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

Today, we release QAD Q4_0 GGUFs. These are updated 4-bit checkpoints for LFM2.5-230M, LFM2.5-350M, LFM2.5-1.2B-Instruct, and LFM2.5-2.6B. They allow developers to run LFM2.5 models at Q4_0 memory and speed without the usual quality drop:

For all four models, we compare their released GGUFs produced with post-training quantization (PTQ) against the trained QAD Q4_0 checkpoints on a benchmark suite spanning reasoning, instruction-following, tool use, and agentic capabilities: GPQA Diamond, MMLU-Pro, IFEval, IFBench, Multi-IF, and BFCLv4. The BF16 GGUF serves as the in-format ceiling. We also add one scale-appropriate math evaluation: GSM8K for LFM2.5-230M and LFM2.5-350M, and AIME25 for LFM2.5-1.2B-Instruct and LFM2.5-2.6B. We report the mean across five repeats.

Across all four models, QAD substantially improves the Q4_0 checkpoint. The QAD checkpoints retain 97.1%, 96.5%, 97.4%, and 96.6% of their respective BF16 baseline performance.

We measure decode throughput for the four models LFM2.5-230M, LFM2.5-350M, LFM2.5-1.2B-Instruct, and LFM2.5-2.6B across four targets: MacBook Pro, NucBox EVO-X2, Samsung Galaxy S26 Ultra, and Raspberry Pi 5. MacBook Pro and NucBox use GPU inference, while Samsung and Raspberry Pi use Arm CPU inference. BF16 and F16 are shown as full-precision references where profiled.

The 230M and 350M QAD Q4_0 checkpoints match Q5_K_M quality within evaluation variance at a 4-33% higher decode throughput. The 1.2B and 2.6B QAD Q4_0 checkpoints match Q4_K_M quality at a 3-14% higher throughput. The QAD Q4_0 checkpoints also match Unsloth's UD-Q4_K_XL (where applicable, for the 230M and 1.2B), a strong external post-training quantization checkpoint.

Use the files with llama.cpp or any runtime that supports GGUF Q4_0 artifacts.

The QAD GGUFs are available on Hugging Face today: LFM2.5-230M, LFM2.5-350M, LFM2.5-1.2B-Instruct, and LFM2.5-2.6B.

We can't wait to see what you build.