LFM2.5 is a new family of hybrid models designed for on-device deployment. It builds on the LFM2 architecture with extended pre-training and reinforcement learning.
On-device personal assistant
: Designed to power real-life applications, chaining tool calls, and following complex instructions on all devices.
Compressed performance
: Competitive with much larger dense and MoE models on instruction following and agentic tasks.
Unmatched throughput
: Fastest in its size class on both CPU and GPU inference, with day-one support for llama.cpp, MLX, vLLM, and SGLang.
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