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Date	Provider	Score	Summary
19 Aug 2026, 9:48 PM	Hugging Face Blog	6.5	LFM2.5 Q4\_0 Checkpoints from Quantization-Aware Distillation LiquidAI released QAD (Quantization-Aware Distillation) Q4_0 GGUF checkpoints for four LFM2.5 models (230M, 350M, 1.2B-Instruct, 2.6B), recovering ~97% of the BF16 accuracy typically lost to 4-bit quantization while maintaining Q4_0 memory footprint and throughput. Benchmarks across GPQA Diamond, MMLU-Pro, IFEval, BFCLv4, and others show the QAD checkpoints match or exceed Q5_K_M and Q4_K_M quality at 3-33% higher decode throughput, tested on MacBook Pro, NucBox EVO-X2, Samsung Galaxy S26 Ultra, and Raspberry Pi 5. Why: If you're running LFM2.5 models on edge hardware via llama.cpp, swap your existing PTQ Q4_0 or Q4_K_M GGUFs for these QAD Q4_0 files to get measurably better reasoning, instruction-following, and tool-use accuracy at the same memory and speed — the 1.2B and 2.6B checkpoints are the most relevant for agentic workloads. quantization edge-deployment llama-cpp gguf small-models

Date

Provider

Score

Summary

19 Aug 2026, 9:48 PM

Hugging Face Blog

6.5

LFM2.5 Q4\_0 Checkpoints from Quantization-Aware Distillation

LiquidAI released QAD (Quantization-Aware Distillation) Q4_0 GGUF checkpoints for four LFM2.5 models (230M, 350M, 1.2B-Instruct, 2.6B), recovering ~97% of the BF16 accuracy typically lost to 4-bit quantization while maintaining Q4_0 memory footprint and throughput. Benchmarks across GPQA Diamond, MMLU-Pro, IFEval, BFCLv4, and others show the QAD checkpoints match or exceed Q5_K_M and Q4_K_M quality at 3-33% higher decode throughput, tested on MacBook Pro, NucBox EVO-X2, Samsung Galaxy S26 Ultra, and Raspberry Pi 5.

Why: If you're running LFM2.5 models on edge hardware via llama.cpp, swap your existing PTQ Q4_0 or Q4_K_M GGUFs for these QAD Q4_0 files to get measurably better reasoning, instruction-following, and tool-use accuracy at the same memory and speed — the 1.2B and 2.6B checkpoints are the most relevant for agentic workloads.