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Let's install transformers from main for now." + ], + "metadata": { + "id": "LDVowlaaK5P7" + } + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "VAmN3_LvKjNL", + "outputId": "0facabbe-65e4-45ba-9150-381b9ad7b777" + }, + "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[32m6.0/6.0 MB\u001b[0m \u001b[31m71.6 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[32m363.4/363.4 MB\u001b[0m \u001b[31m3.1 MB/s\u001b[0m eta 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"cell_type": "code", + "source": [ + "from transformers import ColQwen2ForRetrieval, ColQwen2Processor\n", + "import torch\n", + "\n", + "model_name = \"vidore/colqwen2-v1.0-hf\"\n", + "\n", + "model = ColQwen2ForRetrieval.from_pretrained(\n", + " model_name,\n", + " torch_dtype=torch.bfloat16,\n", + " device_map=\"auto\",\n", + " attn_implementation=\"flash_attention_2\",\n", + ")\n", + "processor = ColQwen2Processor.from_pretrained(model_name)" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 484, + "referenced_widgets": [ + "b6fc374b26ba4d73bf8c39da9bfded80", + "78cb27265325446b8ad40fdd533e818d", + "3ededd5b7d7b470a9d9f19da99f60cb6", + "7e25413502ac49248d794bd77510b8d0", + "2b13ef42cf724ad584b35e6aedb75729", + "0a8e81e803884e6a9c917db4a95f0d94", + "0f7d3acbc0ca4969b6ed9c998ec4bdbb", + "e2fc14c60a034c389ebe5394d2becdaf", + "9362167da12f467e90744658f32537a1", + "2a70f2d14c5c4fa38b7de264c16fb828", + "fb07c774d70c41798a02468f07afbb52", + 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"d55cf8b8-c346-41e4-d42d-e2d98ba260c5" + }, + "execution_count": 2, + "outputs": [ + { + "output_type": "stream", + "name": "stderr", + "text": [ + "/usr/local/lib/python3.11/dist-packages/huggingface_hub/utils/_auth.py:94: 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": [ + "config.json: 0%| | 0.00/6.08k [00:00] 157.34K --.-KB/s in 0.05s \n", + "\n", + "2025-06-02 15:18:24 (2.99 MB/s) - ‘US-original-Declaration-1776.jpg’ saved [161116/161116]\n", + "\n", + "--2025-06-02 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Image.open(\"/content/500px-Romeoandjuliet1597.jpg\"),\n", + " Image.open(\"/content/US-original-Declaration-1776.jpg\"),\n", + "]\n", + "\n", + "queries = [\n", + " \"When was the United States Declaration of Independence proclaimed?\",\n", + " \"Who printed the edition of Romeo and Juliet?\",\n", + "]\n", + "\n", + "# Process the inputs\n", + "inputs_images = processor(images=images).to(model.device)\n", + "inputs_text = processor(text=queries).to(model.device)" + ], + "metadata": { + "id": "nW9QC3_TKmkx" + }, + "execution_count": 8, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "We can now infer." + ], + "metadata": { + "id": "yXJurtigObn9" + } + }, + { + "cell_type": "code", + "source": [ + "# Forward pass\n", + "with torch.no_grad():\n", + " image_embeddings = model(**inputs_images).embeddings\n", + " query_embeddings = model(**inputs_text).embeddings\n", + "\n", + "# Score the queries against the images\n", + "scores = processor.score_retrieval(query_embeddings, image_embeddings)\n", + "\n", + "print(\"Retrieval scores (query x image):\")\n", + "print(scores)" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "FKr8bS7VNQT5", + "outputId": "81592aad-7354-4e6d-f7e1-f7819b7f977a" + }, + "execution_count": 9, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Retrieval scores (query x image):\n", + "tensor([[ 8.6250, 16.5000],\n", + " [16.3750, 9.3750]], dtype=torch.bfloat16)\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "As you can see, the first image is most similar to second text prompt and the second image is the most similar to first prompt!" + ], + "metadata": { + "id": "lh1W6607Oexk" + } + }, + { + "cell_type": "markdown", + "source": [ + "To learn more, check out [the model repository](https://huggingface.co/vidore/colqwen2-v1.0) and the [transformers docs](https://huggingface.co/docs/transformers/main/en/model_doc/colqwen2)." + ], + "metadata": { + "id": "t2yXdAWzOn_h" + } + } + ] +} \ No newline at end of file