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arXiv AI By Mingkang Liu, Peipei Jiao, Weiming Meng, Tianyuan Liu, Lichen Pang, Junyan Li

UniCompress: From Multi-LoRA Experts to One Compact Model for Edge Intelligence

Read the original on arXiv AI →

UniCompress is a deployment-oriented framework that transforms multi-task vision‑language expertise into a single compact, adapter‑free model suitable for edge intelligence. Starting from a BF16 Qwen3‑VL‑4B teacher, it builds an asymmetric INT2 student using GPTQ initialization and teacher‑guided quantization recovery, jointly compressing both the vision encoder and language backbone. Evaluated on four embodied tasks, UniCompress cuts weight storage from 8.9 GB to 2.1 GB (≈76% reduction) while improving the INT2 macro task score by 34.3 percentage points over GPTQ‑INT2 and staying within 0.9 points of the BF16 teacher.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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