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Liquid AI Releases LFM2-VL: Super-Fast, Open-Weight Vision-Language Models Designed for Low-Latency and Device-Aware Deployment

ByRicardo

Liquid AI has formally launched LFM2-VL, a brand new household of vision-language basis fashions optimized for low-latency, on-device deployment. With two extremely environment friendly variants—LFM2-VL-450M and LFM2-VL-1.6B—this launch marks a big leap in bringing multimodal AI to smartphones, laptops, wearables, and embedded techniques with out compromising velocity or accuracy.

Unprecedented Pace and Effectivity

LFM2-VL fashions are engineered to ship as much as 2× quicker GPU inference in comparison with current vision-language fashions, whereas sustaining aggressive benchmark efficiency on duties like picture description, visible query answering, and multimodal reasoning. The 450M-parameter variant is tailor-made for extremely resource-constrained environments, whereas the 1.6B-parameter model provides higher functionality whereas nonetheless remaining light-weight sufficient for single-GPU or high-end cell use.

https://www.liquid.ai/weblog/lfm2-vl-efficient-vision-language-models

Technical Improvements

  • Modular Structure: LFM2-VL combines a language mannequin spine (LFM2-1.2B or LFM2-350M), a SigLIP2 NaFlex imaginative and prescient encoder (400M or 86M parameters), and a multimodal projector with a “pixel unshuffle” approach that dynamically reduces picture token counts for quicker processing.
  • Native Decision Dealing with: Photos are processed at their native decision as much as 512×512 pixels with out distortion from upscaling. Bigger pictures are break up into non-overlapping 512×512 patches, preserving element and facet ratio. The 1.6B mannequin additionally encodes a downscaled thumbnail of the complete picture for international context understanding.
  • Versatile Inference: Customers can tune the speed-quality tradeoff at inference time by adjusting most picture tokens and patch rely, permitting real-time adaptation to gadget capabilities and software wants.
  • Coaching: The fashions had been first pre-trained on the LFM2 spine, then collectively mid-trained to fuse imaginative and prescient and language capabilities utilizing a progressive adjustment of text-to-image knowledge ratios, and eventually fine-tuned for picture understanding on roughly 100 billion multimodal tokens.

Benchmark Efficiency

LFM2-VL delivers aggressive outcomes on public benchmarks equivalent to RealWorldQA, MM-IFEval, and OCRBench, rivaling bigger fashions like InternVL3 and SmolVLM2, however with a smaller reminiscence footprint and far quicker processing—making it ultimate for edge and cell purposes.

Each mannequin sizes are open-weight and downloadable on Hugging Face beneath an Apache 2.0-based license, allowing free use for analysis and business use by corporations. Bigger enterprises should contact Liquid AI for a business license. The fashions combine seamlessly with Hugging Face Transformers and assist quantization for additional effectivity good points on edge {hardware}.

https://www.liquid.ai/weblog/lfm2-vl-efficient-vision-language-models

Use Circumstances and Integration

LFM2-VL is designed for builders and enterprises searching for to deploy quick, correct, and environment friendly multimodal AI instantly on gadgets—lowering cloud dependency and enabling new purposes in robotics, IoT, good cameras, cell assistants, and extra. Instance purposes embody real-time picture captioning, visible search, and interactive multimodal chatbots.

Getting Began

  • Obtain: Each fashions can be found now on the Liquid AI Hugging Face assortment.
  • Run: Instance inference code is offered for platforms like llama.cpp, supporting numerous quantization ranges for optimum efficiency on completely different {hardware}.
  • Customise: The structure helps integration with Liquid AI’s LEAP platform for additional customization and multi-platform edge deployment.

In abstract, Liquid AI’s LFM2-VL units a brand new commonplace for environment friendly, open-weight vision-language fashions on the sting. With native decision assist, tunable speed-quality tradeoffs, and a give attention to real-world deployment, it empowers builders to construct the subsequent era of AI-powered purposes—wherever, on any gadget.

Take a look at the Technical Details and Models on Hugging Face. Be happy to take a look at our GitHub Page for Tutorials, Codes and Notebooks. Additionally, be at liberty to comply with us on Twitter and don’t overlook to affix our 100k+ ML SubReddit and Subscribe to our Newsletter.

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