Liquid AI releases QAD 4-bit LFM2.5 checkpoints for edge deployment
Original titleLFM2.5 Q4_0: Quantization-Aware Distillation for Edge Deployment
Liquid AI released 4-bit Q4_0 GGUF checkpoints for LFM2.5-230M, LFM2.5-350M, LFM2.5-1.2B-Instruct, and LFM2.5-2.6B, trained with Quantization-Aware Distillation.
The company says the checkpoints recover most accuracy lost to quantization, reaching roughly 97% of their BF16 averages while keeping Q4_0 memory footprint and throughput.
Benchmarks compare them against post-training quantized Q4_0 GGUFs and against Q5_K_M, Q4_K_M, and Unsloth's UD-Q4_K_XL.
The post shows how quantization-aware distillation recovers accuracy lost in Q4_0 checkpoints, with throughput measured across four hardware backends for deployment tradeoffs.
Source: Liquid AI Blog · liquid.aiPublished · added here