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"_model_name": "DescriptionStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "description_width": "" + } + } + } + } + }, + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "view-in-github", + "colab_type": "text" + }, + "source": [ + "\"Open" + ] + }, + { + "cell_type": "markdown", + "source": [ + "## Gemma 3 For Video Understanding\n", + "\n", + "Did you know that you can use Gemma 3 for video understanding?\n", + "\n", + "Gemma 3 interleaving image and text allow for this. You can simply interleave timestamps and frames asking the model to summarize the events in the videos. Here's a small notebook to do so.\n", + "\n", + "Install the release for Gemma 3 and login to access the model." + ], + "metadata": { + "id": "wAstVO6roVVF" + } + }, + { + "cell_type": "code", + "source": [ + "!pip install -q git+https://github.com/huggingface/transformers@v4.49.0-Gemma-3" + ], + "metadata": { + "id": "oBo7w7Toes3U" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "rnDKsFpCdS4c" + }, + "outputs": [], + "source": [ + "!huggingface-cli login" + ] + }, + { + "cell_type": "markdown", + "source": [ + "Let's load the model." + ], + "metadata": { + "id": "t-KxXI7Gom2S" + } + }, + { + "cell_type": "code", + "source": [ + "import torch\n", + "from transformers import AutoProcessor, Gemma3ForConditionalGeneration\n", + "\n", + "ckpt = \"google/gemma-3-4b-it\"\n", + "model = Gemma3ForConditionalGeneration.from_pretrained(\n", + " ckpt, device_map=\"auto\", torch_dtype=torch.bfloat16,\n", + ")\n", + 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(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": [ + "config.json: 0%| | 0.00/855 [00:00, 0.0),\n", + " (, 0.63),\n", + " (, 1.3),\n", + " (, 1.94),\n", + " (, 2.6),\n", + " (, 3.24),\n", + " (, 3.9),\n", + " (, 4.54),\n", + " (, 5.21),\n", + " (, 5.87)]" + ] + }, + "metadata": {}, + "execution_count": 65 + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "Here's our system prompt and the instruction. We will add frames and images on top of it." + ], + "metadata": { + "id": "zM408Ph2o3aB" + } + }, + { + "cell_type": "code", + "source": [ + "messages = [\n", + " {\n", + " \"role\": \"system\",\n", + " \"content\": [{\"type\": \"text\", \"text\": \"You are a helpful assistant.\"}]\n", + " },\n", + "\n", + " {\n", + " \"role\": \"user\",\n", + " \"content\": [\n", + " {\"type\": \"text\", \"text\": f\"What is happening in this video? Summarize the events.\"}]\n", + " }\n", + "]" + ], + "metadata": { + "id": "2slyCVhFg2uZ" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "messages[1][\"content\"][0]" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "om5EYU8Rg3rT", + "outputId": "d72ceb2d-8bf9-406d-b39d-b42d2e254cab" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "{'type': 'text',\n", + " 'text': 'What is happening in this video? Summarize the events.'}" + ] + }, + "metadata": {}, + "execution_count": 67 + } + ] + }, + { + "cell_type": "code", + "source": [ + "for frame in frames:\n", + " image, timestamp = frame\n", + " messages[1][\"content\"].append({\"type\": \"text\", \"text\": f\"Frame {timestamp}:\"})\n", + " image.save(f\"image_{timestamp}.png\")\n", + " messages[1][\"content\"].append({\"type\": \"image\", \"url\": f\"image_{timestamp}.png\"})" + ], + "metadata": { + "id": "HVX-nZ8Vel3m" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "messages" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "0nnTc2Ayjf4B", + "outputId": "931bc8ed-2c19-4120-ddb3-0acd8745f5b1" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "[{'role': 'system',\n", + " 'content': [{'type': 'text', 'text': 'You are a helpful assistant.'}]},\n", + " {'role': 'user',\n", + " 'content': [{'type': 'text',\n", + " 'text': 'What is happening in this video? Summarize the events.'},\n", + " {'type': 'text', 'text': 'Frame 0.0:'},\n", + " {'type': 'image', 'url': 'image_0.0.png'},\n", + " {'type': 'text', 'text': 'Frame 0.63:'},\n", + " {'type': 'image', 'url': 'image_0.63.png'},\n", + " {'type': 'text', 'text': 'Frame 1.3:'},\n", + " {'type': 'image', 'url': 'image_1.3.png'},\n", + " {'type': 'text', 'text': 'Frame 1.94:'},\n", + " {'type': 'image', 'url': 'image_1.94.png'},\n", + " {'type': 'text', 'text': 'Frame 2.6:'},\n", + " {'type': 'image', 'url': 'image_2.6.png'},\n", + " {'type': 'text', 'text': 'Frame 3.24:'},\n", + " {'type': 'image', 'url': 'image_3.24.png'},\n", + " {'type': 'text', 'text': 'Frame 3.9:'},\n", + " {'type': 'image', 'url': 'image_3.9.png'},\n", + " {'type': 'text', 'text': 'Frame 4.54:'},\n", + " {'type': 'image', 'url': 'image_4.54.png'},\n", + " {'type': 'text', 'text': 'Frame 5.21:'},\n", + " {'type': 'image', 'url': 'image_5.21.png'},\n", + " {'type': 'text', 'text': 'Frame 5.87:'},\n", + " {'type': 'image', 'url': 'image_5.87.png'}]}]" + ] + }, + "metadata": {}, + "execution_count": 69 + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "Preprocess our input and infer." + ], + "metadata": { + "id": "7bN81iH8o7hY" + } + }, + { + "cell_type": "code", + "source": [ + "inputs = processor.apply_chat_template(\n", + " messages, add_generation_prompt=True, tokenize=True,\n", + " return_dict=True, return_tensors=\"pt\"\n", + ").to(model.device)" + ], + "metadata": { + "id": "pvfRxM8AhDbF" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "input_len = inputs[\"input_ids\"].shape[-1]\n", + "\n", + "generation = model.generate(**inputs, max_new_tokens=500, do_sample=False)\n", + "generation = generation[0][input_len:]\n", + "\n", + "decoded = processor.decode(generation, skip_special_tokens=True)\n", + "print(decoded)" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "uCLj5VHAhWcb", + "outputId": "0e10d454-36a6-43ad-dbd9-23fe4cadbcb2" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Here's a summary of what's happening in the video:\n", + "\n", + "The video features a beautiful, fluffy cat. Throughout the sequence, the cat is lying down, mostly looking upwards. It appears to be grooming itself, licking its nose and paws repeatedly. The cat has a relaxed and content demeanor, enjoying a moment of self-care.\n" + ] + } + ] + } + ] +} \ No newline at end of file