Liquid AI 发布 LFM2-VL-3B：面向边缘的高效视觉语言模型
LFM2-VL-3B: A New Efficient Vision-Language for the Edge | Blog | Liquid AI
Liquid AI 发布 3B 参数视觉语言模型 LFM2-VL-3B，基于 LFM2-2.6B 主干并集成 SigLIP2 400M NaFlex 编码器，支持原生分辨率与可变宽高比图像处理。该模型在 MM-IFEval 上取得 51.8%、RealWorldQA 上 71.4%，语言基准为 GPQA 30%、MMLU 63%，视觉理解覆盖日语、法语、西班牙语、德语、意大利语、葡萄牙语、阿拉伯语、中文和韩语。模型已在 Hugging Face（LFM Open License）和 LEAP 平台上线。
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We’re excited to release LFM2-VL-3B, the newest and most capable addition to our family of vision LFMs (450M and 1.6B). Built on the LFM2-2.6B backbone, this 3B parameter model targets applications that require more accuracy while maintaining the speed advantage of the LFM2 architecture. It is available today on LEAP and Hugging Face.
Flexible Architecture
LFM2-VL-3B follows the recipe adopted for our previous VLMs. It builds on our most powerful dense model, LFM2-2.6B, and integrates a SigLIP2 400M NaFlex encoder. This enables image processing at native resolutions with variable aspect ratios. Its flexible architecture allows developers to balance performance and speed by adjusting the number of vision tokens per image. This offers finer control for deployment, especially in edge environments.
You can find more information about the architecture in our LFM2-VL blog post.
Broader Capabilities
LFM2-VL-3B delivers competitive results across open-source evaluations, achieving an impressive 51.8% on MM-IFEval and 71.4% on RealWorldQA. The model shows strong performance in single- and multi-image comprehension and English OCR, with low hallucination rates on the POPE benchmark.
It maintains comparable language-only knowledge benchmark scores to its backbone, LFM2-2.6B, with 30% on GPQA and 63% on MMLU. In addition, we have significantly expanded multilingual capabilities, extending visual understanding beyond English to include Japanese, French, Spanish, German, Italian, Portuguese, Arabic, Chinese, and Korean.
LFM2-VL-3B is now available on Hugging Face under our LFM Open License, and through our LEAP platform, making cutting-edge efficient AI accessible to developers and researchers worldwide.
The LFM2 series continues to push the boundaries of efficient AI. We're proving that with the right architecture and approach, smaller models can deliver enterprise-grade performance without the computational overhead. In the future, we will continue to scale our foundation models to bring this level of efficiency to more devices and unlock new applications.
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