AI BriefingLFM3-VL-2B: 2B-parameter multimodal model achieves 45.7 points on MMMU-Pro
LiquidAI/LFM3-VL-2B
About the project
LFM3-VL-2B, released by LiquidAI, is a multimodal language model that simultaneously understands text, images, and video. It is lightweight with a 2B parameter scale, enabling inference in local environments or on edge devices.
It supports an image-text-to-text pipeline that responds to image or video inputs with text. Built on the Transformers library, it includes a dedicated chat template for handling complex tool calls and reasoning processes.
It recorded 45.7 points on the MMMU-Pro benchmark, demonstrating its performance as a small model. It also publishes results on various multimodal evaluation datasets such as ScreenSpot-Pro, allowing developers to pre-validate suitability for specific tasks.
It can serve as an alternative for developers who find the high computational costs of large models burdensome. It is suitable for projects aiming to implement multimodal tasks such as image-based question answering, video summarization, and visual information extraction in lightweight environments.
LiquidAI/LFM3-VL-2B
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