Qwen2.5-VL-3B

Qwen2.5-VL-3B is a compact yet powerful multimodal model with 3 billion parameters, specifically designed for efficient deployment on edge devices without compromising performance. The model combines a Vision Transformer encoder with the Qwen2.5 series decoder, enabling image, video, and text processing through innovative approaches like dynamic resolution handling and absolute temporal encoding. This allows it to process images of varying sizes and videos of up to one hour in length, with second-level event localization.

A key feature of the 3B model is its high-quality document analysis capability, including OCR, object detection, video understanding, and computer interface automation. Trained on approximately 4 trillion tokens across multiple modalities, the model achieves deep visual comprehension and can generate structured JSON outputs for object coordinates and attributes. The innovative Window Attention mechanism in its vision encoder significantly reduces computational costs, scaling linearly rather than quadratically—making it ideal for mobile and edge devices.

The model delivers outstanding results on key benchmarks: 53.1% on MMMU, 93.9% on DocVQA, 79.3% on TextVQA, and 62.3% on MathVista. Its performance in agent-based tasks is particularly impressive, scoring 76.9% on AITZ and 90.8% on AndroidWorld, demonstrating robust UI interaction capabilities.

Use cases for the 3B model span a wide range, from mobile document analysis (OCR) to security system integration. Licensed under Apache-2.0, it can be freely incorporated into commercial products, making it an attractive choice for startups and enterprises pursuing AI innovation.

Announce Date: 19.02.2025
Parameters: 3.75B
Context: 128K
Attention Type: Full Attention
VRAM requirements: 6.1 GB using 4 bits quantization
Developer: Alibaba
Transformers Version: 4.41.2
License: Apache 2.0

Use our pre-built public endpoints to test inference and explore Qwen2.5-VL-3B capabilities.

Rent your own physically dedicated instance with hourly or long-term monthly billing.

We recommend deploying private instances in the following scenarios:

• maximize endpoint performance,
• enable full context for long sequences,
• ensure top-tier security for data processing in an isolated, dedicated environment,
• use custom weights, such as fine-tuned models or LoRA adapters.

Contact our dedicated neural networks support team at nn@immers.cloud or send your request to the sales department at sale@immers.cloud.