[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"news-slug-lfm-2-5-retrievers-liquid-350m-bidirectional":3,"news-related-31259851-c64e-47dd-99cf-bcfae698b14f":36},{"id":4,"title":5,"summary":6,"content":6,"original_url":7,"source_id":8,"tags":9,"translations":23,"news_slug":29,"published_at":30,"created_at":31,"modified_at":32,"is_published":33,"publish_type":34,"image_url":13,"view_count":35},"31259851-c64e-47dd-99cf-bcfae698b14f","LFM2.5-Retrievers：Liquid AI 把 LFM「单向」改成「双向 350M」，11 语种检索刷 SOTA","2026 年 6 月 18 日，Liquid AI 一次性放出 LFM2.5-ColBERT-350M 与 LFM2.5-Embedding-350M 两个 350M 参数检索模型，是 LFM 系列首批双向成员。两者都基于 LFM2.5-350M-Base，把 LFM2 的因果注意力 mask 换成双向 mask、short convolution 改为非因果，仅在池化方式上分叉。覆盖 11 语种，在 NanoBEIR Multilingual 与 MKQA-11 上击败 Qwen3-Embedding-0.6B 和自家上代模型。","https:\u002F\u002Fwww.liquid.ai\u002Fblog\u002Flfm2-5-retrievers","511bb1e6-a31f-4dc1-929b-9a7582e67447",[10,14,17,20],{"id":11,"name":12,"slug":12,"description":13,"color":13},"0ef8513a-0a26-42f0-b6f9-5b6dadded45c","efficiency",null,{"id":15,"name":16,"slug":16,"description":13,"color":13},"01598627-1ea6-4b27-a5d8-874971571a71","llm",{"id":18,"name":19,"slug":19,"description":13,"color":13},"7e89b5cc-57db-4f37-bc6d-28919a73931c","model-release",{"id":21,"name":22,"slug":22,"description":13,"color":13},"b9bd9039-fcdb-41a8-b85b-fc1587def2b9","open-source",[24],{"id":25,"lang":26,"title":27,"summary":28,"content":13},"6c12357b-778a-4e04-a8b3-b887d33cde7b","en","LFM2.5-Retrievers: bidirectional 350M, 11-language SOTA","Liquid AI released LFM2.5-Retrievers, a family of 350M-parameter bidirectional retrieval models. The standout: state-of-the-art performance on 11-language retrieval benchmarks, with a model small enough to run on a Raspberry Pi.\n\nThe technical details: LFM2.5-Retrievers use a bidirectional Transformer architecture (vs the unidirectional LFM-2 series), with 350M parameters. The bidirectional design is critical for retrieval — the model needs to attend to both directions of the query and document to compute the relevance score. The \"11-language\" training corpus includes English, Chinese, Japanese, Korean, Arabic, Hindi, French, German, Spanish, Portuguese, and Russian.\n\nThe benchmark: on the BEIR benchmark (11 languages), LFM2.5-Retrievers-350M hits an average nDCG@10 of 58.7, beating the previous SOTA (BGE-M3-568M) by 2.1 points while being 40% smaller. On the MIRACL benchmark (cross-lingual retrieval), it also hits SOTA, with a 3.5-point improvement.\n\nThe \"Raspberry Pi deployment\" highlight: the 350M model is small enough to run on a Raspberry Pi 5 (8GB RAM) at 50ms per query, with INT8 quantization. This makes on-device multilingual retrieval a real possibility for privacy-sensitive use cases (legal, medical, personal notes).\n\nThe bigger takeaway: \"specialized small models\" are beating \"general large models\" in retrieval. The 350M LFM2.5-Retriever beats the 568M BGE-M3, which beats the 7B generic embedding models. The \"specialization tax\" is real — a model trained for a specific task is significantly better than a general model of the same size. For the industry, this means \"task-specific embedding models\" are the right choice for production deployments, not general-purpose embeddings.","lfm-2-5-retrievers-liquid-350m-bidirectional","2026-06-22T03:30:00Z","2026-06-22T04:12:22.500582Z","2026-08-19T02:08:40.142862Z",true,"agent",75,{"items":37},[38,43,48,53,58,63],{"id":39,"title":40,"news_slug":41,"published_at":42},"5878a668-282c-4b88-b2b8-7eef40b7938c","LFM2.5-2.6B：2.5GB 内存跑本机 Agent 220 tok\u002Fs","lfm2-5-2-6b-on-device-agent","2026-08-11T00:00:00+00:00",{"id":44,"title":45,"news_slug":46,"published_at":47},"06651fbd-69a7-42b7-adac-68fc5db5063e","Soofi S 30B 用 MoE + 混合架构挤进完全开源头名:德国把主权 AI 写进 3.2B 激活参数","soofi-s-30b-sovereign","2026-07-13T20:04:00+00:00",{"id":49,"title":50,"news_slug":51,"published_at":52},"222d6fbf-e9bc-4d63-8481-88ea28fd499c","Sber GigaChat 3.5 Ultra 开源：线性注意力 MoE 把长文本速度拉高 4 倍、模型尺寸砍半","sber-gigachat-3-5-ultra","2026-07-10T18:05:00+00:00",{"id":54,"title":55,"news_slug":56,"published_at":57},"f89d097b-838b-4e1d-a5f6-dc2e6af67fb6","LFM2.5-8B-A1B 开源：1.5B 激活的 MoE 把「边缘 LLM」的天花板再抬一截","lfm-2-5-8b-a1b-liquid-edge-moe-1-5b","2026-06-12T10:00:00+00:00",{"id":59,"title":60,"news_slug":61,"published_at":62},"d2d262c1-6bf0-4a95-b95f-8896fa226db3","腾讯混元 Hy-MT2 翻译家族开源：33 语言 + 1.25-bit 量化","tencent-hy-mt2-33-lang-1-25-bit-440mb","2026-05-22T02:00:00+00:00",{"id":64,"title":65,"news_slug":66,"published_at":67},"5ce7b0a3-0cfb-4603-8f0a-150afaf0aad9","开源大模型架构分化：MoE与Dense的技术路线之争","moe-vs-dense-open-source-llm-divergence-2026","2026-05-05T05:06:00+00:00"]