HS_science_A_student_simulator.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "<a href=\"https://colab.research.google.com/github/PaulSZH95/hs_peer_support_chem/blob/main/HS_science_A_student_simulator.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ],
      "metadata": {
        "id": "Oaj2MoAJZ_d-"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AhvVBVVhLZm4"
      },
      "source": [
        "# Instruct-tuning Open LLaMA using sciq and HydraLM\n",
        "\n",
        "### This tutorial is inspired by:\n",
        "- DeepLearningAI [webinar](https://www.youtube.com/watch?v=eTieetk2dSw&t)\n",
        "- [Speakers' repository](https://github.com/FourthBrain/Building-with-Instruction-Tuned-LLMs-A-Step-by-Step-Guide)\n",
        "\n",
        "#### Motivation:\n",
        "Harness the power of LLM to simulate a high school peer tutor (universal) for chemistry students.\n",
        "\n",
        "#### [SCIq](https://arxiv.org/pdf/1707.06209.pdf):\n",
        "Dataset created in 2017 leveraging a large corpus of 28 science study textbooks from various online learning resources, including CK-12 and OpenStax, which are shared under a Creative Commons License.\n",
        "The dataset is said to provide better question and answer qualities.\n",
        "\n",
        "#### [Chemistry_dataset_standardized](https://docs.google.com/document/d/1YKDRCu7M9mflWrxKc1HeFs2HBWk4HXVrrOsLrIn6EXM/edit):\n",
        "Dataset created by hydraml for the purpose of finetuning LLAMA 2 to achieve Chatgpt4 capabilities.\n",
        "\n",
        "#### [LORA](https://arxiv.org/pdf/2106.09685.pdf):\n",
        "- Any matrix can be decompiosed to 2 matrics: matrics of A x B can be a product of Matrix A.r X r.B\n",
        "In this case, r is the rank and you can speed findtuning up by finetuning from small r up to larger r. (this is a optuna job)\n",
        "\n",
        "#### [QLORA](https://huggingface.co/docs/optimum/concept_guides/quantization):\n",
        "- Now you do it with a quantized mechanism using a datatype of 4-bit NormalFloat (NF)\n",
        "\n",
        "#### [PEFT](https://huggingface.co/blog/peft):\n",
        "A library for you to inject layers before/after the transformer layers.\n",
        "\n",
        "#### Summary:\n",
        "We are only training the injected layers (adapters) and we are using LORA technique and 4bit quantization to speed things up."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FuXIFTFapAMI",
        "outputId": "437e3284-cc98-4a65-c297-5f7a4fb8ccbf"
      },
      "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",
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            "\u001b[?25h  Building wheel for trl (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 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",
            "  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[32m1.3/1.3 MB\u001b[0m \u001b[31m7.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h"
          ]
        }
      ],
      "source": [
        "!pip install -q -U git+https://github.com/lvwerra/trl.git\n",
        "!pip install -q -U bitsandbytes\n",
        "!pip install -U -q git+https://github.com/huggingface/transformers@de9255de27abfcae4a1f816b904915f0b1e23cd9\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 -U sentencepiece"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MJ-5idQwzvg-"
      },
      "source": [
        "Let's look at our dataset to get an idea of what we're working with!"
      ]
    },
    {
      "cell_type": "code",
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              "version_minor": 0,
              "model_id": "a295759f62994cd597dfa8bcd199faf8"
            }
          },
          "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": "7ab3d87192504864895b228df199f2a4"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating train split: 0 examples [00:00, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "91efc623dd2c41c6a711b837a2ddf27c"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "import datasets\n",
        "from datasets import load_dataset, DatasetDict\n",
        "\n",
        "sciq = load_dataset(\"sciq\")\n",
        "chem_stand = load_dataset(\"HydraLM/chemistry_dataset_standardized\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1WrJ_vsWquxr",
        "outputId": "5f8bb354-bca6-4892-f6ea-06a712b1c59b"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "DatasetDict({\n",
            "    train: Dataset({\n",
            "        features: ['question', 'distractor3', 'distractor1', 'distractor2', 'correct_answer', 'support'],\n",
            "        num_rows: 11679\n",
            "    })\n",
            "    validation: Dataset({\n",
            "        features: ['question', 'distractor3', 'distractor1', 'distractor2', 'correct_answer', 'support'],\n",
            "        num_rows: 1000\n",
            "    })\n",
            "    test: Dataset({\n",
            "        features: ['question', 'distractor3', 'distractor1', 'distractor2', 'correct_answer', 'support'],\n",
            "        num_rows: 1000\n",
            "    })\n",
            "})\n",
            "\n",
            "\n",
            "\n",
            "DatasetDict({\n",
            "    train: Dataset({\n",
            "        features: ['message', 'message_type', 'message_id', 'conversation_id'],\n",
            "        num_rows: 40000\n",
            "    })\n",
            "})\n"
          ]
        }
      ],
      "source": [
        "# metadata of datasets\n",
        "print(sciq)\n",
        "print(\"\\n\\n\")\n",
        "print(chem_stand)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## HydraLM/chemistry_dataset_standardized\n",
        "See first 4 output of dataset to realise that the Question is the first entrant of the message key, the answer be the subsequent entrant followed by new question and answers.\n",
        "\n",
        "We will restructure the dataset for our use case\n",
        "\n"
      ],
      "metadata": {
        "id": "zvt6iFdpPsuI"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# first 2 entrant is displayed.\n",
        "print(chem_stand[\"train\"][0])\n",
        "print('\\n')\n",
        "print(chem_stand[\"train\"][1])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JWB7W5VFQfQF",
        "outputId": "31efcde4-7619-466a-9ec5-8abcfd76fb78"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'message': 'Calculate the standard enthalpy of formation of C2H6(g) given the following information: \\n\\nC(s) + O2(g) → CO2(g); ΔH° = -393.5 kJ/mol \\n\\nH2(g) + 1/2 O2(g) → H2O(l); ΔH° = -285.8 kJ/mol \\n\\n2C(s) + 3H2(g) → C2H6(g); ΔH° = ?', 'message_type': 'input', 'message_id': 0, 'conversation_id': 0}\n",
            "\n",
            "\n",
            "{'message': \"To calculate the standard enthalpy of formation of C2H6(g), we need to use Hess's Law, which states that the total enthalpy change of a reaction is the sum of the enthalpy changes of its individual steps.\\n\\nFirst, we need to manipulate the given reactions to match the target reaction:\\n\\nTarget reaction: 2C(s) + 3H2(g) → C2H6(g)\\n\\n1. 2(C(s) + O2(g) → CO2(g)); ΔH° = 2(-393.5 kJ/mol) = -787 kJ/mol\\n2. 3(H2(g) + 1/2 O2(g) → H2O(l)); ΔH° = 3(-285.8 kJ/mol) = -857.4 kJ/mol\\n3. 2CO2(g) + 3H2O(l) → C2H6(g) + 4O2(g); ΔH° = ?\\n\\nNow, we can add reactions 1 and 2 and reverse reaction 3 to obtain the target reaction:\\n\\n2C(s) + 2O2(g) + 3H2(g) + 3/2 O2(g) → 2CO2(g) + 3H2O(l) + C2H6(g) + 4O2(g)\\n\\nSimplifying, we get:\\n\\n2C(s) + 3H2(g) → C2H6(g)\\n\\nNow, we can use Hess's Law to find the enthalpy change for the target reaction:\\n\\nΔH°(target) = ΔH°(1) + ΔH°(2) - ΔH°(3)\\n\\nΔH°(target) = -787 kJ/mol + (-857.4 kJ/mol) - ΔH°(3)\\n\\nΔH°(3) = -787 kJ/mol - (-857.4 kJ/mol)\\n\\nΔH°(3) = 70.4 kJ/mol\\n\\nSo, the standard enthalpy of formation of C2H6(g) is 70.4 kJ/mol.\", 'message_type': 'output', 'message_id': 1, 'conversation_id': 0}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# now we restructure dataset\n",
        "import datasets\n",
        "questions = []\n",
        "response = []\n",
        "for i,j in enumerate(chem_stand[\"train\"][\"message\"]):\n",
        "  if i % 2 == 0:\n",
        "    questions.append(j)\n",
        "  else:\n",
        "    response.append(j)\n",
        "chem_message_dict = datasets.Dataset.from_dict({\"questions\":questions,\"answers\":response})\n",
        "chem_dataset = DatasetDict({\n",
        "    \"train\": chem_message_dict\n",
        "})"
      ],
      "metadata": {
        "id": "QNrQY_rJLrfn"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# split chem_stand to train 80, val 10, test 10\n",
        "train_test = chem_dataset[\"train\"].train_test_split(test_size=0.2)\n",
        "test_valid = train_test['test'].train_test_split(test_size=0.5)\n",
        "chem_dataset_split = DatasetDict({\n",
        "    'train': train_test['train'],\n",
        "    'test': test_valid['test'],\n",
        "    'validation': test_valid['train']})\n",
        "chem_dataset_split"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "G60RtW5YMRpm",
        "outputId": "92160077-9284-4bdc-8fb5-b92c938112c3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DatasetDict({\n",
              "    train: Dataset({\n",
              "        features: ['questions', 'answers'],\n",
              "        num_rows: 16000\n",
              "    })\n",
              "    test: Dataset({\n",
              "        features: ['questions', 'answers'],\n",
              "        num_rows: 2000\n",
              "    })\n",
              "    validation: Dataset({\n",
              "        features: ['questions', 'answers'],\n",
              "        num_rows: 2000\n",
              "    })\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# train set is pretty large for both sciq and chem_stand, not suitable for free colab training. Will reduce it to N entries\n",
        "\n",
        "def subsampling(dataset_obj_keyed, n_start,n_end):\n",
        "  return datasets.Dataset.from_dict(dataset_obj_keyed[n_start:n_end])\n"
      ],
      "metadata": {
        "id": "N49A9GLcOjp9"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train_2000 = subsampling(chem_dataset_split[\"train\"],100,2100)\n",
        "train_2000_chem = DatasetDict({\n",
        "    'train': train_2000,\n",
        "    'test': chem_dataset_split['test'],\n",
        "    'validation': chem_dataset_split['validation']})\n",
        "train_2000_chem"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "i81Ju2HsOjwD",
        "outputId": "0fa044df-6ae5-4dca-80f5-f725bfe9daa8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DatasetDict({\n",
              "    train: Dataset({\n",
              "        features: ['questions', 'answers'],\n",
              "        num_rows: 2000\n",
              "    })\n",
              "    test: Dataset({\n",
              "        features: ['questions', 'answers'],\n",
              "        num_rows: 2000\n",
              "    })\n",
              "    validation: Dataset({\n",
              "        features: ['questions', 'answers'],\n",
              "        num_rows: 2000\n",
              "    })\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_2k = subsampling(sciq[\"train\"],100,2100)\n",
        "train_2000_sciq = DatasetDict({\n",
        "    'train': train_2k,\n",
        "    'test': sciq['test'],\n",
        "    'validation': sciq['validation']})\n",
        "train_2000_sciq"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "H-3GidcAYJvq",
        "outputId": "cd674f7e-a428-4e28-bc98-03200fbfd03d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DatasetDict({\n",
              "    train: Dataset({\n",
              "        features: ['question', 'distractor3', 'distractor1', 'distractor2', 'correct_answer', 'support'],\n",
              "        num_rows: 2000\n",
              "    })\n",
              "    test: Dataset({\n",
              "        features: ['question', 'distractor3', 'distractor1', 'distractor2', 'correct_answer', 'support'],\n",
              "        num_rows: 1000\n",
              "    })\n",
              "    validation: Dataset({\n",
              "        features: ['question', 'distractor3', 'distractor1', 'distractor2', 'correct_answer', 'support'],\n",
              "        num_rows: 1000\n",
              "    })\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# intruction formating for chem_standard\n",
        "import random\n",
        "def chem_format(example):\n",
        "  question = example.get(\"questions\",\"\")\n",
        "  answer = example.get(\"answers\",\"\")\n",
        "  starter = \"Below is a question your task is to provide an answer \\n\"\n",
        "  question_part = f\"### Question: {question} \\n\\n\"\n",
        "  answer_part = f\"### Answer: {question} \\n\\n\"\n",
        "  full_prompt = f\"{starter}{question_part}{answer_part}\"\n",
        "  return {\"text\": full_prompt}\n",
        "\n",
        "def sciq_format(example):\n",
        "  question = example.get(\"question\",\"\")\n",
        "  op1 = example.get('distractor3',\"\")\n",
        "  op2 = example.get('distractor2',\"\")\n",
        "  op3 = example.get('distractor1',\"\")\n",
        "  ans = example.get('correct_answer',\"\")\n",
        "  options = [op1,op2,op3,ans]\n",
        "  random.shuffle(options)\n",
        "  options = \",\".join(options)\n",
        "  support = example.get(\"support\",\"\")\n",
        "  starter = f\"Below is a question with options options provided. Only 1 out of 4 options will be correct. your task is to chose a correct answer from given options and provide support \\n\\n\"\n",
        "  question_part = f\"### Question: {question} \\n\\n\"\n",
        "  options_part = f\"### Options: {options} \\n\\n\"\n",
        "  answer_part = f\"### Answer: {ans} \\n\\n\"\n",
        "  support_part = f\"### Support: {support}\"\n",
        "  full_prompt = f\"{starter}{question_part}{options_part}{answer_part}{support_part}\"\n",
        "  return {\"text\": full_prompt}"
      ],
      "metadata": {
        "id": "ASU7npWfOj4E"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "chem_formated = train_2000_chem.map(chem_format)\n",
        "sciq_formated = train_2000_sciq.map(sciq_format)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 209,
          "referenced_widgets": [
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      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/2000 [00:00<?, ? examples/s]"
            ],
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        {
          "output_type": "display_data",
          "data": {
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              "Map:   0%|          | 0/2000 [00:00<?, ? examples/s]"
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            "application/vnd.jupyter.widget-view+json": {
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        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/2000 [00:00<?, ? examples/s]"
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            "application/vnd.jupyter.widget-view+json": {
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        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/2000 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
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        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/1000 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
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        {
          "output_type": "display_data",
          "data": {
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              "Map:   0%|          | 0/1000 [00:00<?, ? examples/s]"
            ],
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            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(chem_formated)\n",
        "print(chem_formated[\"train\"][0][\"text\"])\n",
        "print(sciq_formated)\n",
        "print(sciq_formated[\"train\"][0][\"text\"])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wvgiI9jAOj7I",
        "outputId": "59a99506-e197-4286-e473-37acf8f0a9f5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "DatasetDict({\n",
            "    train: Dataset({\n",
            "        features: ['questions', 'answers', 'text'],\n",
            "        num_rows: 2000\n",
            "    })\n",
            "    test: Dataset({\n",
            "        features: ['questions', 'answers', 'text'],\n",
            "        num_rows: 2000\n",
            "    })\n",
            "    validation: Dataset({\n",
            "        features: ['questions', 'answers', 'text'],\n",
            "        num_rows: 2000\n",
            "    })\n",
            "})\n",
            "Below is a question your task is to provide an answer \n",
            "### Question: What is the concentration of protein in a given sample of milk using spectrophotometry technique? \n",
            "\n",
            "### Answer: What is the concentration of protein in a given sample of milk using spectrophotometry technique? \n",
            "\n",
            "\n",
            "DatasetDict({\n",
            "    train: Dataset({\n",
            "        features: ['question', 'distractor3', 'distractor1', 'distractor2', 'correct_answer', 'support', 'text'],\n",
            "        num_rows: 2000\n",
            "    })\n",
            "    test: Dataset({\n",
            "        features: ['question', 'distractor3', 'distractor1', 'distractor2', 'correct_answer', 'support', 'text'],\n",
            "        num_rows: 1000\n",
            "    })\n",
            "    validation: Dataset({\n",
            "        features: ['question', 'distractor3', 'distractor1', 'distractor2', 'correct_answer', 'support', 'text'],\n",
            "        num_rows: 1000\n",
            "    })\n",
            "})\n",
            "Below is a question with options options provided. Only 1 out of 4 options will be correct. your task is to chose a correct answer from given options and provide support \n",
            "\n",
            "### Question: In a chemical reaction, the amounts of reactants and products will be constant when what state is attained? \n",
            "\n",
            "### Options: saturation,peak,equilibrium,homogeneity \n",
            "\n",
            "### Answer: equilibrium \n",
            "\n",
            "### Support: The amount of reactants and products do not have to be equal. However, after equilibrium is attained, the amounts of reactants and products will be constant.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# combine datasets\n",
        "\n",
        "def list_to_text_datadict(list_you_made):\n",
        "  random.shuffle(list_you_made)\n",
        "  return datasets.Dataset.from_dict({\"text\": list_you_made})\n",
        "\n",
        "\n",
        "comb_train = sciq_formated['train'][\"text\"] + chem_formated['train'][\"text\"]\n",
        "comb_test = sciq_formated['test'][\"text\"] + chem_formated['test'][\"text\"]\n",
        "comb_val  = sciq_formated['validation'][\"text\"] + chem_formated['validation'][\"text\"]\n",
        "\n",
        "# shuffle\n",
        "\n",
        "comb_dataset = DatasetDict({\n",
        "    'train': list_to_text_datadict(comb_train),\n",
        "    'test': list_to_text_datadict(comb_test),\n",
        "    'validation': list_to_text_datadict(comb_val)})\n",
        "comb_dataset"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ieRhzi_iZswk",
        "outputId": "05c05ce1-371c-451c-a683-6ab5059068c7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DatasetDict({\n",
              "    train: Dataset({\n",
              "        features: ['text'],\n",
              "        num_rows: 4000\n",
              "    })\n",
              "    test: Dataset({\n",
              "        features: ['text'],\n",
              "        num_rows: 3000\n",
              "    })\n",
              "    validation: Dataset({\n",
              "        features: ['text'],\n",
              "        num_rows: 3000\n",
              "    })\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 113,
          "referenced_widgets": [
            "15de36826d6043cb87ff4c93e4f2b0d7",
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            "8bb7856c77d7442491e64de9a9519e63",
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            "a14eb9fec91f4b77849d063a5be961ac",
            "fb6d849c4c2e42649d5059e80acfbc2f",
            "cde2af5b3a1d497f9af73a0f17e16823",
            "95a0925a784442889cea9f81314fbab0",
            "666870ee42df44a89bb4915a4031f05a",
            "2b086e62136e42e4989b4c509055461d"
          ]
        },
        "id": "leQlkMEwjcsj",
        "outputId": "529c5037-67df-4d18-d670-459a81dd77eb"
      },
      "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": "15de36826d6043cb87ff4c93e4f2b0d7"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "from huggingface_hub import notebook_login\n",
        "notebook_login()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 241,
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        },
        "id": "EZPFn5qvaYWO",
        "outputId": "7ff602f6-e457-4c89-a80c-0dc33779b0a0"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)lve/main/config.json:   0%|          | 0.00/790 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "4451bd89486d4c8db7b5702f8ad17c40"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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              "model_id": "86cc20ba4ae54e23a5a754944b91a1b1"
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "647948b56e864e4bbc986505a38ea7b4"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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              "Downloading (…)l-00001-of-00002.bin:   0%|          | 0.00/9.98G [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "d608ad6fb8a14ccf92f5c634037bdc2d"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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              "Downloading (…)l-00002-of-00002.bin:   0%|          | 0.00/3.50G [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "7982e74ea320416788d1755f30eba00f"
            }
          },
          "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,
              "model_id": "4ffeb5107b794460859b07dd9dc307bc"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)neration_config.json:   0%|          | 0.00/132 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "d31f50b0a72345178abbf27a53d63185"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "import torch\n",
        "import transformers\n",
        "from peft import LoraConfig\n",
        "from transformers import AutoModelForCausalLM, BitsAndBytesConfig, AutoTokenizer\n",
        "\n",
        "model_id = \"h2oai/h2ogpt-gm-oasst1-en-2048-open-llama-7b-preview-700bt\"\n",
        "\n",
        "qlora_config = LoraConfig(\n",
        "    r=16,\n",
        "    lora_alpha=32,\n",
        "    lora_dropout=0.05,\n",
        "    bias=\"none\",\n",
        "    task_type=\"CAUSAL_LM\"\n",
        ")\n",
        "\n",
        "bnb_config = BitsAndBytesConfig(\n",
        "    load_in_4bit=True,\n",
        "    bnb_4bit_use_double_quant=True,\n",
        "    bnb_4bit_quant_type=\"nf4\",\n",
        "    bnb_4bit_compute_dtype=torch.bfloat16\n",
        ")\n",
        "\n",
        "base_model = AutoModelForCausalLM.from_pretrained(\n",
        "    model_id,\n",
        "    quantization_config=bnb_config,\n",
        "    use_auth_token=True\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "29jtzmb4Vw83",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 130,
          "referenced_widgets": [
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            "12a5554bc01a49b29885fbb5d3026c68",
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            "6e59338070724a9faba3c24369036886",
            "78d7393dcb73473aa62c86ff63698c74",
            "32c62c2b90184239973288b3d4bb8748",
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            "cffd7f616e6e4935a52dfcc32bccbdb5",
            "d534313974e945f8ab9d0390a9eac17b",
            "62fd19aafb694c7987aa0169481963aa",
            "74ad578af4484b5682c3283a8cb0774a",
            "7808f6ca87b54e96b6a4fa8ea97e782e",
            "3f9f425b6e734f09a24b6d14d880d7bd",
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            "275fad5ff7a542b0a7afd2a27ed56641",
            "4de40ba799a04afca0a0dbccdbeaef32",
            "eef45957af0e4cd4b9562306cba36b57",
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            "ee2c369635c24388831019ea8ee6e6fe",
            "39a807c9b04041268d7c087548279a53",
            "8e54394029014dd29845c45a5e984115",
            "9bf9d870d0514f71910489d76fbb34ab",
            "5ad51562dc4b4c928dc2a05e3db0f20b",
            "dfd2a016d63d46c08c70492972de94cd",
            "fcb9b99d69984c4d9d41c35284870600",
            "5f7683283fc946999d796e1f629592a5",
            "9e5190bdd8a0489ba228af60d3ef8020"
          ]
        },
        "outputId": "322e25f8-df50-47c4-dd0a-801c2056428b"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading tokenizer.model:   0%|          | 0.00/534k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "d4a4a28e60bc4ef4a8efbc4419badff7"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)cial_tokens_map.json:   0%|          | 0.00/249 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "7808f6ca87b54e96b6a4fa8ea97e782e"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)okenizer_config.json:   0%|          | 0.00/744 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "4d0f2151adaf40ed87928630c537be33"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ],
      "source": [
        "from transformers import LlamaTokenizer\n",
        "\n",
        "tokenizer = LlamaTokenizer.from_pretrained(model_id)\n",
        "tokenizer.add_special_tokens({'pad_token': '[PAD]'})"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "DnrBaSmg68Nr",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "43907583-5f84-4ac2-c3dc-3aa3f5ef09bd"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "LlamaForCausalLM(\n",
            "  (model): LlamaModel(\n",
            "    (embed_tokens): Embedding(32000, 4096, padding_idx=0)\n",
            "    (layers): ModuleList(\n",
            "      (0-31): 32 x LlamaDecoderLayer(\n",
            "        (self_attn): LlamaAttention(\n",
            "          (q_proj): Linear4bit(in_features=4096, out_features=4096, bias=False)\n",
            "          (k_proj): Linear4bit(in_features=4096, out_features=4096, bias=False)\n",
            "          (v_proj): Linear4bit(in_features=4096, out_features=4096, bias=False)\n",
            "          (o_proj): Linear4bit(in_features=4096, out_features=4096, bias=False)\n",
            "          (rotary_emb): LlamaRotaryEmbedding()\n",
            "        )\n",
            "        (mlp): LlamaMLP(\n",
            "          (gate_proj): Linear4bit(in_features=4096, out_features=11008, bias=False)\n",
            "          (down_proj): Linear4bit(in_features=11008, out_features=4096, bias=False)\n",
            "          (up_proj): Linear4bit(in_features=4096, out_features=11008, bias=False)\n",
            "          (act_fn): SiLUActivation()\n",
            "        )\n",
            "        (input_layernorm): LlamaRMSNorm()\n",
            "        (post_attention_layernorm): LlamaRMSNorm()\n",
            "      )\n",
            "    )\n",
            "    (norm): LlamaRMSNorm()\n",
            "  )\n",
            "  (lm_head): Linear(in_features=4096, out_features=32000, bias=False)\n",
            ")\n"
          ]
        }
      ],
      "source": [
        "print(base_model)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## SFFT Model:"
      ],
      "metadata": {
        "id": "8NEUBUxbC42p"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "QLboLYUFyuXJ",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 81,
          "referenced_widgets": [
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            "a15a6bde6ae3495181666e48b652c8a7",
            "2baf5f34f44a4945893a11216a37a1bb",
            "537b542e40884c83ab4a1df111d45226",
            "918de861638b49e3b1219fd9702b7b37",
            "d47fc6ef3a3d405bbe1c26be02e648de",
            "f7eefe657d164572bbc59aeb73390562",
            "17b2dd58a4dd4ab0be91131827202af1",
            "f6900cb538eb417aad4654b0565649e8",
            "6dde8bd096fa477aac36df352983df6e",
            "04e8d670d2a9446eb09358d129ab525e",
            "512cadf09e50495b8324dac98534ed67",
            "bf72b2dcc80b4c7faa4b257e78d9720b",
            "0f593fafc6a946e39f117b8f935eec38",
            "c8af420ed5b842cbad81292c98faab15",
            "1f0ac92cc00145aca92ff729103fddda",
            "fc30de391c374c3d92a0e7a20b49ef32",
            "d7bc855725b5423e9b439d0e8e32ef14"
          ]
        },
        "outputId": "a414ae21-f535-4088-d232-b0a6b114417a"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/4000 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "295250d6e15546b39a105bf684f29e54"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/3000 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "17b2dd58a4dd4ab0be91131827202af1"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "from trl import SFTTrainer\n",
        "\n",
        "supervised_finetuning_trainer = SFTTrainer(\n",
        "    base_model,\n",
        "    train_dataset=comb_dataset[\"train\"],\n",
        "    eval_dataset=comb_dataset[\"test\"],\n",
        "    args=transformers.TrainingArguments(\n",
        "        per_device_train_batch_size=1,\n",
        "        gradient_accumulation_steps=4,\n",
        "        learning_rate=2e-4,\n",
        "        max_steps=1000,\n",
        "        output_dir=\"./tune-LLama\",\n",
        "        optim=\"paged_adamw_8bit\",\n",
        "        fp16=True,\n",
        "    ),\n",
        "    tokenizer=tokenizer,\n",
        "    peft_config=qlora_config,\n",
        "    dataset_text_field=\"text\",\n",
        "    max_seq_length=512\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Observe adapters injected into the LLAMA model.\n",
        "#### Everything about the original model is frozen"
      ],
      "metadata": {
        "id": "lyLsHwxXDGC_"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "base_model"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8-pffUEE3dJt",
        "outputId": "e4d4dda6-560f-4f2b-faa9-faed4f13927d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LlamaForCausalLM(\n",
              "  (model): LlamaModel(\n",
              "    (embed_tokens): Embedding(32000, 4096, padding_idx=0)\n",
              "    (layers): ModuleList(\n",
              "      (0-31): 32 x LlamaDecoderLayer(\n",
              "        (self_attn): LlamaAttention(\n",
              "          (q_proj): Linear4bit(\n",
              "            in_features=4096, out_features=4096, bias=False\n",
              "            (lora_dropout): ModuleDict(\n",
              "              (default): Dropout(p=0.05, 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): Linear4bit(in_features=4096, out_features=4096, bias=False)\n",
              "          (v_proj): Linear4bit(\n",
              "            in_features=4096, out_features=4096, bias=False\n",
              "            (lora_dropout): ModuleDict(\n",
              "              (default): Dropout(p=0.05, 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",
              "          (o_proj): Linear4bit(in_features=4096, out_features=4096, bias=False)\n",
              "          (rotary_emb): LlamaRotaryEmbedding()\n",
              "        )\n",
              "        (mlp): LlamaMLP(\n",
              "          (gate_proj): Linear4bit(in_features=4096, out_features=11008, bias=False)\n",
              "          (down_proj): Linear4bit(in_features=11008, out_features=4096, bias=False)\n",
              "          (up_proj): Linear4bit(in_features=4096, out_features=11008, bias=False)\n",
              "          (act_fn): SiLUActivation()\n",
              "        )\n",
              "        (input_layernorm): LlamaRMSNorm()\n",
              "        (post_attention_layernorm): LlamaRMSNorm()\n",
              "      )\n",
              "    )\n",
              "    (norm): LlamaRMSNorm()\n",
              "  )\n",
              "  (lm_head): Linear(in_features=4096, out_features=32000, bias=False)\n",
              ")"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "465rybz1ltM7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 173
        },
        "outputId": "0d1e5ea1-5be3-4862-9be2-a0829e53d024"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='1000' max='1000' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [1000/1000 1:06:19, Epoch 1/1]\n",
              "    </div>\n",
              "    <table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              " <tr style=\"text-align: left;\">\n",
              "      <th>Step</th>\n",
              "      <th>Training Loss</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>500</td>\n",
              "      <td>0.833300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1000</td>\n",
              "      <td>0.773200</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "TrainOutput(global_step=1000, training_loss=0.8032535400390625, metrics={'train_runtime': 3987.1041, 'train_samples_per_second': 1.003, 'train_steps_per_second': 0.251, 'total_flos': 1.230655490064384e+16, 'train_loss': 0.8032535400390625, 'epoch': 1.0})"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ],
      "source": [
        "supervised_finetuning_trainer.train()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "repo = 'supramantest/hs_peer_support_chem'\n",
        "NEW_MODEL_NAME = 'chem_tutor'\n",
        "supervised_finetuning_trainer.model.save_pretrained(NEW_MODEL_NAME)\n",
        "lora_config = LoraConfig.from_pretrained(NEW_MODEL_NAME)"
      ],
      "metadata": {
        "id": "BF-aD_qd4T5w"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "peft_model"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bFThwjJeEwIG",
        "outputId": "105d4fec-be1f-4c04-891c-41559bdf1d23"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "PeftModelForCausalLM(\n",
              "  (base_model): LoraModel(\n",
              "    (model): LlamaForCausalLM(\n",
              "      (model): LlamaModel(\n",
              "        (embed_tokens): Embedding(32000, 4096, padding_idx=0)\n",
              "        (layers): ModuleList(\n",
              "          (0-31): 32 x LlamaDecoderLayer(\n",
              "            (self_attn): LlamaAttention(\n",
              "              (q_proj): Linear4bit(\n",
              "                in_features=4096, out_features=4096, bias=False\n",
              "                (lora_dropout): ModuleDict(\n",
              "                  (default): Dropout(p=0.05, 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): Linear4bit(in_features=4096, out_features=4096, bias=False)\n",
              "              (v_proj): Linear4bit(\n",
              "                in_features=4096, out_features=4096, bias=False\n",
              "                (lora_dropout): ModuleDict(\n",
              "                  (default): Dropout(p=0.05, 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",
              "              (o_proj): Linear4bit(in_features=4096, out_features=4096, bias=False)\n",
              "              (rotary_emb): LlamaRotaryEmbedding()\n",
              "            )\n",
              "            (mlp): LlamaMLP(\n",
              "              (gate_proj): Linear4bit(in_features=4096, out_features=11008, bias=False)\n",
              "              (down_proj): Linear4bit(in_features=11008, out_features=4096, bias=False)\n",
              "              (up_proj): Linear4bit(in_features=4096, out_features=11008, bias=False)\n",
              "              (act_fn): SiLUActivation()\n",
              "            )\n",
              "            (input_layernorm): LlamaRMSNorm()\n",
              "            (post_attention_layernorm): LlamaRMSNorm()\n",
              "          )\n",
              "        )\n",
              "        (norm): LlamaRMSNorm()\n",
              "      )\n",
              "      (lm_head): Linear(in_features=4096, out_features=32000, bias=False)\n",
              "    )\n",
              "  )\n",
              ")"
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from huggingface_hub import notebook_login\n",
        "notebook_login()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 113,
          "referenced_widgets": [
            "f46d4bd65b514fb3819ace0df64a9b06",
            "e87ff676bf8843dbb60aab2c4f1c6a15",
            "d321480dc2354edeb395b1623b2d151a",
            "e354c4f629ee4a0a9206c64cfc3ad8ca",
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            "d58a3b18bf3443f99413d7040f745175",
            "09a3d45f26574db3bec9256213cbd4ff",
            "c531f39f94234199a0b8b969c7763ac3",
            "0e656c7a34d14d2e981cfe0870880d83",
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            "12b68fc0100d494697e825f792e6f627",
            "a9b53da111a54c2d99f63f52f7a33985",
            "487c8c29299642409e3fecd53aef3603",
            "258f0a6b768d46498ba3b04b35e5d534",
            "4b30daf584ea45e8b4cdba0c478fd037",
            "77e615b1859644f2a5d589b29a56d07c",
            "e74a7844f33f4d0988b4686e34b4f894",
            "4c349c8435f441009aacc06821e0d48b",
            "7a2208c4f9a64ffda30c7857fb98904c",
            "cda82a5b7c3f44d1b94720f3502228e1",
            "d167e0b3ecb24a78aa701361aa51b093",
            "dd7d9408e07a467daa53f282be1bcfc8",
            "cd8e5bf279a949d69b4197bea314a1ce",
            "4473855a2ec745cbb5fc1f5bf4203a6b",
            "6e3c032db41b483c918d3a9c623b4f41",
            "fd11c283280c4591bf05d07e617a00a6",
            "47f1f78fd01749afbd52fd8d43ee8503"
          ]
        },
        "id": "ojerk9LlM4Sb",
        "outputId": "ee7036d4-2e70-487c-dd82-a3e35862894b"
      },
      "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": "f46d4bd65b514fb3819ace0df64a9b06"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# you must follow exactly , less the repo name, as the rapid changes to the libraries might break stuff.\n",
        "# raise issue on github if anything breaks\n",
        "from peft import PeftModel, get_peft_model\n",
        "repo = 'supramantest/hs_peer_support_chem'\n",
        "peft_model = get_peft_model(base_model, lora_config)\n",
        "peft_model.push_to_hub(repo)\n",
        "tokenizer.push_to_hub(repo)\n",
        "comb_dataset.push_to_hub(repo)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lm7zT2wm4Wmq",
        "outputId": "82788ca1-bfeb-499e-8510-89bc6ceb96da"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "CommitInfo(commit_url='https://huggingface.co/supramantest/hs_peer_support_chem/commit/ab643fc28841ceee7e74f639fc15ef93a464f06f', commit_message='Upload model', commit_description='', oid='ab643fc28841ceee7e74f639fc15ef93a464f06f', pr_url=None, pr_revision=None, pr_num=None)"
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "_7bMw4dsEEg5"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "hA8JsJMXc3V6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 369,
          "referenced_widgets": [
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            "text/plain": [
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      ],
      "source": [
        "# inference part can be run separately from the training part\n",
        "from peft import get_peft_model\n",
        "import torch\n",
        "import transformers\n",
        "from peft import LoraConfig\n",
        "from transformers import AutoModelForCausalLM, BitsAndBytesConfig, LlamaTokenizer\n",
        "\n",
        "\n",
        "lora_config = LoraConfig.from_pretrained(\"supramantest/hs_peer_support_chem\")\n",
        "bnb_config = BitsAndBytesConfig(\n",
        "    load_in_4bit=True,\n",
        "    bnb_4bit_use_double_quant=True,\n",
        "    bnb_4bit_quant_type=\"nf4\",\n",
        "    bnb_4bit_compute_dtype=torch.bfloat16\n",
        ")\n",
        "\n",
        "tokenizer = LlamaTokenizer.from_pretrained(\"supramantest/hs_peer_support_chem\")\n",
        "model = AutoModelForCausalLM.from_pretrained(\n",
        "    lora_config.base_model_name_or_path,\n",
        "    quantization_config=bnb_config,\n",
        "    device_map={\"\":0})"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "3dSHYWLEdb8l"
      },
      "outputs": [],
      "source": [
        "model = get_peft_model(model, lora_config)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ykykdbHPdiAN"
      },
      "outputs": [],
      "source": [
        "from IPython.display import display, Markdown\n",
        "\n",
        "def make_inference(prompt, context = None):\n",
        "  inputs = tokenizer(prompt, return_tensors=\"pt\", return_token_type_ids=False).to(\"cuda:0\")\n",
        "  outputs = model.generate(**inputs, max_new_tokens=100)\n",
        "  display(Markdown((tokenizer.decode(outputs[0], skip_special_tokens=True))))\n",
        "  outputs = model.generate(**inputs, max_new_tokens=50)\n",
        "  print(\"---- NON-INSTRUCT-TUNED-MODEL ----\")\n",
        "  display(Markdown((tokenizer.decode(outputs[0], skip_special_tokens=True))))"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "### Answer: neurotransmitters ### Support: When a nerve impulse reaches the end of an axon, the axon releases chemicals called neurotransmitters ."
      ],
      "metadata": {
        "id": "6vBCLN99Ki9R"
      },
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        "prompt1 = \"Below is a question with options options provided. Only 1 out of 4 options will be correct. your task is to chose a correct answer from given options and provide support ### Question: When a nerve impulse reaches the end of an axon, what are the chemicals released by the axon called? ### Options: neurons,electrolytes,neurotransmitters,receptors\"\n",
        "\n",
        "make_inference(prompt1)"
      ],
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          "height": 424
        },
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        {
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          "data": {
            "text/plain": [
              "<IPython.core.display.Markdown object>"
            ],
            "text/markdown": "Below is a question with options options provided. Only 1 out of 4 options will be correct. your task is to chose a correct answer from given options and provide support ### Question: When a nerve impulse reaches the end of an axon, what are the chemicals released by the axon called? ### Options: neurons,electrolytes,neurotransmitters,receptors\n\n### Answer: Neurotransmitters\n\nThe correct answer is neurotransmitters.\n\nNeurotransmitters are chemicals released by neurons to communicate with other neurons. They are responsible for transmitting signals across the synapse, which is the gap between two neurons.\n\n### Explanation:\n\nThe correct answer is neurotransmitters. Neurotransmitters are chemicals released by neurons to communicate with other neurons. They are responsible for transmitting signals"
          },
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        },
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          "name": "stdout",
          "text": [
            "---- NON-INSTRUCT-TUNED-MODEL ----\n"
          ]
        },
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          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Markdown object>"
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            "text/markdown": "Below is a question with options options provided. Only 1 out of 4 options will be correct. your task is to chose a correct answer from given options and provide support ### Question: When a nerve impulse reaches the end of an axon, what are the chemicals released by the axon called? ### Options: neurons,electrolytes,neurotransmitters,receptors\n\n### Answer: Neurotransmitters\n\nThe correct answer is neurotransmitters.\n\nNeurotransmitters are chemicals released by neurons to communicate with other neurons. They are responsible for transmitting signals across the synapse"
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