{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/mrm8488/shared_colab_notebooks/blob/master/finetune_IDEFICS_image_captioning_peft.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UNCNPVi8iAgw"
      },
      "source": [
        "# Fine-tune IDEFICS for image captioning 🖼️➡️🔡\n",
        "> By [mrm8488](https://twitter.com/mrm8488) with the support of [CliBrAIn](https://clibrain.com/)\n",
        "\n",
        "## IDEFICS: A Flamingo-based model, trained at scale for the community\n",
        "\n",
        "<div style=\"text-align: center;\">\n",
        "</div>\n",
        "<div style=\"display: flex; justify-content: center;\">\n",
        "    <img src=\"https://huggingface.co/HuggingFaceM4/idefics-80b/resolve/main/assets/Idefics_colab.png\" alt=\"Idefics image\" >\n",
        "</div>\n",
        "\n",
        "Credit: [Flamingo blog](https://www.deepmind.com/blog/tackling-multiple-tasks-with-a-single-visual-language-model)\n",
        "\n",
        "[IDEFICS](https://huggingface.co/HuggingFaceM4/idefics-80b) is a multi-modal model based on the [Flamingo](https://arxiv.org/abs/2204.14198) architecture.\n",
        "\n",
        "IDEFICS elegantly integrates two specialized, open-access pre-trained models to harmonize both visual and textual modalities. Sophisticated Transformer blocks serve as the newly instituted parameters, acting as the linchpin between the vision encoder and the linguistic model. This groundbreaking model is cultivated on a diverse training set, consisting of paired image-text instances as well as multifaceted multimedia web content.\n",
        "\n",
        "The code for this notebook is heavily based on this [colab](https://github.com/huggingface/notebooks/blob/main/examples/idefics/finetune_image_captioning_peft.ipynb)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Install and import necessary libraries"
      ],
      "metadata": {
        "id": "7m9zw1wcCC8e"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# check our GPU\n",
        "! nvidia-smi"
      ],
      "metadata": {
        "id": "8PtxGMg_beRq",
        "outputId": "057210b9-c7c8-4e98-8eb7-d8e5c0365bf5",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mon Sep  4 19:25:55 2023       \n",
            "+-----------------------------------------------------------------------------+\n",
            "| NVIDIA-SMI 525.105.17   Driver Version: 525.105.17   CUDA Version: 12.0     |\n",
            "|-------------------------------+----------------------+----------------------+\n",
            "| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |\n",
            "| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |\n",
            "|                               |                      |               MIG M. |\n",
            "|===============================+======================+======================|\n",
            "|   0  NVIDIA A100-SXM...  Off  | 00000000:00:04.0 Off |                    0 |\n",
            "| N/A   33C    P0    43W / 400W |      0MiB / 40960MiB |      0%      Default |\n",
            "|                               |                      |             Disabled |\n",
            "+-------------------------------+----------------------+----------------------+\n",
            "                                                                               \n",
            "+-----------------------------------------------------------------------------+\n",
            "| Processes:                                                                  |\n",
            "|  GPU   GI   CI        PID   Type   Process name                  GPU Memory |\n",
            "|        ID   ID                                                   Usage      |\n",
            "|=============================================================================|\n",
            "|  No running processes found                                                 |\n",
            "+-----------------------------------------------------------------------------+\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "c0CN_O3xbhlI"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install -q datasets\n",
        "!pip install -q git+https://github.com/huggingface/transformers.git@add-model-idefics\n",
        "!pip install -q bitsandbytes sentencepiece accelerate loralib\n",
        "!pip install -q -U git+https://github.com/huggingface/peft.git"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "prXRsUiXCII9",
        "outputId": "46cd9ca0-0960-4cd2-a825-0051c4bd1cbb"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[?25l     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/519.3 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[32m204.8/519.3 kB\u001b[0m \u001b[31m5.9 MB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0m\r\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m519.3/519.3 kB\u001b[0m \u001b[31m10.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m115.3/115.3 kB\u001b[0m \u001b[31m15.8 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[31m23.6 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[31m16.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m268.8/268.8 kB\u001b[0m \u001b[31m25.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h\u001b[33m  WARNING: Did not find branch or tag 'add-model-idefics', assuming revision or ref.\u001b[0m\u001b[33m\n",
            "\u001b[0m  \u001b[1;31merror\u001b[0m: \u001b[1msubprocess-exited-with-error\u001b[0m\n",
            "  \n",
            "  \u001b[31m×\u001b[0m \u001b[32mgit checkout -q add-model-idefics\u001b[0m did not run successfully.\n",
            "  \u001b[31m│\u001b[0m exit code: \u001b[1;36m1\u001b[0m\n",
            "  \u001b[31m╰─>\u001b[0m See above for output.\n",
            "  \n",
            "  \u001b[1;35mnote\u001b[0m: This error originates from a subprocess, and is likely not a problem with pip.\n",
            "\u001b[1;31merror\u001b[0m: \u001b[1msubprocess-exited-with-error\u001b[0m\n",
            "\n",
            "\u001b[31m×\u001b[0m \u001b[32mgit checkout -q add-model-idefics\u001b[0m did not run successfully.\n",
            "\u001b[31m│\u001b[0m exit code: \u001b[1;36m1\u001b[0m\n",
            "\u001b[31m╰─>\u001b[0m See above for output.\n",
            "\n",
            "\u001b[1;35mnote\u001b[0m: This error originates from a subprocess, and is likely not a problem with pip.\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m92.6/92.6 MB\u001b[0m \u001b[31m19.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.3/1.3 MB\u001b[0m \u001b[31m72.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m251.2/251.2 kB\u001b[0m \u001b[31m28.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  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[32m7.5/7.5 MB\u001b[0m \u001b[31m49.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.3/1.3 MB\u001b[0m \u001b[31m71.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m7.8/7.8 MB\u001b[0m \u001b[31m107.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Building wheel for peft (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Log in to the hub to automatically push ckpts\n",
        "from huggingface_hub import notebook_login\n",
        "notebook_login()"
      ],
      "metadata": {
        "id": "ELu1W322bjkL",
        "outputId": "d555c7c1-e4c2-4155-ac5d-0cf24cd8e95f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 145,
          "referenced_widgets": [
            "172bd57dbd774a47a1cc6de677983da6",
            "6ce70cd2888b4bcb89bb69728533fba8",
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            "c66e186b3e9f41c58287625d8f58ba13",
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            "8783566663db4d3fab1a4442fab88368",
            "1d3e0eaf1f6f48d292cccce239cfc85f",
            "2cb3df9fcc56493da6917d6ddacde2c8"
          ]
        }
      },
      "execution_count": 4,
      "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": "172bd57dbd774a47a1cc6de677983da6"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "id": "MxoHmx-HfAgf"
      },
      "outputs": [],
      "source": [
        "import torch\n",
        "from datasets import load_dataset\n",
        "from peft import LoraConfig, get_peft_model\n",
        "from PIL import Image\n",
        "from transformers import IdeficsForVisionText2Text, AutoProcessor, Trainer, TrainingArguments, BitsAndBytesConfig\n",
        "import torchvision.transforms as transforms"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DP_ilre6jI6l"
      },
      "source": [
        "## Load quantized model\n",
        "First get the quantized version of the model. This will allow us to use the 9B version of Idefics with a single 16GB gpu\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n",
        "model_id = \"HuggingFaceM4/idefics-9b\""
      ],
      "metadata": {
        "id": "BM4Tm4wwcGC8"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "id": "IRiT0q0Ck-3Y"
      },
      "outputs": [],
      "source": [
        "# Here we skip some special modules that can't be quantized properly\n",
        "bnb_config = BitsAndBytesConfig(\n",
        "    load_in_4bit=True,\n",
        "    bnb_4bit_use_double_quant=True,\n",
        "    bnb_4bit_quant_type=\"nf4\",\n",
        "    bnb_4bit_compute_dtype=torch.float16,\n",
        "    llm_int8_skip_modules=[\"lm_head\", \"embed_tokens\"],\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "processor = AutoProcessor.from_pretrained(model_id, use_auth_token=True)\n",
        "# Simply take-off the quantization_config arg if you want to load the original model\n",
        "model = IdeficsForVisionText2Text.from_pretrained(model_id, quantization_config=bnb_config, device_map=\"auto\")"
      ],
      "metadata": {
        "id": "mpmn9S_0cDqX",
        "outputId": "8ce55291-aaf1-447c-95f0-d4c8f0a86bc3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000,
          "referenced_widgets": [
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      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/transformers/models/auto/processing_auto.py:203: FutureWarning: The `use_auth_token` argument is deprecated and will be removed in v5 of Transformers.\n",
            "  warnings.warn(\n"
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            ],
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      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "If you print the model, you will see that all `nn.Linear` layers are in fact replaced by `bnb.nn.Linear4bit` layers."
      ],
      "metadata": {
        "id": "PloVmAaM75kJ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(model)"
      ],
      "metadata": {
        "id": "4gaDzRK174Ur",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2518eb42-2fe3-4a16-fb66-5f4134801bde"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "IdeficsForVisionText2Text(\n",
            "  (model): IdeficsModel(\n",
            "    (embed_tokens): IdeficsDecoupledEmbedding(\n",
            "      num_embeddings=32000, num_additional_embeddings=2, embedding_dim=4096, partially_freeze=False\n",
            "      (additional_embedding): Embedding(2, 4096)\n",
            "    )\n",
            "    (vision_model): IdeficsVisionTransformer(\n",
            "      (embeddings): IdeficsVisionEmbeddings(\n",
            "        (patch_embedding): Conv2d(3, 1280, kernel_size=(14, 14), stride=(14, 14), bias=False)\n",
            "        (position_embedding): Embedding(257, 1280)\n",
            "      )\n",
            "      (pre_layrnorm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
            "      (encoder): IdeficsVisionEncoder(\n",
            "        (layers): ModuleList(\n",
            "          (0-31): 32 x IdeficsVisionEncoderLayer(\n",
            "            (self_attn): IdeficsVisionAttention(\n",
            "              (k_proj): Linear4bit(in_features=1280, out_features=1280, bias=True)\n",
            "              (v_proj): Linear4bit(in_features=1280, out_features=1280, bias=True)\n",
            "              (q_proj): Linear4bit(in_features=1280, out_features=1280, bias=True)\n",
            "              (out_proj): Linear4bit(in_features=1280, out_features=1280, bias=True)\n",
            "            )\n",
            "            (layer_norm1): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
            "            (mlp): IdeficsVisionMLP(\n",
            "              (activation_fn): GELUActivation()\n",
            "              (fc1): Linear4bit(in_features=1280, out_features=5120, bias=True)\n",
            "              (fc2): Linear4bit(in_features=5120, out_features=1280, bias=True)\n",
            "            )\n",
            "            (layer_norm2): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
            "          )\n",
            "        )\n",
            "      )\n",
            "      (post_layernorm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
            "    )\n",
            "    (perceiver_resampler): IdeficsPerceiverResampler(\n",
            "      (blocks): ModuleList(\n",
            "        (0-5): 6 x ModuleList(\n",
            "          (0): IdeficsPerceiverAttention(\n",
            "            (context_layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
            "            (latents_layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
            "            (q_layer_norm): LayerNorm((96,), eps=1e-05, elementwise_affine=True)\n",
            "            (k_layer_norm): LayerNorm((96,), eps=1e-05, elementwise_affine=True)\n",
            "            (q_proj): Linear4bit(in_features=1280, out_features=1536, bias=False)\n",
            "            (k_proj): Linear4bit(in_features=1280, out_features=1536, bias=False)\n",
            "            (v_proj): Linear4bit(in_features=1280, out_features=1536, bias=False)\n",
            "            (output_proj): Linear4bit(in_features=1536, out_features=1280, bias=False)\n",
            "          )\n",
            "          (1): IdeficsMLP(\n",
            "            (ln): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
            "            (fc): Linear4bit(in_features=1280, out_features=5120, bias=False)\n",
            "            (act): ReLU()\n",
            "            (c_proj): Linear4bit(in_features=5120, out_features=1280, bias=False)\n",
            "          )\n",
            "        )\n",
            "      )\n",
            "      (layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
            "    )\n",
            "    (layers): ModuleList(\n",
            "      (0-31): 32 x IdeficsDecoderLayer(\n",
            "        (self_attn): IdeficsAttention(\n",
            "          (q_proj): Linear4bit(in_features=4096, out_features=4096, bias=False)\n",
            "          (k_proj): Linear4bit(in_features=4096, out_features=4096, bias=False)\n",
            "          (v_proj): Linear4bit(in_features=4096, out_features=4096, bias=False)\n",
            "          (o_proj): Linear4bit(in_features=4096, out_features=4096, bias=False)\n",
            "          (rotary_emb): IdeficsEmbedding()\n",
            "        )\n",
            "        (mlp): IdeficsMLP(\n",
            "          (gate_proj): Linear4bit(in_features=4096, out_features=11008, bias=False)\n",
            "          (down_proj): Linear4bit(in_features=11008, out_features=4096, bias=False)\n",
            "          (up_proj): Linear4bit(in_features=4096, out_features=11008, bias=False)\n",
            "          (act_fn): SiLUActivation()\n",
            "        )\n",
            "        (input_layernorm): IdeficsRMSNorm()\n",
            "        (post_attention_layernorm): IdeficsRMSNorm()\n",
            "      )\n",
            "    )\n",
            "    (gated_cross_attn_layers): ModuleList(\n",
            "      (0-7): 8 x IdeficsGatedCrossAttentionLayer(\n",
            "        (cross_attn): IdeficsAttention(\n",
            "          (q_proj): Linear4bit(in_features=4096, out_features=4096, bias=False)\n",
            "          (k_proj): Linear4bit(in_features=1280, out_features=4096, bias=False)\n",
            "          (v_proj): Linear4bit(in_features=1280, out_features=4096, bias=False)\n",
            "          (o_proj): Linear4bit(in_features=4096, out_features=4096, bias=False)\n",
            "          (rotary_emb): IdeficsEmbedding()\n",
            "          (q_layer_norm): IdeficsRMSNorm()\n",
            "          (k_layer_norm): IdeficsRMSNorm()\n",
            "        )\n",
            "        (mlp): IdeficsMLP(\n",
            "          (gate_proj): Linear4bit(in_features=4096, out_features=11008, bias=False)\n",
            "          (down_proj): Linear4bit(in_features=11008, out_features=4096, bias=False)\n",
            "          (up_proj): Linear4bit(in_features=4096, out_features=11008, bias=False)\n",
            "          (act_fn): SiLUActivation()\n",
            "        )\n",
            "        (input_layernorm): IdeficsRMSNorm()\n",
            "        (post_attention_layernorm): IdeficsRMSNorm()\n",
            "        (act_cross_attn): Tanh()\n",
            "        (act_dense): Tanh()\n",
            "      )\n",
            "    )\n",
            "    (norm): IdeficsRMSNorm()\n",
            "  )\n",
            "  (lm_head): IdeficsDecoupledLinear(\n",
            "    in_features=4096, out_features=32000, out_additional_features=2, bias=False, partially_freeze=False\n",
            "    (additional_fc): Linear(in_features=4096, out_features=2, bias=False)\n",
            "  )\n",
            ")\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Load and prepare the dataset 📚🪒"
      ],
      "metadata": {
        "id": "o_w_c9Nl3Kt8"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def convert_to_rgb(image):\n",
        "    # `image.convert(\"RGB\")` would only work for .jpg images, as it creates a wrong background\n",
        "    # for transparent images. The call to `alpha_composite` handles this case\n",
        "    if image.mode == \"RGB\":\n",
        "        return image\n",
        "\n",
        "    image_rgba = image.convert(\"RGBA\")\n",
        "    background = Image.new(\"RGBA\", image_rgba.size, (255, 255, 255))\n",
        "    alpha_composite = Image.alpha_composite(background, image_rgba)\n",
        "    alpha_composite = alpha_composite.convert(\"RGB\")\n",
        "    return alpha_composite"
      ],
      "metadata": {
        "id": "3LTPmZJ_dfhR"
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "instruction = \"Describe the following image in a highly detailed way:\" # set it as you wish"
      ],
      "metadata": {
        "id": "anps5eMd6Atx"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def ds_transforms(example_batch):\n",
        "    image_size = processor.image_processor.image_size\n",
        "    image_mean = processor.image_processor.image_mean\n",
        "    image_std = processor.image_processor.image_std\n",
        "\n",
        "    image_transform = transforms.Compose([\n",
        "        convert_to_rgb,\n",
        "        transforms.RandomResizedCrop((image_size, image_size), scale=(0.9, 1.0), interpolation=transforms.InterpolationMode.BICUBIC),\n",
        "        transforms.ToTensor(),\n",
        "        transforms.Normalize(mean=image_mean, std=image_std),\n",
        "    ])\n",
        "\n",
        "    prompts = []\n",
        "    for i in range(len(example_batch['prompt'])):\n",
        "        caption = example_batch['prompt'][i]\n",
        "        prompts.append(\n",
        "            [\n",
        "                example_batch['image'][i],\n",
        "                f\"{instruction}: {caption}</s>\",\n",
        "            ],\n",
        "        )\n",
        "\n",
        "    inputs = processor(prompts, transform=image_transform, return_tensors=\"pt\").to(device)\n",
        "\n",
        "    inputs[\"labels\"] = inputs[\"input_ids\"]\n",
        "\n",
        "    return inputs"
      ],
      "metadata": {
        "id": "cFwakrNidihN"
      },
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# load and prepare dataset\n",
        "dataset_id = \"poloclub/diffusiondb\"\n",
        "subset_id = \"2m_random_50k\"\n",
        "\n",
        "ds = load_dataset(dataset_id, subset_id, split=\"train\") # It will take a while"
      ],
      "metadata": {
        "id": "iYjJFadzcj5b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000,
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              "version_major": 2,
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          "metadata": {}
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        {
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          },
          "metadata": {}
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        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating train split: 0 examples [00:00, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "f56f55be3487457bbfba829f2d696603"
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          "metadata": {}
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ds"
      ],
      "metadata": {
        "id": "EyOWPW-hm7jm",
        "outputId": "f49529d5-7f51-4df7-fd89-0643a5ece5cc",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Dataset({\n",
              "    features: ['image', 'prompt', 'seed', 'step', 'cfg', 'sampler', 'width', 'height', 'user_name', 'timestamp', 'image_nsfw', 'prompt_nsfw'],\n",
              "    num_rows: 50000\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ds = ds.train_test_split(test_size=0.1, seed=45, shuffle=True)"
      ],
      "metadata": {
        "id": "mYSr9SVJfAU8"
      },
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "ds"
      ],
      "metadata": {
        "id": "1sDu-iLkPRbs",
        "outputId": "b235f404-1fe7-4d19-aee1-cd6929e5581f",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DatasetDict({\n",
              "    train: Dataset({\n",
              "        features: ['image', 'prompt', 'seed', 'step', 'cfg', 'sampler', 'width', 'height', 'user_name', 'timestamp', 'image_nsfw', 'prompt_nsfw'],\n",
              "        num_rows: 45000\n",
              "    })\n",
              "    test: Dataset({\n",
              "        features: ['image', 'prompt', 'seed', 'step', 'cfg', 'sampler', 'width', 'height', 'user_name', 'timestamp', 'image_nsfw', 'prompt_nsfw'],\n",
              "        num_rows: 5000\n",
              "    })\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_ds = ds[\"train\"]\n",
        "eval_ds = ds[\"test\"]"
      ],
      "metadata": {
        "id": "5T58w20WfCNC"
      },
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train_ds.set_transform(ds_transforms)\n",
        "eval_ds.set_transform(ds_transforms)"
      ],
      "metadata": {
        "id": "FYpeVoXCcvYj"
      },
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train_ds[0]"
      ],
      "metadata": {
        "id": "uMNVzIyClPuc",
        "outputId": "04066d01-1b9c-4c4e-ad71-7398152a960d",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'input_ids': tensor([    1, 32000, 32001, 32000, 20355,   915,   278,  1494,  1967, 29901,\n",
              "         28396,   377,  5168, 10109,   411, 11359,   297,  4818, 29871,     2],\n",
              "        device='cuda:0'),\n",
              " 'attention_mask': tensor([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n",
              "        device='cuda:0'),\n",
              " 'pixel_values': tensor([[[[-1.2959, -1.4565, -1.5733,  ..., -1.5587, -1.5149, -1.5149],\n",
              "           [-1.7193, -1.6755, -1.6901,  ..., -1.4565, -1.5295, -1.5733],\n",
              "           [-1.5003, -1.4565, -1.5003,  ..., -1.5295, -1.4857, -1.4711],\n",
              "           ...,\n",
              "           [-1.7193, -1.7193, -1.7193,  ..., -1.7193, -1.7193, -1.7047],\n",
              "           [-1.7485, -1.7339, -1.7339,  ..., -1.7193, -1.7485, -1.7339],\n",
              "           [-1.7485, -1.7631, -1.7631,  ..., -1.7193, -1.7485, -1.7485]],\n",
              " \n",
              "          [[-0.7016, -0.9417, -1.0918,  ..., -1.1218, -1.0617, -0.9417],\n",
              "           [-1.3169, -1.3019, -1.2869,  ..., -0.9117, -1.0767, -1.1368],\n",
              "           [-1.0167, -1.0167, -1.0467,  ..., -1.0317, -0.9117, -0.9267],\n",
              "           ...,\n",
              "           [-1.4669, -1.4970, -1.4970,  ..., -1.4219, -1.3619, -1.3319],\n",
              "           [-1.4820, -1.4820, -1.4970,  ..., -1.4219, -1.3919, -1.3619],\n",
              "           [-1.4519, -1.4519, -1.4820,  ..., -1.4069, -1.4069, -1.3919]],\n",
              " \n",
              "          [[-0.2004, -0.3995, -0.5559,  ..., -0.5559, -0.5275, -0.4137],\n",
              "           [-0.7977, -0.7692, -0.7266,  ..., -0.3284, -0.5133, -0.5986],\n",
              "           [-0.4706, -0.4422, -0.4706,  ..., -0.4706, -0.3142, -0.3568],\n",
              "           ...,\n",
              "           [-1.0252, -1.0394, -1.0536,  ..., -0.9399, -0.8972, -0.8545],\n",
              "           [-1.0394, -1.0536, -1.0536,  ..., -0.9541, -0.9399, -0.9114],\n",
              "           [-1.0252, -1.0394, -1.0536,  ..., -0.9399, -0.9683, -0.9399]]]],\n",
              "        device='cuda:0'),\n",
              " 'image_attention_mask': tensor([[0],\n",
              "         [0],\n",
              "         [1],\n",
              "         [1],\n",
              "         [1],\n",
              "         [1],\n",
              "         [1],\n",
              "         [1],\n",
              "         [1],\n",
              "         [1],\n",
              "         [1],\n",
              "         [1],\n",
              "         [1],\n",
              "         [1],\n",
              "         [1],\n",
              "         [1],\n",
              "         [1],\n",
              "         [1],\n",
              "         [1],\n",
              "         [1]], device='cuda:0'),\n",
              " 'labels': tensor([    1, 32000, 32001, 32000, 20355,   915,   278,  1494,  1967, 29901,\n",
              "         28396,   377,  5168, 10109,   411, 11359,   297,  4818, 29871,     2],\n",
              "        device='cuda:0')}"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# LoRA ⚙️\n",
        "After specifying the low-rank adapters (LoRA) config, we load the `PeftModel` using the ` get_peft_model` utility function"
      ],
      "metadata": {
        "id": "Kui4EkCmOQzd"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "id": "jKa5oTorp_A-"
      },
      "outputs": [],
      "source": [
        "config = LoraConfig(\n",
        "    r=16,\n",
        "    lora_alpha=32,\n",
        "    target_modules=[\"q_proj\", \"k_proj\", \"v_proj\"],\n",
        "    lora_dropout=0.05,\n",
        "    bias=\"none\",\n",
        ")\n",
        "model = get_peft_model(model, config)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ShuZJ5K2pYoL",
        "outputId": "4825006b-4306-438a-ae70-1b9bfee2ca33"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "trainable params: 19,750,912 || all params: 8,949,430,544 || trainable%: 0.2206946230030432\n"
          ]
        }
      ],
      "source": [
        "model.print_trainable_parameters()"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "As you can see we only train 19M of params instead of 9B!"
      ],
      "metadata": {
        "id": "ksrHatAx3h_C"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Training 💪\n",
        "Finally, using the Hugging Face Trainer, we can finetune the model! \\\\\n",
        "We will train for 2 epochs, but please, feel free to adapt the `training_args` as you wish. However, I noted that fine-tuning in mixed precision `fp16` can lead to overflows. As such, we recommend training in mixed precision `bf16` when possible."
      ],
      "metadata": {
        "id": "0Ok1sOZKQ29s"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "metadata": {
        "id": "9cD3OuygpR5l"
      },
      "outputs": [],
      "source": [
        "model_name = model_id.split(\"/\")[1]\n",
        "training_args = TrainingArguments(\n",
        "    output_dir=f\"{model_name}-ft-describe-diffusion-test\",\n",
        "    learning_rate=2e-4,\n",
        "    bf16=True,\n",
        "    fp16=False,\n",
        "    per_device_train_batch_size=4,\n",
        "    per_device_eval_batch_size=8,\n",
        "    num_train_epochs=2,\n",
        "    gradient_accumulation_steps=8,\n",
        "    dataloader_pin_memory=False,\n",
        "    save_total_limit=3,\n",
        "    evaluation_strategy=\"steps\",\n",
        "    save_strategy=\"steps\",\n",
        "    save_steps=1000,\n",
        "    eval_steps=1000,\n",
        "    logging_steps=10,\n",
        "    max_steps=-1,\n",
        "    remove_unused_columns=False,\n",
        "    push_to_hub=True,\n",
        "    label_names=[\"labels\"],\n",
        "    load_best_model_at_end=True,\n",
        "    optim=\"paged_adamw_8bit\",\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "trainer = Trainer(\n",
        "    model=model,\n",
        "    args=training_args,\n",
        "    train_dataset=train_ds,\n",
        "    eval_dataset=eval_ds,\n",
        ")"
      ],
      "metadata": {
        "id": "fZ5NJCg-fe0x"
      },
      "execution_count": 27,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Log the training progress 📊"
      ],
      "metadata": {
        "id": "u-le0vu048SJ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "%load_ext tensorboard\n",
        "%tensorboard --logdir /content/idefics-9b-ft-describe-diffusion-test"
      ],
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        "\n",
        "**Our goal is to be the leading provider of AI in Spanish, enriching the lives of individuals and organizations while making a positive and long-lasting impact on society.**\n",
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        "\n",
        "Our **services** include:\n",
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        "1. **Large Language Models (LLMs):** We develop 100% proprietary language models in Spanish.\n",
        "2. **Integrations and Custom Solutions:** We provide technical integrations through APIs to cater to the specific needs of our clients.\n",
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