Model Releases
LFM2.5-Encoders: Fast at Long Context, Even on CPU
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 budge
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 — 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. Key highlights: Highly capable for its size. On par with the best similarly sized encoders and well ahead of 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; runs in the browser on WebGPU. 📄 Model details Property LFM2.5-Encoder-230M LFM2.5-Encoder-350M Type Bidirectional encoder (masked language model) Bidirectional encoder (masked language model) Backbone LFM2 LFM2 Total parameters ~229.7M ~354.5M Hidden size 1024 1024 Vocabulary size 65,536 65,536 Context length 8,192 tokens 8,192 tokens License LFM Open License v1.0 LFM Open License v1.0 Supported languages: English, German, Spanish, French, Italian, Dutch, Polish, Portuguese, Arabic, Hindi, Japanese, Russian, Turkish, Vietnamese, Chinese (15). Training. LFM2.5-Encoder-350M is adapted from the LFM2 base and trained with a masked language modeling objective on a large multilingual corpus. Pre-training uses a two-stage schedule that extends the context window to up to 8,192 tokens. We recommend fine-tuning LFM2.5-Encoder-350M for a range of downstream tasks, such as: Text classification: sentiment, topic, intent/routing, moderation, and business-text linting. Token classification: named-entity recognition, span extraction, and sequence labeling. Retrieval and reranking: a backbone for dense embedding or late-interaction (ColBERT-style) retrievers. Semantic similarity: STS, paraphrase, and duplicate detection. Natural language inference and extractive QA: sentence-pair reasoning and answer-span extraction. submitted by /u/pmttyji [link] [comments]
Source: r/LocalLLaMA | 2026-07-28