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  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/donbcolab/vlm_architecture/blob/main/notebooks/llava_1_6_mistral.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# LLaVA 1.6 Mistral"
      ],
      "metadata": {
        "id": "FBQqObAuV-vx"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Install Python Dependencies"
      ],
      "metadata": {
        "id": "s16yW6o0l87U"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install -qU torch transformers timm datasets bitsandbytes accelerate"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CaM6YGddUx_X",
        "outputId": "c93a0bf9-a8c3-407d-afb0-8d4a75a4cc5e"
      },
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m2.3/2.3 MB\u001b[0m \u001b[31m25.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m542.0/542.0 kB\u001b[0m \u001b[31m32.2 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[31m13.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m302.6/302.6 kB\u001b[0m \u001b[31m33.8 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[31m70.3 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[31m13.6 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[31m26.2 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[31m19.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Import necessary libraries"
      ],
      "metadata": {
        "id": "1t3ofIR2mHhX"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "PfJNXElhUaAx"
      },
      "outputs": [],
      "source": [
        "from PIL import Image\n",
        "import requests\n",
        "from transformers import LlavaNextForConditionalGeneration\n",
        "from transformers import LlavaNextProcessor\n",
        "import torch"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Hugging Face model configuration\n",
        "- Load the vision model and processor from Hugging Face"
      ],
      "metadata": {
        "id": "9OlACINKmNqy"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Hugging Face model configuration\n",
        "\n",
        "model_id = \"llava-hf/llava-v1.6-mistral-7b-hf\"\n",
        "\n",
        "device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n",
        "\n",
        "model = LlavaNextForConditionalGeneration.from_pretrained(model_id, torch_dtype=torch.bfloat16).to(device)\n",
        "processor = LlavaNextProcessor.from_pretrained(model_id)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 646,
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        "id": "uv-ADG6eaLLn",
        "outputId": "e534a4b2-c8e2-42c1-f37c-8c9e0fa5f87c"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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            ],
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          "metadata": {}
        },
        {
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          "metadata": {}
        },
        {
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          "data": {
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            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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          },
          "metadata": {}
        },
        {
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            "application/vnd.jupyter.widget-view+json": {
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Loading checkpoint shards:   0%|          | 0/4 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
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          },
          "metadata": {}
        },
        {
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          "metadata": {}
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          "metadata": {}
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          "metadata": {}
        },
        {
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            ],
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          "metadata": {}
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        {
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            ],
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          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Visualize the Model Architecture\n",
        "\n",
        "- Print the model architecture to understand its structure"
      ],
      "metadata": {
        "id": "NLR8po3xmjPk"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Visualize the Model Architecture\n",
        "\n",
        "print(model)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eb8Y8bI6WjTk",
        "outputId": "15528d52-149c-42ba-add3-6f8a501064b0"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "LlavaNextForConditionalGeneration(\n",
            "  (vision_tower): CLIPVisionModel(\n",
            "    (vision_model): CLIPVisionTransformer(\n",
            "      (embeddings): CLIPVisionEmbeddings(\n",
            "        (patch_embedding): Conv2d(3, 1024, kernel_size=(14, 14), stride=(14, 14), bias=False)\n",
            "        (position_embedding): Embedding(577, 1024)\n",
            "      )\n",
            "      (pre_layrnorm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)\n",
            "      (encoder): CLIPEncoder(\n",
            "        (layers): ModuleList(\n",
            "          (0-23): 24 x CLIPEncoderLayer(\n",
            "            (self_attn): CLIPAttention(\n",
            "              (k_proj): Linear(in_features=1024, out_features=1024, bias=True)\n",
            "              (v_proj): Linear(in_features=1024, out_features=1024, bias=True)\n",
            "              (q_proj): Linear(in_features=1024, out_features=1024, bias=True)\n",
            "              (out_proj): Linear(in_features=1024, out_features=1024, bias=True)\n",
            "            )\n",
            "            (layer_norm1): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)\n",
            "            (mlp): CLIPMLP(\n",
            "              (activation_fn): QuickGELUActivation()\n",
            "              (fc1): Linear(in_features=1024, out_features=4096, bias=True)\n",
            "              (fc2): Linear(in_features=4096, out_features=1024, bias=True)\n",
            "            )\n",
            "            (layer_norm2): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)\n",
            "          )\n",
            "        )\n",
            "      )\n",
            "      (post_layernorm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)\n",
            "    )\n",
            "  )\n",
            "  (multi_modal_projector): LlavaNextMultiModalProjector(\n",
            "    (linear_1): Linear(in_features=1024, out_features=4096, bias=True)\n",
            "    (act): GELUActivation()\n",
            "    (linear_2): Linear(in_features=4096, out_features=4096, bias=True)\n",
            "  )\n",
            "  (language_model): MistralForCausalLM(\n",
            "    (model): MistralModel(\n",
            "      (embed_tokens): Embedding(32064, 4096)\n",
            "      (layers): ModuleList(\n",
            "        (0-31): 32 x MistralDecoderLayer(\n",
            "          (self_attn): MistralSdpaAttention(\n",
            "            (q_proj): Linear(in_features=4096, out_features=4096, bias=False)\n",
            "            (k_proj): Linear(in_features=4096, out_features=1024, bias=False)\n",
            "            (v_proj): Linear(in_features=4096, out_features=1024, bias=False)\n",
            "            (o_proj): Linear(in_features=4096, out_features=4096, bias=False)\n",
            "            (rotary_emb): MistralRotaryEmbedding()\n",
            "          )\n",
            "          (mlp): MistralMLP(\n",
            "            (gate_proj): Linear(in_features=4096, out_features=14336, bias=False)\n",
            "            (up_proj): Linear(in_features=4096, out_features=14336, bias=False)\n",
            "            (down_proj): Linear(in_features=14336, out_features=4096, bias=False)\n",
            "            (act_fn): SiLU()\n",
            "          )\n",
            "          (input_layernorm): MistralRMSNorm()\n",
            "          (post_attention_layernorm): MistralRMSNorm()\n",
            "        )\n",
            "      )\n",
            "      (norm): MistralRMSNorm()\n",
            "    )\n",
            "    (lm_head): Linear(in_features=4096, out_features=32064, bias=False)\n",
            "  )\n",
            ")\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Setup the input for the Vision model"
      ],
      "metadata": {
        "id": "xlLfWy5qmuU-"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Setup and Run Vision model inference\n"
      ],
      "metadata": {
        "id": "YrfgSBNvm-cl"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Discover the Model's Insights!"
      ],
      "metadata": {
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}