3. Fine-tuning Florence-2.ipynb

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      "metadata": {
        "id": "view-in-github",
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      "source": [
        "<a href=\"https://colab.research.google.com/github/ANYANTUDRE/Florence-2-Vision-Language-Model/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>"
      ]
    },
    {
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      "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"
      ],
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        "id": "Y6b1dvjgYXOD",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ebe2ac8f-c2a0-4284-d62b-01719008aed0"
      },
      "execution_count": 1,
      "outputs": [
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            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m547.8/547.8 kB\u001b[0m \u001b[31m8.1 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[31m45.5 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[31m69.9 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[31m3.7 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[31m18.3 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[31m9.4 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[31m6.9 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[31m17.0 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[31m12.5 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[31m60.3 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"
          ]
        }
      ]
    },
    {
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        "from datasets import load_dataset\n",
        "\n",
        "data = load_dataset(\"HuggingFaceM4/DocumentVQA\")"
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        "id": "CpCRb7PfYZP-",
        "outputId": "11344977-a517-49be-ede2-884948854479"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading readme:   0%|          | 0.00/806 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "32fdd9674bb74a7e8b5340a5b08afe67"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Resolving data files:   0%|          | 0/38 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "78b5da6e14be4bf182ff0bbda7dc8b1b"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Resolving data files:   0%|          | 0/17 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "8c404cf0b0a94e5b91818f597203ef36"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Resolving data files:   0%|          | 0/17 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "d895a227b5744bc0a64d007310d4ec86"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Resolving data files:   0%|          | 0/38 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "9d786814277c4e41b7ebcef3a20a9a06"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Resolving data files:   0%|          | 0/17 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "00529dd9bf59422ba2575b5473585ea2"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Resolving data files:   0%|          | 0/17 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "8c6e96ce29bd41dfbcdfecd00433ae59"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading data:   0%|          | 0/38 [00:00<?, ?files/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "364764c252a74e32814368ecac445f22"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading data:   0%|          | 0/17 [00:00<?, ?files/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "f5c8031abad64b619b3f65e21a7c29fa"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading data:   0%|          | 0/17 [00:00<?, ?files/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "ef378762a9714ba8886c0ab47dbdcf6e"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating train split:   0%|          | 0/39463 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "e86dbfd55aae4f7ebc187639bfe91dbd"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating validation split:   0%|          | 0/5349 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "84f1dbd54af34236ae6ea54db90de31b"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating test split:   0%|          | 0/5188 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "378247575a5948398fee9a003e303859"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "error",
          "ename": "KeyboardInterrupt",
          "evalue": "",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-2-fe402c0827c2>\u001b[0m in \u001b[0;36m<cell line: 3>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mdatasets\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mload_dataset\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mload_dataset\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"HuggingFaceM4/DocumentVQA\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/datasets/load.py\u001b[0m in \u001b[0;36mload_dataset\u001b[0;34m(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, verification_mode, ignore_verifications, keep_in_memory, save_infos, revision, token, use_auth_token, task, streaming, num_proc, storage_options, trust_remote_code, **config_kwargs)\u001b[0m\n\u001b[1;32m   2614\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2615\u001b[0m     \u001b[0;31m# Download and prepare data\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2616\u001b[0;31m     builder_instance.download_and_prepare(\n\u001b[0m\u001b[1;32m   2617\u001b[0m         \u001b[0mdownload_config\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdownload_config\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2618\u001b[0m         \u001b[0mdownload_mode\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdownload_mode\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/datasets/builder.py\u001b[0m in \u001b[0;36mdownload_and_prepare\u001b[0;34m(self, output_dir, download_config, download_mode, verification_mode, ignore_verifications, try_from_hf_gcs, dl_manager, base_path, use_auth_token, file_format, max_shard_size, num_proc, storage_options, **download_and_prepare_kwargs)\u001b[0m\n\u001b[1;32m   1027\u001b[0m                         \u001b[0;32mif\u001b[0m \u001b[0mnum_proc\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1028\u001b[0m                             \u001b[0mprepare_split_kwargs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"num_proc\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnum_proc\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1029\u001b[0;31m                         self._download_and_prepare(\n\u001b[0m\u001b[1;32m   1030\u001b[0m                             \u001b[0mdl_manager\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdl_manager\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1031\u001b[0m                             \u001b[0mverification_mode\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mverification_mode\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/datasets/builder.py\u001b[0m in \u001b[0;36m_download_and_prepare\u001b[0;34m(self, dl_manager, verification_mode, **prepare_split_kwargs)\u001b[0m\n\u001b[1;32m   1122\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1123\u001b[0m                 \u001b[0;31m# Prepare split will record examples associated to the split\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1124\u001b[0;31m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_prepare_split\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msplit_generator\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mprepare_split_kwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1125\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mOSError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1126\u001b[0m                 raise OSError(\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/datasets/builder.py\u001b[0m in \u001b[0;36m_prepare_split\u001b[0;34m(self, split_generator, file_format, num_proc, max_shard_size)\u001b[0m\n\u001b[1;32m   1882\u001b[0m             \u001b[0mjob_id\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1883\u001b[0m             \u001b[0;32mwith\u001b[0m \u001b[0mpbar\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1884\u001b[0;31m                 for job_id, done, content in self._prepare_split_single(\n\u001b[0m\u001b[1;32m   1885\u001b[0m                     \u001b[0mgen_kwargs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mgen_kwargs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mjob_id\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mjob_id\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0m_prepare_split_args\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1886\u001b[0m                 ):\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/datasets/builder.py\u001b[0m in \u001b[0;36m_prepare_split_single\u001b[0;34m(self, gen_kwargs, fpath, file_format, max_shard_size, job_id)\u001b[0m\n\u001b[1;32m   1995\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1996\u001b[0m                 \u001b[0m_time\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtime\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtime\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1997\u001b[0;31m                 \u001b[0;32mfor\u001b[0m \u001b[0m_\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtable\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mgenerator\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1998\u001b[0m                     \u001b[0;32mif\u001b[0m \u001b[0mmax_shard_size\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mwriter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_num_bytes\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0mmax_shard_size\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1999\u001b[0m                         \u001b[0mnum_examples\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnum_bytes\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mwriter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfinalize\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/datasets/packaged_modules/parquet/parquet.py\u001b[0m in \u001b[0;36m_generate_tables\u001b[0;34m(self, files)\u001b[0m\n\u001b[1;32m     87\u001b[0m                             \u001b[0mparquet_file\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0miter_batches\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbatch_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mbatch_size\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcolumns\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconfig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     88\u001b[0m                         ):\n\u001b[0;32m---> 89\u001b[0;31m                             \u001b[0mpa_table\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpa\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTable\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfrom_batches\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mrecord_batch\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     90\u001b[0m                             \u001b[0;31m# Uncomment for debugging (will print the Arrow table size and elements)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     91\u001b[0m                             \u001b[0;31m# logger.warning(f\"pa_table: {pa_table} num rows: {pa_table.num_rows}\")\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data"
      ],
      "metadata": {
        "id": "wa6y6702v77t"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "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"
      ],
      "metadata": {
        "id": "zqDWEWDcaSxN"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "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": {
        "id": "tF2jRl3wa-g3"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "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 = 1\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=1, 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": {
        "id": "LjpWNSXePbWx"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "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(\"anyantudre/Florence-2-FT-DocVQA\")\n",
        "processor.push_to_hub(\"anyantudre/Florence-2-FT-DocVQA\")"
      ],
      "metadata": {
        "id": "EUGIOfq4P6pF"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}