New ModelsOpen weights
LFM2.5-2.6B
Liquid's LFM2.5-2.6B: agentic RL trained inside real harnesses, running in 1.7GB on a phone
A 2.69B-parameter hybrid model pre-trained on ~34T tokens whose post-training ran agentic RL inside real harnesses (Hermes Agent, OpenClaw, Pi), so tool calling was learned where tool calling happens. It beats Qwen3.5-9B, three times its size, on ToolSandbox and instruction following, runs 220 tok/s on an M5 Max CPU and fits in ~1.7GB at Q4 on a phone. Liquid's own model card honestly scopes it away from agentic coding and knowledge-heavy work: this is for private, on-device agents.
2.69B parameters, 128K context77.83 ToolSandbox, above Qwen3.5-9B220 tok/s on Apple M5 Max CPU