efficient-deployment

ExTernD introduces an expanded-rank ternary decomposition for post-training LLM quantization, enabling accuracy approaching bf16 by using a factored representation with free inner rank. It matches Q4_K accuracy at 5.2-5.5 effective bits per weight on models like Gemma-4 and Qwen3.5.

The paper proposes Light-MER, a lightweight multimodal emotion recognition framework that uses knowledge distillation from an 8B teacher model to a sub-1B student, achieving state-of-the-art performance with significantly higher inference efficiency, challenging the necessity of models larger than 1B parameters.

This paper introduces Maestro, a structured pruning framework for Mixture-of-Experts language models that uses Markov chains to model expert activation trajectories, achieving globally aware pruning and outperforming baselines by up to 10.61% under 50% compression.

FlexMoE proposes a one-for-all nested intra-expert pruning method for MoE language models, enabling multiple deployable subnetworks from a single training run with minimal performance loss.

CAT-Q introduces a post-training ternary quantization method for LLMs that uses learnable modulation and softened ternarization, achieving superior performance over BitNet 1.58-bit while using only 512 calibration samples and scaling to 235B parameters.

TENP proposes a structured pruning framework for Mixture-of-Experts LLMs that retains important experts and applies neuron pruning to less important ones, achieving high sparsity with minimal accuracy loss on Qwen and DeepSeek models.

A novel end-to-end framework for LLM compression that jointly optimizes structural pruning and mixed-precision quantization, achieving significant perplexity reductions and speedups over state-of-the-art methods, especially at ultra-low bit precisions.

BitsMoE introduces a spectral-energy-guided bit allocation framework for quantizing Mixture-of-Experts LLMs, achieving substantial accuracy improvements and speedups under ultra-low-bit quantization.

OpenBMB released MiniCPM-V 4.6, a 1.3B parameter multimodal model. Using high-resolution visual processing and efficient compression, it achieves fast inference on consumer hardware and mobile phones, outperforming larger models. It is fully open-source and supports multiple inference and quantization frameworks.