From the source
Today, we're releasing LFM2.5-350M , an improved version of our 350M model with additional pre-training (from 10T to 28T tokens) and large-scale reinforcement learning.
Built on the LFM2 architecture, it delivers exceptionally fast inference and runs everywhere, from cloud GPUs to cheap CPUs.
It excels at tool use, data extraction, and structured outputs - all at 350M parameters - making it purpose-built for large-scale data processing and function calling at the edge.
To meet developers where they are, we ensure our models run seamlessly on preferred inference engines and hardware across CPUs, NPUs, and GPUs.
We are thrilled to welcome Zetic , RunAnywhere , and Mirai to our local AI ecosystem, joining AMD , Qualcomm Technologies , Intel , LM Studio , and Cactus Compute to drive robust edge deployments across vehicles, smartphones, laptops, IoT, and embedded systems.
Additionally, our new collaboration with Distil Labs empowers teams to effortlessly fine-tune highly efficient LFMs models to replace expensive and slow LLMs using just model traces.
The base (LFM2.5-350M-Base) and post-trained (LFM2.5-350M) models are available today on Hugging Face , LEAP , and our Playground .
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