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待翻譯:LFM2.5-2.6B: On-Device Agents

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AI 服務暫時不可用,以下為來源摘要,待恢復後補全翻譯:Liquid Foundation Models Models built for real-world constraints. We’ve redefined what’s possible, designed for efficiency, speed, and real-world deployment on any device. Build with LEAPView docs From wearables to robo…

來源Hacker News AI作者: achrono
待翻譯:LFM2.5-2.6B: On-Device Agents
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AI 服務暫時不可用,以下為來源正文,待恢復後補全翻譯。

Liquid Foundation Models Models built for real-world constraints. We’ve redefined what’s possible, designed for efficiency, speed, and real-world deployment on any device. Build with LEAPView docs From wearables to robotics, phones, laptops, cars, and more, LFMs run seamlessly on GPUs, CPUs, or NPUs, making intelligence accessible everywhere. Explore LFMs Frontier-grade intelligence at a fraction of the compute, on any hardware, on or off the cloud. Every LFM is free to download, run, and fine-tune — including commercially — until your company passes $10M in annual revenue. See pricing & licensing Customize your model with us Text Models LFMs deliver powerful performance in a lightweight, customizable, and compute-efficient footprint for deployment in any environment. LFM2.5-2.6B DemoDocsBlog LFM2.5-1.2B-Base DocsBlog LFM2.5-1.2B-Thinking DemoLEAPDocsBlog LFM2.5-230M DocsBlog LFM2.5-350M LEAPDocsBlog LFM2.5-8B-A1B DemoDocsBlog LFM2.5-1.2B-JP-202606 LEAPDocs LFM2-2.6B LEAPDocsBlog LFM2-700M LEAPDocsBlog LFM2-24B-A2B DocsBlog LFM2-1.2BDeprecated Blog LFM2-350MDeprecated Blog LFM2-8B-A1BDeprecated Blog Vision-Language Models Multimodal models using vision and text inputs and outputs with capabilities designed for low latency and device aware deployment. LFM2.5-VL-1.6B DemoLEAPDocs LFM2.5-VL-450M DemoLEAPDocsBlog LFM2-VL-3B LEAPDocsBlog LFM2-VL-450MDeprecated Blog Audio Model End-to-end foundation model for audio and text generation. Designed for low latency, it enables responsive, high-quality conversations with only 1.5 billion parameters. LFM2.5-Audio-1.5B DemoLEAPDocsBlog LFM2.5-Audio-1.5B-JP Docs Nano Models Tiny customized models for specific tasks and knowledge. LFM2.5-Encoder-350M DemoDocsBlog LFM2.5-Encoder-230M DocsBlog LFM2.5-1.2B-Instruct LEAPDocsBlog LFM2.5-Embedding-350M DocsBlog LFM2.5-ColBERT-350M DocsBlog LFM2.5-VL-450M-Extract Docs LFM2.5-VL-1.6B-Extract Docs LFM2-2.6B-Exp LEAP LFM2-350M-Math LEAPDocsBlog LFM2-350M-PII-Extract-JP LEAPDocsBlog LFM2-2.6B-Transcript LEAPDocsBlog LFM2-350M-ENJP-MT LEAPDocsBlog LFM2-350M-ExtractDeprecated Blog LFM2-1.2B-ExtractDeprecated Blog LFM2-1.2B-RAGDeprecated Blog LFM2-ColBERT-350MDeprecated Blog LFM2-1.2B-ToolDeprecated Blog ShieldFlow (LFM PII) Request accessTalk to our team LFM2-2.6B-MMAI BlogRequest accessTalk to our team Custom Models Purpose-built LFMs shaped around your data, use case, and constraints. Don’t see the model you need?Request a model Want to train an LFM on your own data?Explore fine-tuning Built for efficient inference everywhere you need it. Unmatched speed, quality, and memory-efficiency on the edge, or in the cloud. Whether deploying on smartphones, laptops, vehicles, or any other device, LFMs run efficiently on CPU, GPU, and NPU hardware. Designed for millisecond latency, on-device resilience, and data privacy, LFMs unlock the full potential of local, cloud, and hybrid AI across industries. Decode throughput on Galaxy S26 Ultra · SM8850 · CPU · llama.cpp b9050 · Android · Q4_K_M Each device runs its platform's native runtime config. Latest model releases Read more AUG 4, 2026Models LFM2.5-2.6B: Deploy Agents Everywhere 07.28 07.28Models LFM2.5-Encoders: Fast at Long Context, Even on CPU JUL 28, 2026Models LFM2.5-Encoders: Fast at Long Context, Even on CPU 06.25 06.25Models LFM2.5-230M: Built to Run Anywhere JUN 25, 2026Models LFM2.5-230M: Built to Run Anywhere 06.18 06.18Models LFM2.5 Retrievers: Bi-directional LFMs for Fast Multilingual Search JUN 18, 2026Models LFM2.5 Retrievers: Bi-directional LFMs for Fast Multilingual Search

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要點

  • AI 服務暫時不可用,系統已先保留來源內容與降級元數據。
  • Liquid Foundation Models Models built for real-world constraints. We’ve redefined what’s possible, designed for efficiency, speed, and real-world deployment on any device. Build w…

技術影響

可能影響 GPU、推理集羣、算力成本和供應鏈規劃。

要點與分析由自動化流程生成,可能有誤,請結合原始來源核實。