on-device AI
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Liquid AI’s LFM2-2.6B-Transcript: Fast On-Device AI Model
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Liquid AI has introduced the LFM2-2.6B-Transcript, a highly efficient AI model for transcribing meetings, which operates entirely on-device using the AMD Ryzen™ AI platform. This model provides cloud-level summarization quality while significantly reducing latency, energy consumption, and memory usage, making it practical for use on devices with as little as 3 GB of RAM. It can summarize a 60-minute meeting in just 16 seconds, offering enterprise-grade accuracy without the security and compliance risks associated with cloud processing. This advancement is crucial for businesses seeking secure, fast, and cost-effective solutions for handling sensitive meeting data.
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Liquid AI’s LFM2.5: Compact On-Device Models
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Liquid AI has introduced LFM2.5, a new family of compact on-device foundation models designed to enhance the performance of agentic applications. These models offer improved quality, reduced latency, and support for a wider range of modalities, all within the ~1 billion parameter class. LFM2.5 builds upon the LFM2 architecture with pretraining scaled from 10 trillion to 28 trillion tokens and expanded reinforcement learning post-training, enabling better instruction following. This advancement is crucial as it allows for more efficient and versatile AI applications directly on devices, enhancing user experience and functionality.
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Liquid AI’s LFM2.5: Compact On-Device Models Released
Read Full Article: Liquid AI’s LFM2.5: Compact On-Device Models Released
Liquid Ai has introduced LFM2.5, a series of compact on-device foundation models designed to enhance the performance of agentic applications by offering higher quality, reduced latency, and broader modality support within the ~1 billion parameter range. Building on the LFM2 architecture, LFM2.5 scales pretraining from 10 trillion to 28 trillion tokens and incorporates expanded reinforcement learning post-training to improve instruction-following capabilities. This release includes five open-weight model instances derived from a single architecture, including a general-purpose instruct model, a Japanese-optimized chat model, a vision-language model, a native audio-language model for speech input and output, and base checkpoints for extensive customization. This matters as it enables more efficient and versatile on-device AI applications, broadening the scope and accessibility of AI technology.