Try LFM • Docs • LEAP • Discord LFM2.5 Encoder 350M LFM2.5 Encoder is a family of multilingual bidirectional encoders built on the LFM2 architecture, available in two sizes: LFM2.5 Encoder 230M — a lightweight encoder for tight latency and memory budgets, punching above its size class. LFM2.5 Encoder 350M (this model) — a larger sibling for maximum downstream quality. Both are masked language models with full bidirectional attention, designed to be fine tuned into task specific models (classification, token classification, retrieval, reranking, and semantic similarity) across 15 languages, and to run efficiently on device. Find more details about our encoders in our blog post. Key highlights: Top quality for its size. Ahead of every model its size or smaller, and ~5 points above our own retrieval siblings. General purpose. 8k context, strong across NLI, paraphrase, sentiment, and multilingual tasks. Fast and on device. Matches or beats ModernBERT throughput, with a long context edge on CPU. [!NOTE] 💻 Demos : We built the demos below from fine tuned LFM2.5 Encoders. Each one runs in a CPU only Hugging Face space: Zero shot prompt routing — define your own routing lanes as free text…
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