Compact LLMs.for production AI.

Optimized LFM2.5-VL-3B: FFN Width Pruned, Distillation Aligned, INT4 Quantized

We surgically compress Liquid AI’s LFM2.5-VL-3B — an architecture-searched hybrid conv+attention vision-language model — by cutting FFN width where the search never optimized it: joint-SwiGLU Wanda pruning (10752→8192 and →7168), distillation-to-baseline LoRA recovery, and hand-rolled W4A16 GPTQ + W8 mixed-precision quantization. All three tiers are near-lossless against the base on our paired eval shards — PPL ratios at or below baseline (0.79–0.85), top-5 token agreement ≥ 0.95, quantization adding only +0.02–0.06 nats — across 5.30 GB (−15%), 4.92 GB (−21%), and 1.93 GB packed / 2.22 GB (Konic Optimized) — native INT4/INT8 compressed-tensors in vLLM (−69%). Live comparison on the smallest tier preserves scenes, facts, and tool calling — 10/10 tool rounds, including correct abstention where the base over-triggered. Recorded runs, not general benchmarks.