Liquid AI is now officially available LFM2-VLA new generation of low-latency vision-language models for device deployment. With two highly efficient variants—LFM2-VL-450M The following are some examples of how to get started: LFM2-VL-1.6B—this launch marks a significant leap in bringing multimodal AI to smartphones, laptops, wearables, and embedded systems without compromising speed or accuracy.
The fastest and most efficient speed ever
LFM2-VL is engineered for optimum performance. up to 2× faster GPU inference The new model is more efficient than existing models in terms of benchmarks, such as image descriptions, visual questions answering and multimodal reasoning. The 450M parameters version was developed for resource-constrained settings, while the 1.5B parameters variant offers more capability and is still lightweight for mobile or high-end single GPU use.
Techni- cal Innovations
- Modular ArchitectureThe LFM2-VL combines a SigLIP2 SigLIP2 NaFlex Vision Encoder (400M/86M Parameters) with a Multimodal Projector. “pixel unshuffle” Technique that reduces the number of image tokens dynamically to speed up processing.
- Native Resolution HandlingImages are processed in their respective locations native resolution up to 512×512 pixels Without distortion due to upscaling. Larger images are split into non-overlapping 512×512 patches, preserving detail and aspect ratio. For global context, the 1.6B encodes also a thumbnail version of the entire image.
- Flexible InferenceUsers can Inference time is the perfect moment to tune speed-quality. The maximum tokens of images and the number of patches can be adjusted in real-time to suit device and application requirements.
- TraineesThe models have been pre-trained with the LFM2 core, mid-trained jointly to merge vision and language using progressive adjustments of text-to image data ratios and fine-tuned in order to understand images on around 100 billion multimodal tokens.
Benchmark Performance
LFM2-VL delivers Competitor Results On public benchmarks like RealWorldQA (MM-IFEval), OCRBench and MM Bench, the model is comparable to larger models such as InternVL3 or SmolVLM2. Memory footprint is smaller and much faster processing—making it ideal for edge and mobile applications.
The two models are available in the same sizes. Open-weight and downloadable Hugging Face is a popular way to show affection. Apache 2.0 based licenseThis license permits free research use and commercial usage by businesses. Liquid AI must be contacted for larger companies to obtain a license. The models can be integrated seamlessly with Hugging Face Transformers. They also support quantization, which will increase the efficiency of edge hardware.
Use Cases and Integrative Integration
LFM2-VL was designed with developers and enterprises in mind. Multimodal AI that is fast, accurate and efficient directly on devices—reducing cloud dependency and enabling new applications in robotics, IoT, smart cameras, mobile assistants, and more. Examples of applications are real-time captioning for images, visual searches, and multimodal interactive chatbots.
Getting Started
- DownloadThese models can be found in the Liquid AI collection.
- RunExamples of inference codes are available for platforms, such as llama.cpp. They support various quantizations levels that optimize performance across different hardware.
- CustomizeThis architecture is compatible with Liquid AI’s LEAP Platform for additional customization and deployment on multiple platforms.
The summary is:Liquid AI’s LFM2-VL is a breakthrough in open-weight, efficient vision-language models for the edge. With native resolution support, tunable speed-quality tradeoffs, and a focus on real-world deployment, it empowers developers to build the next generation of AI-powered applications—anywhere, on any device.
Take a look at the Technical Details The following are some examples of how to get started: Models on Hugging Face. Please feel free to browse our GitHub Page for Tutorials, Codes and Notebooks. Also, feel free to follow us on Twitter Join our Facebook group! 100k+ ML SubReddit Subscribe now our Newsletter.
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