CrossVLA introduces a surrogate log-probability estimator to enable DPO on flow-matching VLAs, reports DoRA yielding +10.4 pp mean gains over SFT on LIBERO with 600 trials, and shows inference caching limited to 21% speedup with some strategies harming success rates.
Qa-lora: Quantization-aware low-rank adaptation of large language models.arXiv preprint arXiv:2309.14717
7 Pith papers cite this work. Polarity classification is still indexing.
7
Pith papers citing it
citation-role summary
background 2
method 1
citation-polarity summary
representative citing papers
Quant.npu provides a fully static quantization pipeline for on-device LLMs on NPUs by combining rotation matrices, bit-width-aware initialization, two-stage selective optimization, and adaptive mixed precision.
BPDQ creates variable quantization grids from bit-planes and scalar coefficients, refined iteratively with second-order data to minimize output error, enabling 2-bit serving of Qwen2.5-72B on one RTX 3090 at 83.85% GSM8K accuracy.
A framework combines multi-LoRA runtime switching, multi-stream stylistic decoding, and Dynamic Self-Speculative Decoding with INT4 quantization to achieve 4-6x memory and latency gains for on-device inference of a one-for-all foundational LLM on Qualcomm chipsets.
A comprehensive survey of PEFT algorithms for large models, covering their performance, overhead, applications, and real-world system implementations.
ECG foundation models for signal interpretation and medical LLMs for reasoning can be integrated into agentic systems for real-time cardiovascular intelligence on edge devices.
The paper surveys techniques to speed up and reduce the resource needs of LLM inference, organized by data-level, model-level, and system-level changes, with comparative experiments on representative methods.
citing papers explorer
-
CrossVLA: Cross-Paradigm Post-Training and Inference Optimization for Vision-Language-Action Models
CrossVLA introduces a surrogate log-probability estimator to enable DPO on flow-matching VLAs, reports DoRA yielding +10.4 pp mean gains over SFT on LIBERO with 600 trials, and shows inference caching limited to 21% speedup with some strategies harming success rates.
-
Quant.npu: Enabling Efficient Mobile NPU Inference for on-device LLMs via Fully Static Quantization
Quant.npu provides a fully static quantization pipeline for on-device LLMs on NPUs by combining rotation matrices, bit-width-aware initialization, two-stage selective optimization, and adaptive mixed precision.
-
BPDQ: Bit-Plane Decomposition Quantization on a Variable Grid for Large Language Models
BPDQ creates variable quantization grids from bit-planes and scalar coefficients, refined iteratively with second-order data to minimize output error, enabling 2-bit serving of Qwen2.5-72B on one RTX 3090 at 83.85% GSM8K accuracy.
-
Unlocking the Edge deployment and ondevice acceleration of multi-LoRA enabled one-for-all foundational LLM
A framework combines multi-LoRA runtime switching, multi-stream stylistic decoding, and Dynamic Self-Speculative Decoding with INT4 quantization to achieve 4-6x memory and latency gains for on-device inference of a one-for-all foundational LLM on Qualcomm chipsets.
-
Parameter-Efficient Fine-Tuning for Large Models: A Comprehensive Survey
A comprehensive survey of PEFT algorithms for large models, covering their performance, overhead, applications, and real-world system implementations.
-
ECG Foundation Models and Medical LLMs for Agentic Cardiovascular Intelligence at the Edge: A Review and Outlook
ECG foundation models for signal interpretation and medical LLMs for reasoning can be integrated into agentic systems for real-time cardiovascular intelligence on edge devices.
-
A Survey on Efficient Inference for Large Language Models
The paper surveys techniques to speed up and reduce the resource needs of LLM inference, organized by data-level, model-level, and system-level changes, with comparative experiments on representative methods.