Introducing LFM2.5-Embedding-350M and LFM2.5-ColBERT-350M: two multilingual retrieval models built for ultra-fast and accurate search across 11 languages.
> End-to-end retrieval latency as low as 1.5ms with our enterprise stack! 🚀
> Consistently best-in-class multilingual and cross-lingual performance across Arabic, German, English, Spanish, French, Italian, Japanese, Korean, Norwegian, Portuguese, and Swedish.
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Retrieval models force a tradeoff between speed and accuracy. These two sit at different points on that curve.
> LFM2.5-Embedding-350M: one vector per document. Fastest search, smallest index.
> LFM2.5-ColBERT-350M: one vector per token. Word-level query matching, higher accuracy, larger index.
(2/n)
Both are built on LFM2.5-350M-Base with bidirectional patches, the first full-context encoders in the LFM family.
> Both run via llama.cpp GGUFs (CPUs, Laptops, edge devices) <10ms end-to-end query embedding latency.
> Both fly on GPUs with our custom runtime <2ms end-to-end query embedding latency.
(3/n)
Especially well-suited for short-context collections: product catalogs, FAQ, knowledge bases, support docs, and multilingual search use cases where speed and reliability matter.
LFM2.5-Embedding-350M and LFM2.5-ColBERT-350M are available now.
> Blog post: https://t.co/OG5MXl6cEW
> LFM2.5-Embedding-350M: https://t.co/ZGchsL7Vc7
> LFM2.5-ColBERT-350M: https://t.co/cAZ0cOO99k
> Docs: https://t.co/SyQJEpJd6h
embedding — A list of numbers representing a piece of text's meaning, so that similar meanings end up numerically close and can be searched.
token — The chunk of text a model reads and writes in — roughly three-quarters of a word — and the unit AI usage is billed in.
context window — The maximum amount of text a model can consider at once — its working memory for the current conversation or task.
Why it matters
A dense and a late-interaction retriever from the same 350M base, so index size can be traded against accuracy for catalogs, FAQs and support docs across 11 languages without changing stacks. Query embeddingA list of numbers representing a piece of text's meaning, so that similar meanings end up numerically close and can be searched.Full definition → runs under 10ms on CPU.
