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--- |
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base_model: |
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- Qwen/Qwen2.5-VL-3B-Instruct |
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--- |
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This is the [Qwen/Qwen2.5-VL-3B-Instruct](https://huggingface.co/Qwen/Qwen2.5-VL-3B-Instruct) model, converted to OpenVINO, with int4 weights for the language model, int8 weights for the other models. |
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The INT4 weights are compressed with symmetric, channel-wise quantization, with AWQ and scale estimation. The model works on CPU, GPU and NPU. See below for the model export command/properties. |
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** This is subject to Qwen Research License ** |
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## Download Model |
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To download the model, run `pip install huggingface-hub[cli]` and then: |
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``` |
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huggingface-cli download llmware/Qwen2.5-VL-3B-Instruct-ov-int4-npu --local-dir Qwen2.5-VL-3B-Instruct-ov-int4-npu |
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``` |
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## Run inference with OpenVINO GenAI |
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Use OpenVINO GenAI to run inference on this model. This model works with OpenVINO GenAI 2025.3 and later. For NPU inference, make sure to use the latest NPU driver ([Windows](https://www.intel.com/content/www/us/en/download/794734/intel-npu-driver-windows.html), [Linux](https://github.com/intel/linux-npu-driver)) |
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- Install OpenVINO GenAI and pillow: |
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``` |
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pip install --upgrade openvino-genai pillow |
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``` |
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- Download a test image: `curl -O "https://storage.openvinotoolkit.org/test_data/images/dog.jpg"` |
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- Run inference: |
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```python |
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import numpy as np |
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import openvino as ov |
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import openvino_genai |
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from PIL import Image |
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# Choose GPU instead of NPU to run the model on Intel integrated or discrete GPU, or CPU to run on CPU. |
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# CACHE_DIR caches the model the first time, so subsequent model loading will be faster |
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pipeline_config = {"CACHE_DIR": "model_cache"} |
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pipe = openvino_genai.VLMPipeline("Qwen2.5-VL-3B-Instruct-ov-int4-npu", "NPU", **pipeline_config) |
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image = Image.open("dog.jpg") |
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# optional: resizing to a smaller size (depending on image and prompt) is often useful to speed up inference. |
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image = image.resize((128, 128)) |
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image_data = np.array(image.getdata()).reshape(1, image.size[1], image.size[0], 3).astype(np.uint8) |
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image_data = ov.Tensor(image_data) |
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prompt = "Can you describe the image?" |
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result = pipe.generate(prompt, image=image_data, max_new_tokens=100) |
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print(result.texts[0]) |
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``` |
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See [OpenVINO GenAI repository](https://github.com/openvinotoolkit/openvino.genai?tab=readme-ov-file#performing-visual-language-text-generation) |
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## Model export properties |
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Model export command: |
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``` |
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optimum-cli export openvino -m Qwen/Qwen2.5-VL-3B-Instruct --weight-format int4 --group-size -1 --sym --awq --scale-estimation --dataset contextual Qwen2.5-VL-3B-Instruct-ov-int4- |
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npu |
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``` |
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### Framework versions |
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``` |
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openvino_version : 2025.3.0-19807-44526285f24-releases/2025/3 |
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nncf_version : 2.17.0 |
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optimum_intel_version : 1.26.0.dev0+0e2ccef |
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optimum_version : 1.27.0 |
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pytorch_version : 2.7.1 |
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transformers_version : 4.51.3 |
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``` |
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### LLM export properties |
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``` |
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all_layers : False |
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awq : True |
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backup_mode : int8_asym |
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compression_format : dequantize |
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gptq : False |
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group_size : -1 |
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ignored_scope : [] |
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lora_correction : False |
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mode : int4_sym |
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ratio : 1.0 |
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scale_estimation : True |
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sensitivity_metric : max_activation_variance |
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``` |
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