tinyllama_finetuning.ipynb

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  "cells": [
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      "metadata": {
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          "name": "stdout",
          "text": [
            "Collecting accelerate\n",
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            "Requirement already satisfied: attrs>=17.3.0 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets->trl) (23.2.0)\n",
            "Requirement already satisfied: frozenlist>=1.1.1 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets->trl) (1.4.1)\n",
            "Requirement already satisfied: multidict<7.0,>=4.5 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets->trl) (6.0.5)\n",
            "Requirement already satisfied: yarl<2.0,>=1.0 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets->trl) (1.9.4)\n",
            "Requirement already satisfied: async-timeout<5.0,>=4.0 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets->trl) (4.0.3)\n",
            "Requirement already satisfied: markdown-it-py>=2.2.0 in /usr/local/lib/python3.10/dist-packages (from rich>=11.1.0->tyro>=0.5.11->trl) (3.0.0)\n",
            "Requirement already satisfied: pygments<3.0.0,>=2.13.0 in /usr/local/lib/python3.10/dist-packages (from rich>=11.1.0->tyro>=0.5.11->trl) (2.16.1)\n",
            "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from jinja2->torch>=1.10.0->accelerate) (2.1.5)\n",
            "Requirement already satisfied: python-dateutil>=2.8.1 in /usr/local/lib/python3.10/dist-packages (from pandas->datasets->trl) (2.8.2)\n",
            "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas->datasets->trl) (2023.4)\n",
            "Requirement already satisfied: mpmath>=0.19 in /usr/local/lib/python3.10/dist-packages (from sympy->torch>=1.10.0->accelerate) (1.3.0)\n",
            "Requirement already satisfied: mdurl~=0.1 in /usr/local/lib/python3.10/dist-packages (from markdown-it-py>=2.2.0->rich>=11.1.0->tyro>=0.5.11->trl) (0.1.2)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.8.1->pandas->datasets->trl) (1.16.0)\n",
            "Installing collected packages: xxhash, shtab, nvidia-nvtx-cu12, nvidia-nvjitlink-cu12, nvidia-nccl-cu12, nvidia-curand-cu12, nvidia-cufft-cu12, nvidia-cuda-runtime-cu12, nvidia-cuda-nvrtc-cu12, nvidia-cuda-cupti-cu12, nvidia-cublas-cu12, docstring-parser, dill, nvidia-cusparse-cu12, nvidia-cudnn-cu12, multiprocess, tyro, nvidia-cusolver-cu12, datasets, bitsandbytes, accelerate, trl, peft\n",
            "Successfully installed accelerate-0.28.0 bitsandbytes-0.43.0 datasets-2.18.0 dill-0.3.8 docstring-parser-0.16 multiprocess-0.70.16 nvidia-cublas-cu12-12.1.3.1 nvidia-cuda-cupti-cu12-12.1.105 nvidia-cuda-nvrtc-cu12-12.1.105 nvidia-cuda-runtime-cu12-12.1.105 nvidia-cudnn-cu12-8.9.2.26 nvidia-cufft-cu12-11.0.2.54 nvidia-curand-cu12-10.3.2.106 nvidia-cusolver-cu12-11.4.5.107 nvidia-cusparse-cu12-12.1.0.106 nvidia-nccl-cu12-2.19.3 nvidia-nvjitlink-cu12-12.4.99 nvidia-nvtx-cu12-12.1.105 peft-0.10.0 shtab-1.7.1 trl-0.8.1 tyro-0.7.3 xxhash-3.4.1\n"
          ]
        }
      ],
      "source": [
        "!pip install accelerate peft bitsandbytes transformers trl\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from huggingface_hub import notebook_login\n",
        "notebook_login()"
      ],
      "metadata": {
        "id": "Onpcbr19tpuo",
        "outputId": "c599e36e-5c48-464d-8a17-f2546948ce4e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 160,
          "referenced_widgets": [
            "fc2a53e5b29146519fd329ced421149b",
            "f04e04bdc5e140a79258e46bfc5e9272",
            "f37ad8c8c16e4357bac5e03dc0cfb711",
            "524b4f62d00a4143976cb86a58832f46",
            "700b7f3e38a84072baf4bc1f0cd4ecc7",
            "f1472595a81146d690ca306fa977d030",
            "69dbcc112fed4948bb5bbe831cbc9db5",
            "30cef894e3df42aa95bc4105a5052639",
            "39a36c5fae4c44f39923db26792b7c85",
            "06ef669944c54a91b0c19b8c01363727",
            "25a154c659e04490ac03ca7290c35f35",
            "146d575eb9344a01b63e6721d304927f",
            "31cf29f7802a4d71b1a5b955da5c458f",
            "d62e104f08444e888858a3f9027dbde5",
            "98614cd4693a407eb04c768fedb546ce",
            "04b4f75702174686866c61ad2b4b59bf",
            "7fb4c669bf534206bbec6350f8e2afa3",
            "eafbfe71614f4b69a2c8571cb3e6e0d2",
            "88911425c171411e838dea21101dc6dd",
            "b82fbb0451d94119934d86841cd80647",
            "19db6ef5cf4a4a81a47a068d7194f5e4",
            "489dba80cdfe45229e7ac452464edf46",
            "29a5b319097b40108ca5ae3d718fb4aa",
            "cb46b9f408c14c22adf0d29d1b130e32",
            "0f9151f442834427aed8907825cdfe18",
            "d8c9f34fb5e94f03822a56e93a68e19d",
            "4f3102da2a1e49c6a8b2275a4916fbb3",
            "fa7b578779f242a89f089ba78c93959a",
            "4053b354912145d2b037895102be32c7",
            "0553df9442de43fb807562b781b9314a",
            "ae13486b3c134e859cc84667ac6683bb",
            "431d1a62332646cba36a31cd8cf798b9"
          ]
        }
      },
      "execution_count": 2,
      "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": "fc2a53e5b29146519fd329ced421149b"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# load the required packages.\n",
        "import torch\n",
        "from datasets import load_dataset, Dataset\n",
        "from peft import LoraConfig, AutoPeftModelForCausalLM\n",
        "from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig, TrainingArguments\n",
        "from trl import SFTTrainer\n",
        "import os"
      ],
      "metadata": {
        "id": "8mnl7YgdtxAb"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "dataset=\"mudata.json\"\n",
        "model_id=\"TinyLlama/TinyLlama-1.1B-Chat-v1.0\"\n",
        "output_model=\"tinyllama-vetdataset-finetuned\""
      ],
      "metadata": {
        "id": "4Ho3JJDLt6Pa"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Data preparation"
      ],
      "metadata": {
        "id": "GnimXYnRvR9C"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import json\n",
        "\n",
        "# Read the original JSON file\n",
        "with open('mudata.json', 'r') as file:\n",
        "    data = json.load(file)\n",
        "\n",
        "# Add IDs to each row\n",
        "for idx, item in enumerate(data, start=1):\n",
        "    item['id'] = idx\n",
        "\n",
        "# Write the updated data to a new file\n",
        "new_file_path = 'updated_data.json'\n",
        "with open(new_file_path, 'w') as new_file:\n",
        "    json.dump(data, new_file, indent=2)\n",
        "\n",
        "print(f'Updated data with IDs written to {new_file_path}')"
      ],
      "metadata": {
        "id": "28VnkJnUlyFa",
        "outputId": "115b3446-5a0a-491e-cbbc-03f0be93109a",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Updated data with IDs written to updated_data.json\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from datasets import load_dataset\n",
        "\n",
        "# Load dataset from the hub\n",
        "dataset = load_dataset(\"json\", data_files=\"updated_data.json\")\n",
        "\n",
        "datasets_train_test = dataset[\"train\"].train_test_split(test_size=150)\n",
        "datasets_train_validation = datasets_train_test[\"train\"].train_test_split(test_size=150)\n",
        "\n",
        "dataset[\"train\"] = datasets_train_validation[\"train\"]\n",
        "dataset[\"validation\"] = datasets_train_validation[\"test\"]\n",
        "dataset[\"test\"] = datasets_train_test[\"test\"]\n",
        "\n",
        "print(f\"Train dataset size: {len(dataset['train'])}\")\n",
        "print(f\"Test dataset size: {len(dataset['test'])}\")\n",
        "\n",
        "# Train dataset size: 14732\n",
        "# Test dataset size: 819"
      ],
      "metadata": {
        "id": "-hRThMyilzRK",
        "outputId": "8d9d2642-d2bc-44ef-b544-740b437ac6b6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 84,
          "referenced_widgets": [
            "5048e83029934e0cbfada9c437115b00",
            "ee8fb6c9f9da4fe9a46cb1eb9ad3f28f",
            "d790078f570f4faa80ff47481b8c7514",
            "ce80d327726445f6bf39ef5bbba06cbc",
            "5ff6c685b7484d32a87937cf0d19e395",
            "6fc88c54b94346cabcc013a984cc91ce",
            "a2e7ccfed6cd48d19c01500de39fe2eb",
            "1b00923c800040f7a707575c1cfa4772",
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            "6f0ca57de3cc40a9910c542e5ef947b3",
            "e0ce4651a7ec4d898c244ece813d69ff"
          ]
        }
      },
      "execution_count": 6,
      "outputs": [
        {
          "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": "5048e83029934e0cbfada9c437115b00"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train dataset size: 5732\n",
            "Test dataset size: 150\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Model the Model (not the base version)"
      ],
      "metadata": {
        "id": "Mnej4AYwvawA"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def get_model_and_tokenizer(mode_id):\n",
        "\n",
        "    tokenizer = AutoTokenizer.from_pretrained(mode_id)\n",
        "    tokenizer.pad_token = tokenizer.eos_token\n",
        "    bnb_config = BitsAndBytesConfig(\n",
        "        load_in_4bit=True, bnb_4bit_quant_type=\"nf4\", bnb_4bit_compute_dtype=\"float16\", bnb_4bit_use_double_quant=True\n",
        "    )\n",
        "    model = AutoModelForCausalLM.from_pretrained(\n",
        "        mode_id, quantization_config=bnb_config, device_map=\"auto\"\n",
        "    )\n",
        "    model.config.use_cache=False\n",
        "    model.config.pretraining_tp=1\n",
        "    return model, tokenizer"
      ],
      "metadata": {
        "id": "WrniDfzlvNd7"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# !pip install -i https://test.pypi.org/simple/bitsandbytes"
      ],
      "metadata": {
        "id": "BaD23-Fev7sK"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model, tokenizer = get_model_and_tokenizer(model_id)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 365,
          "referenced_widgets": [
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            "d3ef8ef913264a9ab9dd89a585de2dce",
            "1c94468803444ad8a79aad9bda43b3e1",
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            "2e282fcc907740c0a90fbb54e47cbad3"
          ]
        },
        "id": "ogWCiBBzvk1C",
        "outputId": "f0434ff5-4057-4987-8670-cad74becfa90"
      },
      "execution_count": 9,
      "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": [
              "tokenizer_config.json:   0%|          | 0.00/1.29k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "a725c927becd4df9b1a4c88a92825e9c"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer.model:   0%|          | 0.00/500k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "bf98b40b4dfc470fbd0bc6cba84d0636"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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              "tokenizer.json:   0%|          | 0.00/1.84M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "362765a47dcb488cb5d4c442864a2797"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "special_tokens_map.json:   0%|          | 0.00/551 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "d7bd98a409d048ae968287d799dcd94a"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "config.json:   0%|          | 0.00/608 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "c2801d0ba91e4071b9d171b9c7ea3066"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "model.safetensors:   0%|          | 0.00/2.20G [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "90d86079a7c34ba3abae83e833fd99a5"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "generation_config.json:   0%|          | 0.00/124 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "11c77fea9a2b4feaab0bb820b49418ef"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from datasets import concatenate_datasets\n",
        "\n",
        "# The maximum total input sequence length after tokenization.\n",
        "# Sequences longer than this will be truncated, sequences shorter will be padded.\n",
        "tokenized_inputs = concatenate_datasets([dataset[\"train\"], dataset[\"test\"]]).map(lambda x: tokenizer(x[\"question\"], truncation=True), batched=True)\n",
        "max_source_length = max([len(x) for x in tokenized_inputs[\"input_ids\"]])\n",
        "print(f\"Max source length: {max_source_length}\")\n",
        "\n",
        "# The maximum total sequence length for target text after tokenization.\n",
        "# Sequences longer than this will be truncated, sequences shorter will be padded.\"\n",
        "tokenized_targets = concatenate_datasets([dataset[\"train\"], dataset[\"test\"]]).map(lambda x: tokenizer(x[\"answer\"], truncation=True), batched=True)\n",
        "max_target_length = max([len(x) for x in tokenized_targets[\"input_ids\"]])\n",
        "print(f\"Max target length: {max_target_length}\")\n"
      ],
      "metadata": {
        "id": "ygt68NwWmMWs",
        "outputId": "90905e86-db4a-4e67-e718-bbfae55e32e3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 116,
          "referenced_widgets": [
            "a7d8f8ba2c244e06b63a522fdcdab4ce",
            "ca4d4e8ed4c949c6b66275bbb4224eb2",
            "a7d341d73ffe43dd9b7acd0b18f64157",
            "78dc56fed9ac454c8315eacd639645b3",
            "cec6cde6c0134201927a6fa24260c0bf",
            "ac17c9d803eb4765a7ef9fbf70734606",
            "e606d9ffd94a4cc18ad567517ceb371f",
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            "4ed631425c2042b5b7d160b28d8fede1",
            "c397930cce004ad186344e304fefaf82",
            "b5bb134f1c174737a9cd733b32b1f944",
            "713894adbd4d4983bcffaa6f4471db8f",
            "2ba1392cb7c0466d9eb315cdb7968203",
            "04d401d98b46461088005f883ced88dc",
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            "337e39f7a77d49a6a8b942562d7f9e9a",
            "60c0e04f91d6438aae9c1fd3cb425ac1"
          ]
        }
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/5882 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "a7d8f8ba2c244e06b63a522fdcdab4ce"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Max source length: 58\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/5882 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "9a46a1458427463e9d0b77917d6ebce5"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Max target length: 189\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def preprocess_function(sample,padding=\"max_length\"):\n",
        "    # add prefix to the input for t5\n",
        "    inputs = [\"answer the following question: \" + item for item in sample[\"question\"]]\n",
        "\n",
        "    # tokenize inputs\n",
        "    model_inputs = tokenizer(inputs, max_length=max_source_length, padding=padding, truncation=True)\n",
        "\n",
        "    # Tokenize targets with the `text_target` keyword argument\n",
        "    labels = tokenizer(text_target=sample[\"answer\"], max_length=max_target_length, padding=padding, truncation=True)\n",
        "\n",
        "    # If we are padding here, replace all tokenizer.pad_token_id in the labels by -100 when we want to ignore\n",
        "    # padding in the loss.\n",
        "    if padding == \"max_length\":\n",
        "        labels[\"input_ids\"] = [\n",
        "            [(l if l != tokenizer.pad_token_id else -100) for l in label] for label in labels[\"input_ids\"]\n",
        "        ]\n",
        "\n",
        "    model_inputs[\"labels\"] = labels[\"input_ids\"]\n",
        "    return model_inputs\n",
        "\n",
        "tokenized_dataset = dataset.map(preprocess_function, batched=True)\n",
        "print(f\"Keys of tokenized dataset: {list(tokenized_dataset['train'].features)}\")"
      ],
      "metadata": {
        "id": "YpCmKE10mp95",
        "outputId": "268667cc-d22c-4a67-fac3-dcb02d93d652",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 130,
          "referenced_widgets": [
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            "cb2866537a694abba431e61e7f80b80b",
            "5f95ec64960e44dea649b30ebfdebb25"
          ]
        }
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/5732 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "d89f402a41af428b92a01903a0d20c52"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/150 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "e7f4c951106644d9a612cd12b8ef0737"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/150 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Keys of tokenized dataset: ['id', 'answer', 'question', 'input_ids', 'attention_mask', 'labels']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tokenized_dataset = tokenized_dataset.map(lambda example: {'text': example['question'] + example['answer']})"
      ],
      "metadata": {
        "id": "1Tb5o7upmTDg",
        "outputId": "ea4587d0-f717-4f23-fa77-d9729a935bfd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 113,
          "referenced_widgets": [
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            "23b5eb357ee34ad195827161780249f0",
            "e142902c1597436dbab07e1f53d7a441"
          ]
        }
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/5732 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "877eb7b08d4d439db77df12d976d71e6"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/150 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "04ef485c904440d48b0febc36feff063"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/150 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "cb6eb579861d47b4ae4be6fcd5b5bfba"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Setting up the LoRA"
      ],
      "metadata": {
        "id": "d2hOfQBZw_YX"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "peft_config = LoraConfig(\n",
        "        r=8, lora_alpha=16, lora_dropout=0.05, bias=\"none\", task_type=\"CAUSAL_LM\"\n",
        "    )"
      ],
      "metadata": {
        "id": "JAGakLbmvtj6"
      },
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "training_arguments = TrainingArguments(\n",
        "        output_dir=output_model,\n",
        "        per_device_train_batch_size=16,\n",
        "        gradient_accumulation_steps=4,\n",
        "        optim=\"paged_adamw_32bit\",\n",
        "        learning_rate=2e-4,\n",
        "        lr_scheduler_type=\"cosine\",\n",
        "        save_strategy=\"epoch\",\n",
        "        logging_steps=10,\n",
        "        num_train_epochs=3,\n",
        "        max_steps=250,\n",
        "        fp16=True,\n",
        "        push_to_hub=True\n",
        "    )"
      ],
      "metadata": {
        "id": "Suz5-CbVxDGm"
      },
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "trainer = SFTTrainer(\n",
        "        model=model,\n",
        "        train_dataset=tokenized_dataset[\"train\"],\n",
        "        peft_config=peft_config,\n",
        "        dataset_text_field=\"text\",\n",
        "        args=training_arguments,\n",
        "        tokenizer=tokenizer,\n",
        "        packing=False,\n",
        "        max_seq_length=1024\n",
        "    )"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 121,
          "referenced_widgets": [
            "393ebbb5f24b498bbe09b78b3b84b505",
            "a6f73004610641d498bf58745ff0e16e",
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            "0ea3c45df3c446b59ebfde9f8f6d5709",
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            "3dc93c52adde4c418e7d69ddafc8b511",
            "3b8a1132a5a848d08756af04324b3fe0"
          ]
        },
        "id": "g7KtgzA8xGVs",
        "outputId": "18f26f6b-cd03-435a-caab-32ce4679241c"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/5732 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "393ebbb5f24b498bbe09b78b3b84b505"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/accelerate/accelerator.py:432: 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"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "trainer.train()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 911
        },
        "id": "nXOXGKK0xU6X",
        "outputId": "9db30642-0be0-4b66-d4b5-d24afb42fa87"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='250' max='250' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [250/250 12:56, Epoch 2/3]\n",
              "    </div>\n",
              "    <table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              " <tr style=\"text-align: left;\">\n",
              "      <th>Step</th>\n",
              "      <th>Training Loss</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>10</td>\n",
              "      <td>2.455200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>20</td>\n",
              "      <td>2.265900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>30</td>\n",
              "      <td>2.067100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>40</td>\n",
              "      <td>1.945400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>50</td>\n",
              "      <td>1.853200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>60</td>\n",
              "      <td>1.786500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>70</td>\n",
              "      <td>1.754900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>80</td>\n",
              "      <td>1.744000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>90</td>\n",
              "      <td>1.727300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>100</td>\n",
              "      <td>1.702200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>110</td>\n",
              "      <td>1.639400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>120</td>\n",
              "      <td>1.644500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>130</td>\n",
              "      <td>1.726800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>140</td>\n",
              "      <td>1.616300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>150</td>\n",
              "      <td>1.608800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>160</td>\n",
              "      <td>1.670000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>170</td>\n",
              "      <td>1.667100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>180</td>\n",
              "      <td>1.624800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>190</td>\n",
              "      <td>1.588800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>200</td>\n",
              "      <td>1.609400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>210</td>\n",
              "      <td>1.587400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>220</td>\n",
              "      <td>1.634600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>230</td>\n",
              "      <td>1.585500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>240</td>\n",
              "      <td>1.657000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>250</td>\n",
              "      <td>1.629100</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "TrainOutput(global_step=250, training_loss=1.751646884918213, metrics={'train_runtime': 780.774, 'train_samples_per_second': 20.492, 'train_steps_per_second': 0.32, 'total_flos': 1.0908383311872e+16, 'train_loss': 1.751646884918213, 'epoch': 2.79})"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Merging the LoRA with the base model"
      ],
      "metadata": {
        "id": "Z1DJz6Zr4Jke"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from peft import AutoPeftModelForCausalLM, PeftModel\n",
        "from transformers import AutoModelForCausalLM\n",
        "import torch\n",
        "import os\n",
        "\n",
        "model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.float16, load_in_8bit=False,\n",
        "                                             device_map=\"auto\",\n",
        "                                             trust_remote_code=True)\n",
        "\n",
        "model_path = \"/content/tinyllama-vetdataset-finetuned\"\n",
        "\n",
        "peft_model = PeftModel.from_pretrained(model, model_path, from_transformers=True, device_map=\"auto\")\n",
        "\n",
        "model = peft_model.merge_and_unload()"
      ],
      "metadata": {
        "id": "V6QKygPu4E91",
        "outputId": "f8f9d2f0-04c9-464b-9614-6e10bf7833b4",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:root:Some parameters are on the meta device device because they were offloaded to the cpu.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.push_to_hub(output_model, use_temp_dir=False)\n",
        "tokenizer.push_to_hub(output_model, use_temp_dir=False)"
      ],
      "metadata": {
        "id": "mudcKgOknSLX",
        "outputId": "290134d5-2d24-44a3-9f1e-c173629d5107",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 150,
          "referenced_widgets": [
            "49d944c1095f40119e909b13534faac1",
            "a97ae723f6524514994d94d70e8aed9a",
            "dda2977745cd4cf7a540042c14fd0a14",
            "e145552bbcc64159a47a9afcbe1ae6fa",
            "2695f0f9d6444a7ab97ddcb5cb4a27ae",
            "3887c4440fa24616a4c231b756ce323b",
            "bc3dca437ade4851b3f61363a70e856e",
            "4ab1119d2061406f8f45163ec945211a",
            "028c7af37a374a0ab5670f6c5f5e0fd6",
            "ea3cb45cc60b4a8cb8f6e9d82798a440",
            "05d44285cb684a808e7403fdc242ef83",
            "36c5b64fba024ee998c1ff3172e698fd",
            "aa4b83b593594e7abb55b4384cca0215",
            "3280b9388ae945cdb6b24c637851033d",
            "63dc205ba0a1447ea2184c80a5a453a0",
            "c471611375474d309a6e02adae9359a4",
            "5a62c596518d44e58459b622f761f55b",
            "7ad36dfa72dc409c98b26a21c187a546",
            "97f57da4e7f140c5b8e36cea74158f92",
            "7ac93b7811c34a1084d79d83613e1999",
            "2f472d062ec04260b7e375ce0bfd0086",
            "96298b68ec3a4b50960d3c42642dafe5"
          ]
        }
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "model.safetensors:   0%|          | 0.00/2.20G [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "49d944c1095f40119e909b13534faac1"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "README.md:   0%|          | 0.00/5.18k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "36c5b64fba024ee998c1ff3172e698fd"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "CommitInfo(commit_url='https://huggingface.co/ahmed807762/tinyllama-vetdataset-finetuned/commit/c76d2fd55b0d01598973a10d1378b19b8178d641', commit_message='Upload tokenizer', commit_description='', oid='c76d2fd55b0d01598973a10d1378b19b8178d641', pr_url=None, pr_revision=None, pr_num=None)"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SU2EIsuI4v50",
        "outputId": "9c085dbf-ff54-481b-b3c8-d5544b33f984"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LlamaForCausalLM(\n",
              "  (model): LlamaModel(\n",
              "    (embed_tokens): Embedding(32003, 2048)\n",
              "    (layers): ModuleList(\n",
              "      (0-21): 22 x LlamaDecoderLayer(\n",
              "        (self_attn): LlamaAttention(\n",
              "          (q_proj): Linear(in_features=2048, out_features=2048, bias=False)\n",
              "          (k_proj): Linear(in_features=2048, out_features=256, bias=False)\n",
              "          (v_proj): Linear(in_features=2048, out_features=256, bias=False)\n",
              "          (o_proj): Linear(in_features=2048, out_features=2048, bias=False)\n",
              "          (rotary_emb): LlamaRotaryEmbedding()\n",
              "        )\n",
              "        (mlp): LlamaMLP(\n",
              "          (gate_proj): Linear(in_features=2048, out_features=5632, bias=False)\n",
              "          (up_proj): Linear(in_features=2048, out_features=5632, bias=False)\n",
              "          (down_proj): Linear(in_features=5632, out_features=2048, 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=2048, out_features=32003, bias=False)\n",
              ")"
            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Inference from the LLM"
      ],
      "metadata": {
        "id": "n1nOANDaynhU"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import GenerationConfig\n",
        "from time import perf_counter\n",
        "\n",
        "def generate_response(user_input):\n",
        "\n",
        "  prompt = formatted_prompt(user_input)\n",
        "\n",
        "  inputs = tokenizer([prompt], return_tensors=\"pt\")\n",
        "  generation_config = GenerationConfig(penalty_alpha=0.6,do_sample = True,\n",
        "      top_k=5,temperature=0.5,repetition_penalty=1.2,\n",
        "      max_new_tokens=12,pad_token_id=tokenizer.eos_token_id\n",
        "  )\n",
        "  start_time = perf_counter()\n",
        "\n",
        "  inputs = tokenizer(prompt, return_tensors=\"pt\").to('cuda')\n",
        "\n",
        "  outputs = model.generate(**inputs, generation_config=generation_config)\n",
        "  print(tokenizer.decode(outputs[0], skip_special_tokens=True))\n",
        "  output_time = perf_counter() - start_time\n",
        "  print(f\"Time taken for inference: {round(output_time,2)} seconds\")"
      ],
      "metadata": {
        "id": "mWKVO_5j5pSk"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def formatted_prompt(question)-> str:\n",
        "    return f\"<|im_start|>user\\n{question}<|im_end|>\\n<|im_start|>assistant:\""
      ],
      "metadata": {
        "id": "yIT0IuT9HtEp"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def print_color_space(hex_color):\n",
        "    def hex_to_rgb(hex_color):\n",
        "        hex_color = hex_color.lstrip('#')\n",
        "        return tuple(int(hex_color[i:i+2], 16) for i in (0, 2, 4))\n",
        "    r, g, b = hex_to_rgb(hex_color)\n",
        "    print(f'{hex_color}: \\033[48;2;{r};{g};{b}m           \\033[0m')"
      ],
      "metadata": {
        "id": "cbRpHaNwHthY"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "generate_response(user_input='Light Orange color')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_FeisorS5pZD",
        "outputId": "83830eb7-8cf2-450b-dd19-cb310c12237b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "user\n",
            "Light Orange color \n",
            " assistant: #f0901a \n",
            " light\n",
            "Time taken for inference: 0.35 seconds\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print_color_space('#f0901a ')\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QvuqsFit5pcU",
        "outputId": "461399d8-0eac-4f80-80be-777432aeedf8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "#f0901a : \u001b[48;2;240;144;26m           \u001b[0m\n"
          ]
        }
      ]
    }
  ]
}