Liquid AI released LFM2.5 Q4_0 checkpoints trained with Quantization-Aware Distillation, recovering 97% of BF16 average accuracy lost to quantization. The four models—LFM2.5-230M, LFM2.5-350M, LFM2.5-1.2B-Instruct, and LFM2.5-2.6B—retain the low memory footprint and high throughput of native Q4_0 GGUFs. Benchmarked across reasoning, instruction-following, tool use, and agentic tasks, the QAD checkpoints match or exceed comparable post-training quantization quality at higher decode throughput on edge hardware.
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