Liquid AI has unveiled LFM2.5, a compact AI model family designed for on-device and edge deployments, based on the LFM2 architecture. The family includes several variants like LFM2.5-1.2B-Base, LFM2.5-1.2B-Instruct, a Japanese optimized model, and vision and audio language models. These models are released as open weights on Hugging Face and are accessible via the LEAP platform. LFM2.5-1.2B-Instruct, the primary text model, demonstrates superior performance on benchmarks such as GPQA and MMLU Pro compared to other 1B class models, while the Japanese variant excels in localized tasks. The vision and audio models are optimized for real-world applications, improving over previous iterations in visual reasoning and audio processing tasks. This matters because it represents a significant advancement in deploying powerful AI models on devices with limited computational resources, enhancing accessibility and efficiency in real-world applications.
Liquid AI’s recent release of the LFM2.5 model family marks a significant advancement in the field of compact AI models, particularly for on-device and edge deployments. This new generation of models is built on the LFM2 architecture, which is specifically designed for fast and memory-efficient inference on CPUs and NPUs. Such efficiency is crucial for deploying AI models on devices with limited computational resources, enabling real-time applications across various platforms. The LFM2.5 family includes several variants, such as the LFM2.5-1.2B-Base and LFM2.5-1.2B-Instruct, as well as specialized models for Japanese language, vision language, and audio language tasks. The release of these models as open weights on Hugging Face and their integration into the LEAP platform underscores Liquid AI’s commitment to accessibility and innovation in AI technology.
The LFM2.5-1.2B-Instruct model stands out as the primary general-purpose text model in this family, showcasing impressive performance on various benchmarks. It has been fine-tuned with supervised learning, preference alignment, and multi-stage reinforcement learning to excel in instruction following, tool use, math, and knowledge reasoning. This model’s performance on benchmarks such as GPQA and MMLU Pro surpasses that of competing models like Llama-3.2-1B Instruct and Gemma-3-1B IT, making it a leading choice for applications requiring robust text processing capabilities. The ability to outperform other 1B class models highlights the effectiveness of the extended pretraining and fine-tuning processes employed by Liquid AI.
In addition to the text model, the LFM2.5 family includes a Japanese-optimized variant, LFM2.5-1.2B-JP, which is tailored for tasks specific to the Japanese language. This model achieves state-of-the-art results on Japanese benchmarks, demonstrating its potential for localized applications. The vision language model, LFM2.5-VL-1.6B, incorporates a vision tower for image understanding and is tuned for visual reasoning and OCR tasks. This model is particularly suited for real-world applications such as document understanding and user interface reading, especially in environments with edge constraints. These specialized variants illustrate the versatility of the LFM2.5 family in addressing diverse AI challenges across different languages and modalities.
The LFM2.5-Audio-1.5B model further expands the capabilities of the LFM2.5 family by supporting both text and audio inputs and outputs. This native audio language model is designed for real-time speech-to-speech conversational agents and tasks like automatic speech recognition and text-to-speech. Its efficient audio detokenizer and quantization-aware training enable deployment on devices with limited computational power without compromising performance. The introduction of these models is significant as it empowers developers to create more responsive and capable AI agents for a wide range of applications. The LFM2.5 model family represents a step forward in making advanced AI technologies more accessible and practical for use in real-world scenarios, ultimately driving innovation and enhancing user experiences.
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