LFM2.5 Q4_0 Checkpoints from Quantization-Aware Distillation

Liquid AI has released Quantization-Aware Distillation (QAD) Q4_0 GGUF checkpoints for its LFM2.5 230M, 350M, 1.2B-Instruct, and 2.6B models, aiming to preserve model quality while retaining the low memory use and speed of standard 4-bit quantization. QAD distills a high-precision teacher into a quantized student, recovering an average of 97% of the BF16 accuracy normally lost through quantization across benchmarks covering reasoning, instruction following, tool use, agentic tasks, and math. Tests on a MacBook Pro, NucBox EVO-X2, Samsung Galaxy S26 Ultra, and Raspberry Pi 5 found that the smaller QAD models matched Q5_K_M quality with 4–33% greater decoding throughput, while the larger models matched Q4_K_M quality with 3–14% higher throughput. The checkpoints are available on Hugging Face and can be run through llama.cpp or other runtimes supporting GGUF Q4_0 files.

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