Fine_tune_Florence_2.ipynb

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      "metadata": {
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      "source": [
        "<a href=\"https://colab.research.google.com/github/merveenoyan/smol-vision/blob/main/Fine_tune_Florence_2.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Fine-tuning Florence-2 on DocVQA\n",
        "\n",
        "In this notebook, we will fine-tune Florence-2 by MSFT, a new vision language model capable of various tasks, on document question answering.\n",
        "\n",
        "Let's start by installing the dependencies and loading the dataset."
      ],
      "metadata": {
        "id": "KVmYbqqUSlCH"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install -q datasets flash_attn timm einops"
      ],
      "metadata": {
        "id": "Y6b1dvjgYXOD",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c1213f07-cd8d-4aaf-eb80-f7936faacce0"
      },
      "execution_count": null,
      "outputs": [
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          "name": "stdout",
          "text": [
            "\u001b[?25l     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/547.8 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K     \u001b[91m━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m61.4/547.8 kB\u001b[0m \u001b[31m2.3 MB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0m\r\u001b[2K     \u001b[91m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[91m╸\u001b[0m \u001b[32m542.7/547.8 kB\u001b[0m \u001b[31m9.1 MB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0m\r\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m547.8/547.8 kB\u001b[0m \u001b[31m7.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m2.6/2.6 MB\u001b[0m \u001b[31m69.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m2.3/2.3 MB\u001b[0m \u001b[31m84.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m43.2/43.2 kB\u001b[0m \u001b[31m2.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m40.8/40.8 MB\u001b[0m \u001b[31m41.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m116.3/116.3 kB\u001b[0m \u001b[31m15.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m64.9/64.9 kB\u001b[0m \u001b[31m9.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m194.1/194.1 kB\u001b[0m \u001b[31m24.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m134.8/134.8 kB\u001b[0m \u001b[31m19.6 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[31m72.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Building wheel for flash_attn (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
            "cudf-cu12 24.4.1 requires pyarrow<15.0.0a0,>=14.0.1, but you have pyarrow 16.1.0 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 16.1.0 which is incompatible.\u001b[0m\u001b[31m\n",
            "\u001b[0m"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from datasets import load_dataset\n",
        "\n",
        "data = load_dataset(\"HuggingFaceM4/DocumentVQA\")"
      ],
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          "base_uri": "https://localhost:8080/",
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      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading readme:   0%|          | 0.00/806 [00:00<?, ?B/s]"
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        {
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            ],
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        {
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            ],
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          "metadata": {}
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        {
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            ],
            "application/vnd.jupyter.widget-view+json": {
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          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "We can load the model using `AutoModelForCausalLM` and the processor using `AutoProcessor`  classes of transformers library. Note that we need to pass `trust_remote_code` as `True` since this model is not a transformers model."
      ],
      "metadata": {
        "id": "flp13B-8Myjf"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import AutoModelForCausalLM, AutoProcessor\n",
        "import torch\n",
        "\n",
        "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
        "\n",
        "model = AutoModelForCausalLM.from_pretrained(\"microsoft/Florence-2-base-ft\", trust_remote_code=True, revision='refs/pr/6').to(device)\n",
        "processor = AutoProcessor.from_pretrained(\"microsoft/Florence-2-base-ft\", trust_remote_code=True, revision='refs/pr/6')\n"
      ],
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            "3ea5b0b3d9e5425bb40b2c6b5564e450",
            "80b4c581e5eb4079bb07ea70dcf7048f",
            "5deccbfe24964245a1610a1fe53257f5"
          ]
        },
        "outputId": "df27c2d2-b760-4a51-a61c-d455900d1abb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "config.json:   0%|          | 0.00/2.43k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "bb33cee017ec477eba399456a2082783"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "configuration_florence2.py:   0%|          | 0.00/15.1k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "8576b5f889e54d9a98a5921eb708cb9a"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "modeling_florence2.py:   0%|          | 0.00/127k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "9c98014dce814b3fbf0f12216fb60f38"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "pytorch_model.bin:   0%|          | 0.00/464M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "cc1b25e0c465435bad16f117d6815e7d"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "preprocessor_config.json:   0%|          | 0.00/806 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "1c725853b8204d0fbb3cac00f73b949c"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "processing_florence2.py:   0%|          | 0.00/46.4k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "ba8d0a523f654d3a9e44d9946a6c2c2f"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer_config.json:   0%|          | 0.00/34.0 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "82a0969a863a4c93a3eb44ec2e25262a"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "vocab.json:   0%|          | 0.00/1.10M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "2f65048a2c50488b8dbb109d525a6f10"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer.json:   0%|          | 0.00/1.36M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "a10d017e34c24844bb71b4ef9399df36"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "A new version of the following files was downloaded from https://huggingface.co/microsoft/Florence-2-base-ft:\n",
            "- configuration_florence2.py\n",
            ". Make sure to double-check they do not contain any added malicious code. To avoid downloading new versions of the code file, you can pin a revision.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "torch.cuda.empty_cache()"
      ],
      "metadata": {
        "id": "1V9BcVQMycgq"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Let's do inference with our dataset first to see how the model performs already with our dataset before fine-tuning."
      ],
      "metadata": {
        "id": "uau7ALyLNBVF"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Function to run the model on an example\n",
        "def run_example(task_prompt, text_input, image):\n",
        "    prompt = task_prompt + text_input\n",
        "\n",
        "    # Ensure the image is in RGB mode\n",
        "    if image.mode != \"RGB\":\n",
        "        image = image.convert(\"RGB\")\n",
        "\n",
        "    inputs = processor(text=prompt, images=image, return_tensors=\"pt\").to(device)\n",
        "    generated_ids = model.generate(\n",
        "        input_ids=inputs[\"input_ids\"],\n",
        "        pixel_values=inputs[\"pixel_values\"],\n",
        "        max_new_tokens=1024,\n",
        "        num_beams=3\n",
        "    )\n",
        "    generated_text = processor.batch_decode(generated_ids, skip_special_tokens=False)[0]\n",
        "    parsed_answer = processor.post_process_generation(generated_text, task=task_prompt, image_size=(image.width, image.height))\n",
        "    return parsed_answer\n"
      ],
      "metadata": {
        "id": "kvSWOIHsab7o"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "for idx in range(3):\n",
        "  print(run_example(\"DocVQA\", 'What do you see in this image?', data['train'][idx]['image']))\n",
        "  display(data['train'][idx]['image'].resize([350, 350]))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "tF2jRl3wa-g3",
        "outputId": "9c1cd126-5375-46ae-a850-9a63152a73a1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'DocVQA': 'unanswerable'}\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<PIL.Image.Image image mode=L size=350x350>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'DocVQA': 'unanswerable'}\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<PIL.Image.Image image mode=L size=350x350>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'DocVQA': '499150498'}\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<PIL.Image.Image image mode=L size=350x350>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "We need to construct our dataset. Note how we are adding a new task prefix `<DocVQA>` before the question when constructing the prompt."
      ],
      "metadata": {
        "id": "YQKWM3P6Ncx_"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from torch.utils.data import Dataset\n",
        "\n",
        "class DocVQADataset(Dataset):\n",
        "    def __init__(self, data):\n",
        "        self.data = data\n",
        "\n",
        "    def __len__(self):\n",
        "        return len(self.data)\n",
        "\n",
        "    def __getitem__(self, idx):\n",
        "        example = self.data[idx]\n",
        "        question = \"<DocVQA>\" + example['question']\n",
        "        first_answer = example['answers'][0]\n",
        "        image = example['image']\n",
        "        if image.mode != \"RGB\":\n",
        "            image = image.convert(\"RGB\")\n",
        "        return question, first_answer, image\n"
      ],
      "metadata": {
        "id": "ax4KEL3ceiSO"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Let's get to fine-tuning. We will create our dataset, the data collator, and start training. In A100 with 40GB memory, we can fit in 6 examples. If you're training on T4, you can use batch size of 1."
      ],
      "metadata": {
        "id": "GtkPrXzkODGx"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "from torch.utils.data import DataLoader\n",
        "from tqdm import tqdm\n",
        "from transformers import (AdamW, AutoProcessor, get_scheduler)\n",
        "\n",
        "def collate_fn(batch):\n",
        "    questions, answers, images = zip(*batch)\n",
        "    inputs = processor(text=list(questions), images=list(images), return_tensors=\"pt\", padding=True).to(device)\n",
        "    return inputs, answers\n",
        "\n",
        "# Create datasets\n",
        "train_dataset = DocVQADataset(data['train'])\n",
        "val_dataset = DocVQADataset(data['validation'])\n",
        "\n",
        "# Create DataLoader\n",
        "batch_size = 6\n",
        "num_workers = 0\n",
        "\n",
        "train_loader = DataLoader(train_dataset, batch_size=batch_size, collate_fn=collate_fn, num_workers=num_workers, shuffle=True)\n",
        "val_loader = DataLoader(val_dataset, batch_size=batch_size, collate_fn=collate_fn, num_workers=num_workers)"
      ],
      "metadata": {
        "id": "ilMb0ivGdt9l"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def train_model(train_loader, val_loader, model, processor, epochs=10, lr=1e-6):\n",
        "    optimizer = AdamW(model.parameters(), lr=lr)\n",
        "    num_training_steps = epochs * len(train_loader)\n",
        "    lr_scheduler = get_scheduler(\n",
        "        name=\"linear\",\n",
        "        optimizer=optimizer,\n",
        "        num_warmup_steps=0,\n",
        "        num_training_steps=num_training_steps,\n",
        "    )\n",
        "\n",
        "    for epoch in range(epochs):\n",
        "        model.train()\n",
        "        train_loss = 0\n",
        "        i = -1\n",
        "        for batch in tqdm(train_loader, desc=f\"Training Epoch {epoch + 1}/{epochs}\"):\n",
        "            i += 1\n",
        "            inputs, answers = batch\n",
        "\n",
        "            input_ids = inputs[\"input_ids\"]\n",
        "            pixel_values = inputs[\"pixel_values\"]\n",
        "            labels = processor.tokenizer(text=answers, return_tensors=\"pt\", padding=True, return_token_type_ids=False).input_ids.to(device)\n",
        "\n",
        "            outputs = model(input_ids=input_ids, pixel_values=pixel_values, labels=labels)\n",
        "            loss = outputs.loss\n",
        "\n",
        "            loss.backward()\n",
        "            optimizer.step()\n",
        "            lr_scheduler.step()\n",
        "            optimizer.zero_grad()\n",
        "\n",
        "            train_loss += loss.item()\n",
        "\n",
        "        avg_train_loss = train_loss / len(train_loader)\n",
        "        print(f\"Average Training Loss: {avg_train_loss}\")\n",
        "\n",
        "        # Validation phase\n",
        "        model.eval()\n",
        "        val_loss = 0\n",
        "        with torch.no_grad():\n",
        "            for batch in tqdm(val_loader, desc=f\"Validation Epoch {epoch + 1}/{epochs}\"):\n",
        "                inputs, answers = batch\n",
        "\n",
        "                input_ids = inputs[\"input_ids\"]\n",
        "                pixel_values = inputs[\"pixel_values\"]\n",
        "                labels = processor.tokenizer(text=answers, return_tensors=\"pt\", padding=True, return_token_type_ids=False).input_ids.to(device)\n",
        "\n",
        "                outputs = model(input_ids=input_ids, pixel_values=pixel_values, labels=labels)\n",
        "                loss = outputs.loss\n",
        "\n",
        "                val_loss += loss.item()\n",
        "\n",
        "        avg_val_loss = val_loss / len(val_loader)\n",
        "        print(f\"Average Validation Loss: {avg_val_loss}\")\n",
        "\n",
        "        # Save model checkpoint\n",
        "        output_dir = f\"./model_checkpoints/epoch_{epoch+1}\"\n",
        "        os.makedirs(output_dir, exist_ok=True)\n",
        "        model.save_pretrained(output_dir)\n",
        "        processor.save_pretrained(output_dir)\n"
      ],
      "metadata": {
        "id": "bC06Mc7jOdpY"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "After training, we will push the model to Hugging Face Hub. To do so, we need to login first with write access. Make sure to pass either write token or fine-grained token (by first creating the repository and setting up fine-grained token access)."
      ],
      "metadata": {
        "id": "A6whoCNXR2Hn"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from huggingface_hub import notebook_login\n",
        "\n",
        "notebook_login()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 359,
          "referenced_widgets": [
            "5ea5d7a767e9405db0c12b184104fad7",
            "a28a4879bba845488a059d455840f7df",
            "774a9597328c429ebe856762d4cac46f",
            "df6100e810db453f9baca6aa2f0e5b3d",
            "5a71cc16b2ca4243b0abfd36d6887a50",
            "6d0062d900bf458792746066546e67c0",
            "3de2c8c0b0674ceb8f9f8165282f8382",
            "885858d51c0045efb685996af82dd919",
            "2d7761357d1444b8bc145255388bbb21",
            "edca5834a1fe4ef0996fc6d357c3f483",
            "69d144137e4449f087b3b3fb11e8fcd9",
            "f2412043f7f94b1bad2c6a0bdf894cce",
            "3038099151b445279ab600eee329abf9",
            "31c07e72d49e4dd383730d6bb2f13b2d",
            "bbe052c524864d5faef734b831353d1f",
            "0f77e1f0fd4e4839a05231fa61dd5b18",
            "2457df1786e94920be8687211c86414a"
          ]
        },
        "id": "LjpWNSXePbWx",
        "outputId": "eeafcd1f-9336-41ab-de96-d161a6e2581d"
      },
      "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": "5ea5d7a767e9405db0c12b184104fad7"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "We will freeze image encoder for this tutorial. The authors have reported improvement in unfreezing image encoder, but note that this will result in more resource usage."
      ],
      "metadata": {
        "id": "a0xwNbdVOsLK"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "for param in model.vision_tower.parameters():\n",
        "  param.is_trainable = False"
      ],
      "metadata": {
        "id": "TSScAwP4eryt"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train_model(train_loader, val_loader, model, processor, epochs=2)"
      ],
      "metadata": {
        "id": "LZybGHd3fNJ1"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "You can push the model like below. You can find the fully fine-tuned DocVQA model [here](https://huggingface.co/HuggingFaceM4/Florence-2-DocVQA). You can find the demo [here](https://huggingface.co/spaces/andito/Florence-2-DocVQA).\n",
        "\n",
        "Read more about Florence-2 and fine-tuning it [here](https://huggingface.co/blog/finetune-florence2)."
      ],
      "metadata": {
        "id": "t4OuOljZS8is"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model.push_to_hub(\"HuggingFaceM4/Florence-2-FT-DocVQA\")\n",
        "processor.push_to_hub(\"HuggingFaceM4/Florence-2-FT-DocVQA\")"
      ],
      "metadata": {
        "id": "EUGIOfq4P6pF"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}