Liquid AI releases LFM2.5-350M -> Agentic loops at 350M parameters

Reddit r/LocalLLaMA / 4/1/2026

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Key Points

Liquid AI released the LFM2.5-350M model, positioned for reliable data extraction and tool use with “agentic loops” behavior at ~350M parameters.
The model is designed to be usable in constrained environments, claiming under 500MB footprint when quantized and improved speed/memory efficiency versus larger baselines.
Liquid AI reports training on 28T tokens with scaled RL, and claims it outperforms models like Qwen3.5-0.8B on most benchmarks.
The release emphasizes cross-platform deployment (CPUs, GPUs, and mobile hardware) and reliability for function calling, agent workflows, and structured outputs.
The article points readers to a Liquid AI blog post and an available Hugging Face checkpoint for downloading and experimenting with the model.

Liquid AI releases LFM2.5-350M -> Agentic loops at 350M parameters

LFM2.5-350M by Liquid AI was trained for reliable data extraction and tool use.

At <500MB when quantized, it is built for environments where compute, memory, and latency are particularly constrained.

Trained on 28T tokens with scaled RL, it outperforms larger models like Qwen3.5-0.8B in most benchmarks; while being significantly faster and more memory efficient.