PaliGemma.ipynb

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  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# Using PaliGemma with 🤗 transformers\n",
        "\n",
        "PaliGemma is a new vision language model released by Google. In this notebook, we will see how to use 🤗 transformers for PaliGemma inference.\n",
        "First, install below libraries with update flag as we need to use the latest version of 🤗 transformers along with others."
      ],
      "metadata": {
        "id": "igGrdR8hu1Yd"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zchyD7YIsmi7",
        "outputId": "3c0f29a5-6d9c-4f8f-cfcd-4c4a395e4d8d"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "  Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
            "  Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n",
            "  Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m302.6/302.6 kB\u001b[0m \u001b[31m6.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m119.8/119.8 MB\u001b[0m \u001b[31m7.0 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[31m72.8 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"
          ]
        }
      ],
      "source": [
        "!pip install -q -U accelerate bitsandbytes git+https://github.com/huggingface/transformers.git"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "PaliGemma requires users to accept Gemma license, so make sure to go to [the repository]() and ask for access. If you have previously accepted Gemma license, you will have access to this model as well. Once you have the access, login to Hugging Face Hub using `notebook_login()` and pass your access token by running the cell below."
      ],
      "metadata": {
        "id": "gbF8uBFFvWFc"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from huggingface_hub import notebook_login\n",
        "\n",
        "notebook_login()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 145,
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        "id": "j_NIM_Qgs5mq",
        "outputId": "2190e681-2a8a-4c99-bc9b-b2932c4c4eba"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "VBox(children=(HTML(value='<center> <img\\nsrc=https://huggingface.co/front/assets/huggingface_logo-noborder.sv…"
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import torch\n",
        "import numpy as np\n",
        "from PIL import Image\n",
        "import requests\n",
        "\n",
        "input_text = \"What color is the flower that bee is standing on?\"\n",
        "img_url = \"https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/bee.JPG?download=true\"\n",
        "input_image = Image.open(requests.get(img_url, stream=True).raw)"
      ],
      "metadata": {
        "id": "YyCf7CW7vuU9"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "The image looks like below."
      ],
      "metadata": {
        "id": "l3fBzNOqjO22"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "![](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/bee.JPG?download=true)"
      ],
      "metadata": {
        "id": "fD-KB1tCjSik"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "You can load PaliGemma model and processor like below."
      ],
      "metadata": {
        "id": "kwwhOe6A2gHh"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import AutoTokenizer, PaliGemmaForConditionalGeneration, PaliGemmaProcessor\n",
        "import torch\n",
        "\n",
        "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
        "model_id = \"google/paligemma-3b-mix-224\"\n",
        "model = PaliGemmaForConditionalGeneration.from_pretrained(model_id, torch_dtype=torch.bfloat16)\n",
        "processor = PaliGemmaProcessor.from_pretrained(model_id)"
      ],
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        },
        "id": "HKYYuWe_s7Rs",
        "outputId": "199885e2-1df5-48c6-8524-18f5c6ef0092"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/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": [
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          "metadata": {}
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            ],
            "application/vnd.jupyter.widget-view+json": {
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            }
          },
          "metadata": {}
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          "metadata": {}
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            "application/vnd.jupyter.widget-view+json": {
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          },
          "metadata": {}
        },
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            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "`config.hidden_act` is ignored, you should use `config.hidden_activation` instead.\n",
            "Gemma's activation function will be set to `gelu_pytorch_tanh`. Please, use\n",
            "`config.hidden_activation` if you want to override this behaviour.\n",
            "See https://github.com/huggingface/transformers/pull/29402 for more details.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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            }
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            ],
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            }
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          "metadata": {}
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            ],
            "application/vnd.jupyter.widget-view+json": {
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            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "fbf59c2a6cdf4de7806d9a9606819c07"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "d92923b87720419abc7debb8c83e1ef0"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The processor preprocesses both the image and text, so we will pass them."
      ],
      "metadata": {
        "id": "Z2s9B89ekJ2r"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "inputs = processor(text=input_text, images=input_image,\n",
        "                  padding=\"longest\", do_convert_rgb=True, return_tensors=\"pt\").to(\"cuda\")\n",
        "model.to(device)\n",
        "inputs = inputs.to(dtype=model.dtype)\n"
      ],
      "metadata": {
        "id": "u9kau5IOjNt9"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "We can pass in our preprocessed inputs."
      ],
      "metadata": {
        "id": "cvJTWZX0jM3K"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "with torch.no_grad():\n",
        "  output = model.generate(**inputs, max_length=496)\n",
        "\n",
        "print(processor.decode(output[0], skip_special_tokens=True))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bNRrmPcQyEfT",
        "outputId": "db63238f-2598-4980-edbb-7968fac21ad7"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "What color is the flower that bee is standing on?\n",
            "pink\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Load model in 4-bit\n",
        "\n",
        "You can also load model in 4-bit and 8-bit, which offers memory gains during inference.\n",
        "First, initialize the `BitsAndBytesConfig`."
      ],
      "metadata": {
        "id": "7wdFjmxPyUsC"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import BitsAndBytesConfig\n",
        "import torch\n",
        "nf4_config = BitsAndBytesConfig(\n",
        "   load_in_4bit=True,\n",
        "   bnb_4bit_quant_type=\"nf4\",\n",
        "   bnb_4bit_use_double_quant=True,\n",
        "   bnb_4bit_compute_dtype=torch.bfloat16\n",
        ")\n"
      ],
      "metadata": {
        "id": "cb9NEdq2s-nf"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "We will now reload the model but pass in above object as `quantization_config`."
      ],
      "metadata": {
        "id": "TtbAZsn64VV0"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import AutoTokenizer, PaliGemmaForConditionalGeneration, PaliGemmaProcessor\n",
        "import torch\n",
        "\n",
        "device=\"cuda\"\n",
        "model_id = \"google/paligemma-3b-mix-224\"\n",
        "model = PaliGemmaForConditionalGeneration.from_pretrained(model_id, torch_dtype=torch.bfloat16,\n",
        "                                                          quantization_config=nf4_config, device_map={\"\":0})\n",
        "processor = PaliGemmaProcessor.from_pretrained(model_id)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49,
          "referenced_widgets": [
            "9778ff3182c2469b8f770b9c14a5f9f9",
            "351f071c541340088296275cf402a4f8",
            "2cee72b11e834c03a6e2e87fcdbe1f5b",
            "cddb771128554665b611a52ad51c50b3",
            "4b6e96f3efd548b7b0a45ac7e6ae8f2b",
            "a8c933ce5d8a49a780b415f1efdeed0b",
            "c40847bf43fc44b5b54a778adee02bf3",
            "2f860d8aece9455ab9b29cf7f5363fc2",
            "b0ff2d1fabc746d0b3c62f76ee754563",
            "43548839e5b24bde84871afe8e364325",
            "1171b37dd38f42c89916bd96224ea2b7"
          ]
        },
        "id": "-iShp-9ntAV5",
        "outputId": "c23c66df-9bf8-485b-d5d2-33f47bc055cb"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Loading checkpoint shards:   0%|          | 0/3 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "9778ff3182c2469b8f770b9c14a5f9f9"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "with torch.no_grad():\n",
        "  output = model.generate(**inputs, max_length=496)\n",
        "\n",
        "print(processor.decode(output[0], skip_special_tokens=True))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "A7GSnVVUy8lJ",
        "outputId": "03435a22-c202-48ff-951b-7b644110ff4a"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "What color is the flower that bee is standing on?\n",
            "pink\n"
          ]
        }
      ]
    }
  ]
}