Hugging Face Trending Papers
ProQuant: Progressive Quantization-aware Training for Edge MLLMs
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arXiv Computation and Language
21h ago
ProQuant: Progressive Quantization-aware Training for Edge MLLMs
ProQuant introduces a progressive quantization‑aware training framework for low‑bit multi‑modal large language models (MLLMs), addressing the performance drop seen with post‑training quantization at low bit‑widths. The method includes a self‑relabeling scheme to align fine‑tuning data with pre‑training distributions and a progressive block activation mechanism that prioritizes multi‑modal understanding, guided by a vision‑sensitive factor to identify critical layers. Experiments on 2B–8B models show ProQuant outperforms existing techniques, achieving up to a 6.6% accuracy gain in W4A8 Qwen3‑VL‑2B‑Instruct with only ~1% drop from full precision.
arXiv Machine Learning
Aug 28
Activation Outliers Matter: Robust Recovery for Quantized Multimodal LLMs
The paper investigates low‑bit quantization for Multimodal Large Language Models (MLLMs), showing that MXFP8 retains near‑lossless performance while 4‑bit formats like MXFP4 and HiF4 cause significant degradation. It identifies activation quantization as the main source of this loss and introduces Residual Fallback Quantization (RFQ), a lightweight framework that adds a quantized residual pathway to improve activation fidelity without architectural changes. Experiments on Wan2.2 and Qwen3‑VL demonstrate that RFQ recovers much of the performance gap to BF16 baselines across generation and reasoning tasks.
Hugging Face Trending Papers
Jun 11
TWLA: Achieving Ternary Weights and Low-Bit Activations for LLMs via Post-Training Quantization
Large language models (LLMs) exhibit exceptional general language processing capabilities, but their memory and compute costs hinder deployment. Ternarization has emerged as a promising compression technique, offering significant reductions in model size and inference complexity.
arXiv AI
Jun 12
TWLA: Achieving Ternary Weights and Low-Bit Activations for LLMs via Post-Training Quantization
arXiv:2606. 13054v1 Announce Type: cross Abstract: Large language models (LLMs) exhibit exceptional general language processing capabilities, but their memory and compute costs hinder deployment.
arXiv Computation and Language
Sep 22
SalQ-VLM: Fine-Grained Saliency-Guided Quantization for Vision-Language Models
arXiv:2508.03351v3 Announce Type: replace-cross Abstract: Large language models (LLMs) have demonstrated remarkable capabilities across diverse language tasks, motivating their extension to vision-la...
arXiv Computer Vision
Sep 16
Efficient Quantization-Aware Distillation with Cross-Modal Alignment for Edge Vision-Language Models
arXiv:2609.16689v1 Announce Type: new Abstract: Large-scale vision-language models (VLM) such as CLIP enable strong open-vocabulary reasoning, yet deploying these capabilities on resource-constrained...