LFM2.5: 2.6 billion parameters trained for agent work right on the device

For a long time the small model was a trimmed-down copy of the big one - the same ambitions, less brain. Here it's the opposite. A small model trained end to end for one single job: to be an agent.

Here's what survives my skeptical eye: they write down where they don't lead. You read a vendor benchmark with a grain of salt - they measured their own thing and chose what to show - but a table where you admit a lost test carries more weight than a table full of first places.

I find the training itself more interesting. The last stage is reinforcement learning inside real agent harnesses - the model isn't learning conversation, it's learning work: call a tool, read the result, keep going. This is where the category's maturity shows - the on-device model is now being built for the specific harness, with agentic work baked in from training onward.

An agent with no monthly bill and no data leaving the machine.

I plan to try it myself. Your routine tasks - classification, extraction, a local document assistant - don't need a giant in the cloud. If 2.6 billion parameters on a laptop can carry that work reliably, the heavy model stays reserved for the heavy task. The check is easy, because the weights are public.

The visual is generated code-art, no third-party images.