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--- |
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license: apache-2.0 |
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language: |
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- multilingual |
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base_model: |
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- Qwen/Qwen2-VL-2B-Instruct |
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tags: |
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- mmeb |
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- vidore |
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- colpali |
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- multimodal-embedding |
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pipeline_tag: feature-extraction |
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--- |
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# Ops-MM-embedding-v1-2B |
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**Ops-MM-embedding-v1-2B** is a dense, large-scale multimodal embedding model developed and open-sourced by the Alibaba Cloud OpenSearch-AI team, fine-tuned from Qwen2-VL. |
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## **Key Features** |
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### Unified Multimodal Embeddings |
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- Encodes text, images, text-image pairs, visual documents, and videos (by treating video frames as multiple image inputs) into a unified embedding space for cross-modal retrieval. |
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### High Performance on MMEB |
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- Achieves **SOTA results** among models of similar scale on **MMEB-V2** and **MMEB-Image** benchmark (until 2025-07-03). |
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### Multilingual Capabilities |
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- The larger variant (**Ops-MM-embedding-v1-7B**) achieves SOTA performance among dense models on the ViDoRe-v2 benchmark, demonstrating strong cross-lingual generalization. |
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## Training data |
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MMEB-train, CC-3M, colpali training set. |
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## Performance |
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### MMEB-V2 |
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| Model | Model Size (B) | Overall | Image-Overall | Video-Overall | Visdoc-Overall | |
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| ------------------------ | -------------- | ------- | ------------- | ------------- | -------------- | |
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| seed-1.6-embedding | unknown | 71.27 | 77.78 | 55.34 | 73.44 | |
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| Ops-MM-embedding-v1-7B | 8.29 | 67.61 | 72.72 | 53.76 | 70.34 | |
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| Ops-MM-embedding-v1-2B | 2.21 | 63.44 | 69.03 | 47.56 | 66.96 | |
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| VLM2Vec-V2.0-Qwen2VL-2B | 2.21 | 58.02 | 64.85 | 34.85 | 65.36 | |
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| gme-Qwen2-VL-7B-Instruct | 8.29 | 57.83 | 55.95 | 38.43 | 75.18 | |
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| gme-Qwen2-VL-2B-Instruct | 2.21 | 54.08 | 51.89 | 33.64 | 72.71 | |
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### MMEB-Image |
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The table below compares performance on MMEB-Image benchmark among models of similar size. |
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| Model | Model Size (B) | Image-Overall | I-CLS | I-QA | I-RET | I-VG | |
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| ---------------------- | -------------- | ------------- | ----- | ----- | ----- | ----- | |
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| Ops-MM-embedding-v1-2B | 2.21 | **69.03** | 68.07 | 65.11 | 69.17 | 80.85 | |
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| B3_Qwen2_2B | 2.21 | 68.1 | 67 | 61.19 | 70.85 | 79.88 | |
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| LLaVE-2B | 1.95 | 65.2 | 62.1 | 60.2 | 65.2 | 84.9 | |
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### ViDoRe-v2 |
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| Model | Avg | ESG Restaurant Human | MIT Bio Multi. | Econ Macro Multi. | ESG Restaurant Synth. Multi. | |
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| ---------------------- | --------- | -------------------- | -------------- | ----------------- | ---------------------------- | |
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| gme-7B | 55.61 | 63.37 | 49.49 | 54.21 | 55.38 | |
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| seed 1.6 embedding | 56.57 | 63.3 | 57.14 | 53.85 | 51.99 | |
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| Ops-MM-embedding-v1-7B | **59.59** | 66.27 | 54.34 | 60.92 | 56.82 | |
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| Ops-MM-embedding-v1-2B | 53.18 | 58.57 | 52.87 | 47.89 | 53.39 | |
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## Usage |
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```python |
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from ops_mm_embedding_v1 import OpsMMEmbeddingV1, fetch_image |
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model = OpsMMEmbeddingV1( |
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"OpenSearch-AI/Ops-MM-embedding-v1-2B", |
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device="cuda", |
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attn_implementation="flash_attention_2" |
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) |
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t2i_prompt = "Find an image that matches the given text." |
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texts = [ |
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"The Tesla Cybertruck is a battery electric pickup truck built by Tesla, Inc. since 2023.", |
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"Alibaba office.", |
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"Alibaba office.", |
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] |
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images = [ |
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"https://upload.wikimedia.org/wikipedia/commons/e/e9/Tesla_Cybertruck_damaged_window.jpg", |
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"https://upload.wikimedia.org/wikipedia/commons/e/e0/TaobaoCity_Alibaba_Xixi_Park.jpg", |
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"https://upload.wikimedia.org/wikipedia/commons/thumb/b/b0/Alibaba_Binjiang_Park.jpg/1024px-Alibaba_Binjiang_Park.jpg" |
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] |
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images = [fetch_image(image) for image in images] |
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# Text and image embedding |
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text_embeddings = model.get_text_embeddings(texts) |
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image_embeddings = model.get_image_embeddings(images) |
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print('Text and image embeddings', (text_embeddings @ image_embeddings.T).tolist()) |
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# Fused Embedding |
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text_with_image_embeddings = model.get_fused_embeddings(texts=texts, images=images, instruction=t2i_prompt) |
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print('Text and image embeddings', (text_embeddings @ image_embeddings.T).tolist()) |
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# Multi-image embeddings |
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multi_images = [ |
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[images[0]], |
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[images[1], images[2]], |
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] |
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multi_image_embeddings = model.get_image_embeddings(multi_images) |
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print('Multi-image embeddings', (multi_image_embeddings @ multi_image_embeddings.T).tolist()) |
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``` |
