>90</td>\n",
              "      <td>2.321900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>120</td>\n",
              "      <td>2.292300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>150</td>\n",
              "      <td>2.309800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>180</td>\n",
              "      <td>2.246200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>210</td>\n",
              "      <td>2.043200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>240</td>\n",
              "      <td>1.884000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>270</td>\n",
              "      <td>1.941100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>300</td>\n",
              "      <td>1.876600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>330</td>\n",
              "      <td>1.861600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>360</td>\n",
              "      <td>1.833400</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "TrainOutput(global_step=384, training_loss=2.114862014849981, metrics={'train_runtime': 16800.642, 'train_samples_per_second': 0.182, 'train_steps_per_second': 0.023, 'total_flos': 5.697145602428928e+16, 'train_loss': 2.114862014849981, 'epoch': 2.0})"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ],
      "source": [
        "# Train the model\n",
        "trainer.train()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 126
        },
        "id": "eB_ODb44cKVM",
        "outputId": "815f564c-6740-4ef3-b544-b5c296c0c8f0"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='107' max='107' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [107/107 24:46]\n",
              "    </div>\n",
              "    "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'eval_loss': 2.0518088340759277,\n",
              " 'eval_runtime': 1497.4404,\n",
              " 'eval_samples_per_second': 0.568,\n",
              " 'eval_steps_per_second': 0.071,\n",
              " 'epoch': 2.0}"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ],
      "source": [
        "# evaluate the model\n",
        "trainer.evaluate()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "id": "jJ7wXadGcKVM"
      },
      "outputs": [],
      "source": [
        "# save fine tuned model\n",
        "model_save_name = 'Mistral7B_fine_tuned_OscarWilde.pt'\n",
        "path = f\"{models_path}/{model_save_name}\"\n",
        "torch.save(model.state_dict(), path)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Xppz6wbncKVM",
        "outputId": "9b421f82-e78f-4721-fd5c-1686b5917d66"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model saved successfully!\n"
          ]
        }
      ],
      "source": [
        "print(\"Model saved successfully!\")"
      ]
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
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      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.10.13"
    },
    "colab": {
      "provenance": [],
      "machine_shape": "hm",
      "gpuType": "T4",
      "include_colab_link": true
    },
    "accelerator": "GPU",
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