finetunedBLOOM.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WE5GJ6s7y0Xo"
      },
      "source": [
        "# Fine-tuning a BLOOM-based ad generation model using `peft`, `transformers` and `bitsandbytes`\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TfBzP8gWzkpv"
      },
      "source": [
        "### Install requirements\n",
        "\n",
        "First, run the cells below to install the requirements:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "otj46qRbtpnd",
        "outputId": "b11a25ac-1104-43a6-ece1-0a7fc75b0316"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m119.8/119.8 MB\u001b[0m \u001b[31m5.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m314.1/314.1 kB\u001b[0m \u001b[31m18.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m39.9/39.9 MB\u001b[0m \u001b[31m8.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m64.9/64.9 kB\u001b[0m \u001b[31m3.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m134.8/134.8 kB\u001b[0m \u001b[31m7.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m21.3/21.3 MB\u001b[0m \u001b[31m43.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
            "cudf-cu12 24.4.1 requires pyarrow<15.0.0a0,>=14.0.1, but you have pyarrow 17.0.0 which is incompatible.\n",
            "google-colab 1.0.0 requires requests==2.31.0, but you have requests 2.32.3 which is incompatible.\n",
            "ibis-framework 8.0.0 requires pyarrow<16,>=2, but you have pyarrow 17.0.0 which is incompatible.\u001b[0m\u001b[31m\n",
            "\u001b[0m  Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
            "  Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n",
            "  Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m7.8/7.8 MB\u001b[0m \u001b[31m59.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  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"
          ]
        }
      ],
      "source": [
        "!pip install -q bitsandbytes datasets accelerate loralib\n",
        "!pip install -q -U git+https://github.com/huggingface/transformers@de9255de27abfcae4a1f816b904915f0b1e23cd9\n",
        "!pip install -q git+https://github.com/huggingface/peft.git"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FOtwYRI3zzXI"
      },
      "source": [
        "### Model loading\n",
        "\n",
        "Loading the bloom-1b7 model! Also loading the bigscience/tokenizer which is the tokenizer for all of the BLOOM models."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "cg3fiQOvmI3Q",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 371,
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        },
        "outputId": "6fe1c761-4ed3-4896-99ba-9afcad434d7e"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:89: 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": [
              "config.json:   0%|          | 0.00/715 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "c6a9f09d6c814d62a9815b2046a84e67"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "model.safetensors:   0%|          | 0.00/3.44G [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "9b8f42cc2466449ba495a84d506d4282"
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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            ],
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              "version_major": 2,
              "version_minor": 0,
              "model_id": "3f15da604b7d447aac7edfb8ad840c09"
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          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer.json:   0%|          | 0.00/14.5M [00:00<?, ?B/s]"
            ],
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              "version_major": 2,
              "version_minor": 0,
              "model_id": "f6ad09c33e784e28bbe676a244441a5e"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "special_tokens_map.json:   0%|          | 0.00/85.0 [00:00<?, ?B/s]"
            ],
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              "version_major": 2,
              "version_minor": 0,
              "model_id": "cf0f931bc8b24327a7ce3f87e4801a30"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "import os\n",
        "os.environ[\"CUDA_VISIBLE_DEVICES\"]=\"0\"\n",
        "import torch\n",
        "import torch.nn as nn\n",
        "import bitsandbytes as bnb\n",
        "from transformers import AutoTokenizer, AutoConfig, AutoModelForCausalLM\n",
        "\n",
        "model = AutoModelForCausalLM.from_pretrained(\n",
        "    \"bigscience/bloom-1b7\",\n",
        "    torch_dtype=torch.float16,\n",
        "    load_in_8bit=True,\n",
        "    device_map='auto',\n",
        ")\n",
        "\n",
        "tokenizer = AutoTokenizer.from_pretrained(\"bigscience/tokenizer\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9fTSZntA1iUG"
      },
      "source": [
        "### Post-processing on the model\n",
        "For post-processing the 8-bit model to enable training, we need to freeze all layers and cast the layer normalization to `float32` for stability. Additionally, we should cast the output of the last layer to `float32` for similar stability reasons."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "T-gy-LxM0yAi"
      },
      "outputs": [],
      "source": [
        "for param in model.parameters():\n",
        "  param.requires_grad = False  # freeze the model - train adapters later\n",
        "  if param.ndim == 1:\n",
        "    # cast the small parameters (e.g. layernorm) to fp32 for stability\n",
        "    param.data = param.data.to(torch.float32)\n",
        "\n",
        "model.gradient_checkpointing_enable()  # reduce number of stored activations\n",
        "model.enable_input_require_grads()\n",
        "\n",
        "class CastOutputToFloat(nn.Sequential):\n",
        "  def forward(self, x): return super().forward(x).to(torch.float32)\n",
        "model.lm_head = CastOutputToFloat(model.lm_head)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KwOTr7B3NlM3"
      },
      "source": [
        "### Apply LoRA"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "4W1j6lxaNnxC"
      },
      "outputs": [],
      "source": [
        "def print_trainable_parameters(model):\n",
        "    \"\"\"\n",
        "    Prints the number of trainable parameters in the model.\n",
        "    \"\"\"\n",
        "    trainable_params = 0\n",
        "    all_param = 0\n",
        "    for _, param in model.named_parameters():\n",
        "        all_param += param.numel()\n",
        "        if param.requires_grad:\n",
        "            trainable_params += param.numel()\n",
        "    print(\n",
        "        f\"trainable params: {trainable_params} || all params: {all_param} || trainable%: {100 * trainable_params / all_param}\"\n",
        "    )"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4iwHGzKBN6wk",
        "outputId": "32c2c251-db7c-4ab4-f4a5-0bf077f8df2c"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "trainable params: 3145728 || all params: 1725554688 || trainable%: 0.18230242262828822\n"
          ]
        }
      ],
      "source": [
        "from peft import LoraConfig, get_peft_model\n",
        "\n",
        "config = LoraConfig(\n",
        "    r=16,\n",
        "    lora_alpha=32,\n",
        "    target_modules=[\"query_key_value\"],\n",
        "    lora_dropout=0.05,\n",
        "    bias=\"none\",\n",
        "    task_type=\"CAUSAL_LM\"\n",
        ")\n",
        "\n",
        "model = get_peft_model(model, config)\n",
        "print_trainable_parameters(model)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QdjWif4CVXR6"
      },
      "source": [
        "### Preprocessing"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Loading our synthetic dataset from Hugging Face [link](https://huggingface.co/datasets/AryanPrakhar/marketing_mail_data)"
      ],
      "metadata": {
        "id": "OmB8KTexh4rf"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "id": "AQ_HCYruWIHU"
      },
      "outputs": [],
      "source": [
        "import transformers\n",
        "from datasets import load_dataset\n",
        "\n",
        "dataset_name = \"AryanPrakhar/marketing_mail_data\"\n",
        "product_name = \"product\"\n",
        "product_desc = \"description\"\n",
        "product_ad = \"marketing_email\""
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dataset = load_dataset(dataset_name)\n",
        "print(dataset)\n",
        "print(dataset['train'][0])"
      ],
      "metadata": {
        "id": "Ap6OE6R_W2QZ",
        "outputId": "aab9225e-1c68-473f-e474-94a12af1abbd",
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        }
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading data:   0%|          | 0.00/7.00k [00:00<?, ?B/s]"
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        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating train split:   0%|          | 0/10 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "6607856b17094d9d81bd2d928e98a47d"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "DatasetDict({\n",
            "    train: Dataset({\n",
            "        features: ['product', 'description', 'marketing_email'],\n",
            "        num_rows: 10\n",
            "    })\n",
            "})\n",
            "{'product': '**SmartPlant**', 'description': 'Self-watering, app-connected plant pot', 'marketing_email': \"Subject: Never Worry About Watering Your Plants Again!\\n\\nHi [Name],\\n\\nSay goodbye to wilting plants and hello to SmartPlant! 🌱\\n\\nOur self-watering, app-connected plant pot makes plant care effortless. Just set it up, and let SmartPlant take care of the rest.\\n\\n**Why you'll love it:**\\n- **Never Over/Under Water Again**: Your plant gets the perfect amount of water.\\n- **Stay Informed**: Get real-time updates and tips via the app.\\n- **Perfect for Busy Lifestyles**: Save time and keep your plants thriving.\\n\\nReady to transform your plant care routine?\\n\\n[Discover SmartPlant Now!]\\n\\nBest regards,\\n[Your Company Name]\"}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "We want to put our data in the form:\n",
        "\n",
        "```\n",
        "Below is a product and description, please write a marketing email for this product.\n",
        "\n",
        "### Product\n",
        "PRODUCT NAME\n",
        "\n",
        "### Description:\n",
        "DESCRIPTION\n",
        "\n",
        "### Marketing Email:\n",
        "OUR EMAIL HERE\n",
        "```\n"
      ],
      "metadata": {
        "id": "yHpB8xf_iA20"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def generate_prompt(product: str, description: str, marketing_email: str) -> str:\n",
        "  prompt = f\"Below is a product and description, please write a marketing email for this product.\\n\\n### Product:\\n{product}\\n### Description:\\n{description}\\n\\n### Marketing Email:\\n{marketing_email}\"\n",
        "  return prompt\n",
        "\n",
        "mapped_dataset = dataset.map(lambda samples: tokenizer(generate_prompt(samples['product'], samples['description'], samples['marketing_email'])))"
      ],
      "metadata": {
        "id": "MWZk1U-kXwZF",
        "outputId": "31ff7b13-5c38-4d3a-d3c4-1768affb413d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49,
          "referenced_widgets": [
            "9d557aa6673846b5a868c3f5ff8193d7",
            "1586695812fd40c9a606a3a3a56e18bb",
            "84b783d723b44de080f33fd69a26faff",
            "68b16765dcab4b82a8be8cde808e7d0c",
            "da6b38ce14554ab586308651fcb8cfd6",
            "af82b4bde42545318258bbe87ea71833",
            "212996c2b1a74ebabd17494589a22559",
            "5db450b926c84c22a74b484df84f6492",
            "4e8cc27f23a74acabbd87376b6cbdbb2",
            "03906c8838874d31818497b49fde13c6",
            "ccc9878fb50b4f89be7392a8865e52ca"
          ]
        }
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/10 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "9d557aa6673846b5a868c3f5ff8193d7"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "trainer = transformers.Trainer(\n",
        "    model=model,\n",
        "    train_dataset=mapped_dataset[\"train\"],\n",
        "    args=transformers.TrainingArguments(\n",
        "        per_device_train_batch_size=4,\n",
        "        gradient_accumulation_steps=4,\n",
        "        warmup_steps=100,\n",
        "        max_steps=100,\n",
        "        learning_rate=1e-3,\n",
        "        fp16=True,\n",
        "        logging_steps=1,\n",
        "        output_dir='outputs'\n",
        "    ),\n",
        "    data_collator=transformers.DataCollatorForLanguageModeling(tokenizer, mlm=False)\n",
        ")\n",
        "model.config.use_cache = False  # silence the warnings. Please re-enable for inference!\n",
        "trainer.train()"
      ],
      "metadata": {
        "id": "yWe9MBT4aT2v",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "448a2550-778b-4f13-89e3-300c8c964dea"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/transformers/optimization.py:411: FutureWarning: This implementation of AdamW is deprecated and will be removed in a future version. Use the PyTorch implementation torch.optim.AdamW instead, or set `no_deprecation_warning=True` to disable this warning\n",
            "  warnings.warn(\n",
            "You're using a PreTrainedTokenizerFast tokenizer. Please note that with a fast tokenizer, using the `__call__` method is faster than using a method to encode the text followed by a call to the `pad` method to get a padded encoding.\n",
            "/usr/local/lib/python3.10/dist-packages/torch/utils/checkpoint.py:464: UserWarning: torch.utils.checkpoint: the use_reentrant parameter should be passed explicitly. In version 2.4 we will raise an exception if use_reentrant is not passed. use_reentrant=False is recommended, but if you need to preserve the current default behavior, you can pass use_reentrant=True. Refer to docs for more details on the differences between the two variants.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/bitsandbytes/autograd/_functions.py:316: UserWarning: MatMul8bitLt: inputs will be cast from torch.float32 to float16 during quantization\n",
            "  warnings.warn(f\"MatMul8bitLt: inputs will be cast from {A.dtype} to float16 during quantization\")\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='100' max='100' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [100/100 04:10, Epoch 66/100]\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>1</td>\n",
              "      <td>2.341700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2</td>\n",
              "      <td>0.773300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3</td>\n",
              "      <td>1.567600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4</td>\n",
              "      <td>1.559800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>5</td>\n",
              "      <td>0.786700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>6</td>\n",
              "      <td>2.329500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>7</td>\n",
              "      <td>2.300600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>8</td>\n",
              "      <td>0.741800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>9</td>\n",
              "      <td>1.541900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>10</td>\n",
              "      <td>1.483500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>11</td>\n",
              "      <td>0.722300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>12</td>\n",
              "      <td>2.154700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>13</td>\n",
              "      <td>2.098000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>14</td>\n",
              "      <td>0.691400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>15</td>\n",
              "      <td>1.355200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>16</td>\n",
              "      <td>1.326700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>17</td>\n",
              "      <td>0.614100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>18</td>\n",
              "      <td>1.846900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>19</td>\n",
              "      <td>1.792000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>20</td>\n",
              "      <td>0.590900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>21</td>\n",
              "      <td>1.104600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>22</td>\n",
              "      <td>1.064600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>23</td>\n",
              "      <td>0.509100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>24</td>\n",
              "      <td>1.454700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>25</td>\n",
              "      <td>1.386300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>26</td>\n",
              "      <td>0.449700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>27</td>\n",
              "      <td>0.794400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>28</td>\n",
              "      <td>0.761200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>29</td>\n",
              "      <td>0.397900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>30</td>\n",
              "      <td>1.048600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>31</td>\n",
              "      <td>0.967100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>32</td>\n",
              "      <td>0.300800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>33</td>\n",
              "      <td>0.551500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>34</td>\n",
              "      <td>0.529900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>35</td>\n",
              "      <td>0.258700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>36</td>\n",
              "      <td>0.699600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>37</td>\n",
              "      <td>0.643600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>38</td>\n",
              "      <td>0.174400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>39</td>\n",
              "      <td>0.368000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>40</td>\n",
              "      <td>0.312800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>41</td>\n",
              "      <td>0.154200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>42</td>\n",
              "      <td>0.391000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>43</td>\n",
              "      <td>0.323300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>44</td>\n",
              "      <td>0.088200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>45</td>\n",
              "      <td>0.197100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>46</td>\n",
              "      <td>0.158900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>47</td>\n",
              "      <td>0.059500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>48</td>\n",
              "      <td>0.183600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>49</td>\n",
              "      <td>0.140300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>50</td>\n",
              "      <td>0.034200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>51</td>\n",
              "      <td>0.088100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>52</td>\n",
              "      <td>0.059400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>53</td>\n",
              "      <td>0.034600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>54</td>\n",
              "      <td>0.069000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>55</td>\n",
              "      <td>0.055800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>56</td>\n",
              "      <td>0.015600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>57</td>\n",
              "      <td>0.029600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>58</td>\n",
              "      <td>0.029400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>59</td>\n",
              "      <td>0.014300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>60</td>\n",
              "      <td>0.032600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>61</td>\n",
              "      <td>0.034300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>62</td>\n",
              "      <td>0.009000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>63</td>\n",
              "      <td>0.018600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>64</td>\n",
              "      <td>0.020300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>65</td>\n",
              "      <td>0.011200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>66</td>\n",
              "      <td>0.025800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>67</td>\n",
              "      <td>0.022300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>68</td>\n",
              "      <td>0.006700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>69</td>\n",
              "      <td>0.013700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>70</td>\n",
              "      <td>0.012700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>71</td>\n",
              "      <td>0.007200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>72</td>\n",
              "      <td>0.019100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>73</td>\n",
              "      <td>0.017200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>74</td>\n",
              "      <td>0.006000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>75</td>\n",
              "      <td>0.010700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>76</td>\n",
              "      <td>0.011800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>77</td>\n",
              "      <td>0.005500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>78</td>\n",
              "      <td>0.015200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>79</td>\n",
              "      <td>0.015100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>80</td>\n",
              "      <td>0.004500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>81</td>\n",
              "      <td>0.009900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>82</td>\n",
              "      <td>0.009300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>83</td>\n",
              "      <td>0.006200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>84</td>\n",
              "      <td>0.015300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>85</td>\n",
              "      <td>0.013800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>86</td>\n",
              "      <td>0.004500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>87</td>\n",
              "      <td>0.012100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>88</td>\n",
              "      <td>0.009800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>89</td>\n",
              "      <td>0.003900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>90</td>\n",
              "      <td>0.013200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>91</td>\n",
              "      <td>0.012500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>92</td>\n",
              "      <td>0.004300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>93</td>\n",
              "      <td>0.009300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>94</td>\n",
              "      <td>0.009200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>95</td>\n",
              "      <td>0.003800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>96</td>\n",
              "      <td>0.012200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>97</td>\n",
              "      <td>0.011700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>98</td>\n",
              "      <td>0.003600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>99</td>\n",
              "      <td>0.009000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>100</td>\n",
              "      <td>0.007700</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "TrainOutput(global_step=100, training_loss=0.4502290259953588, metrics={'train_runtime': 260.4031, 'train_samples_per_second': 6.144, 'train_steps_per_second': 0.384, 'total_flos': 958733689847808.0, 'train_loss': 0.4502290259953588, 'epoch': 66.67})"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Duak7T_B3VpJ"
      },
      "source": [
        "## Sharing adapters on HF"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "HUGGING_FACE_USER_NAME = \"AryanPrakhar\""
      ],
      "metadata": {
        "id": "BjHE2wryglHS"
      },
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {
        "id": "DpYr24pR8T_0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 113,
          "referenced_widgets": [
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          ]
        },
        "outputId": "143bd1ba-c3d8-4459-c2a4-a2930ec6f2e5"
      },
      "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": {
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              "model_id": "8096fe161414416fae0d3bf0c52122e0"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "from huggingface_hub import notebook_login\n",
        "notebook_login()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {
        "id": "VxB6UV5XAvvP",
        "colab": {
          "base_uri": "https://localhost:8080/",
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          ]
        },
        "outputId": "ba04a9bd-9d6c-4679-ec26-1bc22c959f6c"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "adapter_model.safetensors:   0%|          | 0.00/12.6M [00:00<?, ?B/s]"
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          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "CommitInfo(commit_url='https://huggingface.co/AryanPrakhar/marketMail/commit/1fac95b8edb6e85e560118c2e5288ed4e5d85b4c', commit_message='Upload model', commit_description='', oid='1fac95b8edb6e85e560118c2e5288ed4e5d85b4c', pr_url=None, pr_revision=None, pr_num=None)"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
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          },
          "metadata": {},
          "execution_count": 25
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      ],
      "source": [
        "model_name = \"marketMail\"\n",
        "\n",
        "model.push_to_hub(f\"{HUGGING_FACE_USER_NAME}/{model_name}\", use_auth_token=True)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "S65GcxNGA9kz"
      },
      "source": [
        "## Load adapters from the Hub\n",
        "\n",
        "You can also directly load adapters from the Hub using the commands below:"
      ]
    },
    {
      "cell_type": "code",
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        "outputId": "21fa595f-0e0d-43ce-c0ba-0ab824f67b43"
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      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "adapter_config.json:   0%|          | 0.00/641 [00:00<?, ?B/s]"
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        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer_config.json:   0%|          | 0.00/222 [00:00<?, ?B/s]"
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          "metadata": {}
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        {
          "output_type": "display_data",
          "data": {
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          "metadata": {}
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        {
          "output_type": "display_data",
          "data": {
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          "metadata": {}
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        {
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          "data": {
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              "adapter_model.safetensors:   0%|          | 0.00/12.6M [00:00<?, ?B/s]"
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              "model_id": "0470dc6008a24f31bf634ca3f28bfcd0"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "import torch\n",
        "from peft import PeftModel, PeftConfig\n",
        "from transformers import AutoModelForCausalLM, AutoTokenizer\n",
        "\n",
        "peft_model_id = f\"{HUGGING_FACE_USER_NAME}/{model_name}\"\n",
        "config = PeftConfig.from_pretrained(peft_model_id)\n",
        "model = AutoModelForCausalLM.from_pretrained(config.base_model_name_or_path, return_dict=True, load_in_8bit=True, device_map='auto')\n",
        "tokenizer = AutoTokenizer.from_pretrained(config.base_model_name_or_path)\n",
        "\n",
        "# Load the Lora model\n",
        "model = PeftModel.from_pretrained(model, peft_model_id)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MHYljmTjj5wX"
      },
      "source": [
        "## Inference\n",
        "\n",
        "You can then directly use the trained model or the model that you have loaded from the 🤗 Hub for inference as you would do it usually in `transformers`."
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Testing it!"
      ],
      "metadata": {
        "id": "WuppXQXWA27h"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from IPython.display import display, Markdown\n",
        "\n",
        "def make_inference(product, description):\n",
        "  batch = tokenizer(f\"Below is a product and description, please write a marketing email for this product.\\n\\n### Product:\\n{product}\\n### Description:\\n{description}\\n\\n### Marketing Email:\\n\", return_tensors='pt')\n",
        "\n",
        "  with torch.cuda.amp.autocast():\n",
        "    output_tokens = model.generate(**batch, max_new_tokens=200)\n",
        "\n",
        "  display(Markdown((tokenizer.decode(output_tokens[0], skip_special_tokens=True))))"
      ],
      "metadata": {
        "id": "whuXcSPsluc5"
      },
      "execution_count": 27,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "your_product_name_here = \"Scade.pro\"\n",
        "your_product_description_here = \"Convert AI into revenue, engagement, and new projects\"\n",
        "\n",
        "make_inference(your_product_name_here, your_product_description_here)"
      ],
      "metadata": {
        "id": "E5_d4r1Mm6ud",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 512
        },
        "outputId": "8e4982f7-8670-4201-9adf-1cff51cffdc6"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Markdown object>"
            ],
            "text/markdown": "Below is a product and description, please write a marketing email for this product.\n\n### Product:\nScade.pro\n### Description:\nConvert AI into revenue, engagement, and new projects\n\n### Marketing Email:\nSubject: Transform Your Business with Scade.pro! 📋\n\nHi [Recipient's Name],\n\nImagine turning your business into a scalable, recurring revenue stream. With Scade.pro, you can!\n\n- Convert AI into revenue, engagement, and new projects\n\n- Get the ROI you expect\n\nNo more guessing what your business needs to thrive.\n\n👉 Click here to discover Scade.pro!\n\nBest,\n[Your Name]  \nDiscover Scade.pro Team,\n[Your Company]\n\nP.S. Don't miss out on the biggest technology transformation of your lifetime! Click here to learn more!\n\nBest,\n[Your Name]  \nDiscover Scade.pro Team,\n[Your Company]\nP.S. Don't miss out on the biggest technology transformation of your lifetime! \n\nBest,\n[Your Name]  \nDiscover Scade.pro Team,\n[Your Company]\nP.S. Don't miss out on the biggest technology transformation of"
          },
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        }
      ]
    }
  ],
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