Liquid AI’s LFM2.5 encoders bring 8k multilingual retrieval to 230M and 350M models
pmttyji · reddit · 2026-07-29
Liquid AI’s LFM2.5-Encoder line introduces two multilingual bidirectional encoders: 230M and 350M parameters.
The models are built for fine-tuning on classification, token labeling, retrieval, reranking, semantic similarity, NLI, and extractive QA across 15 languages. The release emphasizes long context and efficiency: both variants support 8k context, run well on-device, and can even execute in the browser on WebGPU. The attached benchmark shows the 350M model scoring 81.02 on a 17-task fine-tuning benchmark, ahead of the 230M variant at 79.29 and competitive with larger encoders.
Related event: Liquid AI Unveils LFM2.5 Encoders Optimized for CPU and Long Context(6 posts)→
More from Models
- Users discuss what they actually use Opus 5 for beyond coding — remilouf · 2026-07-29
- Anthropic Hints at Achieving Recursive Self-Improvement, Calls for Pacing Frontier — daniel_mac8 · 2026-07-29
- Claude Opus 5 tops DeepSWE with a 74% score and a claimed 28% cost edge — daniel_mac8 · 2026-07-29
- LiquidAI’s 230M LFM2.5 encoder trends on Hugging Face — LiquidAI · 2026-07-29
- Anthropic may be 1.5 generations ahead internally, with Fable 5.1 weeks away — haider1 · 2026-07-29
- Moonshot’s Kimi K3 is a 2.8T open-weight MoE model with 1M-token context — alex_verem · 2026-07-29