Liquid AI releases LFM2.5-350M model for agentic loops

Liquid AI's new small model for agent workflows

Liquid AI released LFM2.5-350M, a 350M parameter model specifically trained for agentic loops. This model focuses on reliable data extraction and tool use, making it suitable for environments where compute, memory, and latency are constrained.

Technical specifications

• Size: Under 500MB when quantized
• Training: Trained on 28 trillion tokens with scaled reinforcement learning
• Performance: Outperforms larger models like Qwen3.5-0.8B in most benchmarks
• Efficiency: Significantly faster and more memory efficient than comparable models

Key features

• Runs across CPUs, GPUs, and mobile hardware
• Fast, efficient, and low-latency operation
• Reliable function calling and agent workflows
• Consistent structured outputs

Availability

The model checkpoint is available on Hugging Face at LiquidAI/LFM2.5-350M. This makes it accessible for immediate testing and integration into existing workflows.

For developers working with AI coding agents in resource-constrained environments, this model offers a balance between capability and efficiency. The small size combined with strong performance on structured outputs makes it practical for edge deployment and mobile applications.