Ops Colqwen3 4B EU AI Act Compliance Profile

OpenSearch-AI/Ops-Colqwen3-4B

Ops-Colqwen3-4B is a ColPali-style multimodal embedding model based on the Qwen3-VL-4B-Instruct architecture, developed and open-sourced by the Alibaba Cloud OpenSearch-AI team. It maps text queries and visual documents such as images and PDF pages into a unified, aligned multi-vector embedding space, enabling highly effective retrieval of visual documents.

The model is trained using a multi-stage strategy that combines large-scale text-based retrieval datasets with diverse visual document data. This hybrid training approach significantly enhances its capability to handle complex document understanding and retrieval tasks. On the Vidore v1–v3 benchmarks, Ops-Colqwen3-4B achieves state-of-the-art results among models of comparable size.

Key Features

• Model size: 4 billion parameters
• Multimodal alignment: Enables fine-grained semantic alignment between text and images or PDF pages
• Multi-vector embeddings: Following the ColPali design, each input generates multiple context-aware embedding vectors; similarity is computed using MaxSim, enabling high-precision matching
• Scalable embedding dimensions: Supports embedding dimensions up to 2,560 during inference via an extended projection head, enabling higher retrieval accuracy through more expressive representations. Lower-dimensional prefixes (e.g., the first 128 or 320 dimensions) remain highly effective for lightweight applications.
• Multilingual support: Covers over 30 languages
• Context length: Supports up to 32,000 tokens
• Visual token capacity: Handles up to 1,280 visual tokens per page input.

Usage

Requirements

Basic Usage

import torch
from PIL import Image
from scripts.ops_colqwen3_embedder import OpsColQwen3Embedder

images = [Image.new("RGB", (32, 32), color="white"), Image.new("RGB", (16, 16),

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Tags

Library

Base Model