Unsloth Dynamic 2.0 achieves superior accuracy & outperforms other leading quants. Try LFM • Docs • LEAP • Discord LFM2.5 230M LFM2.5 is a family of hybrid models designed for on device deployment . It builds on the LFM2 architecture with extended pre training and reinforcement learning. Our most compact model yet : 230M parameters that punch above their weight, bringing real capability to the tightest memory and compute budgets. Fast edge inference : Best throughput from low cost CPUs to production GPUs, running at 213 tok/s decode speed on Galaxy S25 Ultra and 42 tok/s on a Raspberry Pi 5. Built for agentic tasks : Distilled from LFM2.5 350M and refined with multi stage reinforcement learning, making it well suited for tool use and data extraction. Find more information about LFM2.5 230M in our blog post. 🗒️ Model Details Model Parameters Description LFM2.5 230M Base 230M Pre trained base model for fine tuning LFM2.5 230M 230M General purpose instruction tuned model LFM2.5 230M is a general purpose text only model with the following features: Number of parameters : 230M Number of layers : 14 (8 double gated LIV convolution blocks + 6 GQA blocks) Training budget : 19T tokens Cont…
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