SalQ-VLM: Fine-Grained Saliency-Guided Quantization for Vision-Language Models
cs.CV, cs.AI, cs.CL
Submitted: 2025-08-05
Updated: 2026-09-21
Code: https://github.com/turboderp-org/exllamav2
Terminology
Sources
- QuaRot: Outlier-Free 4-Bit Inference in Rotated LLMs
- Qwen2.5-VL Technical Report
- Sparks of Artificial General Intelligence: Early experiments with GPT-4
- DB-LLM: Accurate Dual-Binarization for Efficient LLMs
- Expanding Performance Boundaries of Open-Source Multimodal Models with Model, Data, and Test-Time Scaling
- DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning
- CBQ: Cross-Block Quantization for Large Language Models
- GPTQ: Accurate Post-Training Quantization for Generative Pre-trained Transformers
- MARLIN: Mixed-Precision Auto-Regressive Parallel Inference on Large Language Models
- LLMC: Benchmarking Large Language Model Quantization with a Versatile Compression Toolkit
- OstQuant: Refining Large Language Model Quantization with Orthogonal and Scaling Transformations for Better Distribution Fitting
- QVGen: Pushing the Limit of Quantized Video Generative Models
- HarmoniCa: Harmonizing Training and Inference for Better Feature Caching in Diffusion Transformer Acceleration
- Mistral 7B
- BoA: Attention-aware Post-training Quantization without Backpropagation
- Critic-Guided Decoding for Controlled Text Generation
- LLaVA-OneVision: Easy Visual Task Transfer
- SEED-Bench-2-Plus: Benchmarking Multimodal Large Language Models with Text-Rich Visual Comprehension
- BRECQ: Pushing the Limit of Post-Training Quantization by Block Reconstruction
- GPTAQ: Efficient Finetuning-Free Quantization for Asymmetric Calibration
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