LoQT: Low-Rank Adapters for Quantized Pretraining
Fuente:
arXiv
Saved in:
| Main Authors: | Loeschcke, Sebastian, Toftrup, Mads, Kastoryano, Michael J., Belongie, Serge, Snæbjarnarson, Vésteinn |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Contrastive Decoding for Synthetic Data Generation in Low-Resource Language Modeling
by: Ulm, Jannek, et al.
Published: (2025)
by: Ulm, Jannek, et al.
Published: (2025)
Coarse-To-Fine Tensor Trains for Compact Visual Representations
by: Loeschcke, Sebastian, et al.
Published: (2024)
by: Loeschcke, Sebastian, et al.
Published: (2024)
Assessing Neural Network Robustness via Adversarial Pivotal Tuning
by: Christensen, Peter Ebert, et al.
Published: (2022)
by: Christensen, Peter Ebert, et al.
Published: (2022)
Gumbel Counterfactual Generation From Language Models
by: Ravfogel, Shauli, et al.
Published: (2024)
by: Ravfogel, Shauli, et al.
Published: (2024)
LoRA-Pro: Are Low-Rank Adapters Properly Optimized?
by: Wang, Zhengbo, et al.
Published: (2024)
by: Wang, Zhengbo, et al.
Published: (2024)
Unlocking the Global Synergies in Low-Rank Adapters
by: Zhang, Zixi, et al.
Published: (2024)
by: Zhang, Zixi, et al.
Published: (2024)
Ensembles of Low-Rank Expert Adapters
by: Li, Yinghao, et al.
Published: (2025)
by: Li, Yinghao, et al.
Published: (2025)
QDyLoRA: Quantized Dynamic Low-Rank Adaptation for Efficient Large Language Model Tuning
by: Rajabzadeh, Hossein, et al.
Published: (2024)
by: Rajabzadeh, Hossein, et al.
Published: (2024)
On the Proper Treatment of Units in Surprisal Theory
by: Kiegeland, Samuel, et al.
Published: (2026)
by: Kiegeland, Samuel, et al.
Published: (2026)
Low-Rank Adapters Meet Neural Architecture Search for LLM Compression
by: Muñoz, J. Pablo, et al.
Published: (2025)
by: Muñoz, J. Pablo, et al.
Published: (2025)
LQER: Low-Rank Quantization Error Reconstruction for LLMs
by: Zhang, Cheng, et al.
Published: (2024)
by: Zhang, Cheng, et al.
Published: (2024)
Multi-Modal Framing Analysis of News
by: Arora, Arnav, et al.
Published: (2025)
by: Arora, Arnav, et al.
Published: (2025)
Multiple Choice Learning of Low-Rank Adapters for Language Modeling
by: Letzelter, Victor, et al.
Published: (2025)
by: Letzelter, Victor, et al.
Published: (2025)
LoRA-GA: Low-Rank Adaptation with Gradient Approximation
by: Wang, Shaowen, et al.
Published: (2024)
by: Wang, Shaowen, et al.
Published: (2024)
LoLA: Low-Rank Linear Attention With Sparse Caching
by: McDermott, Luke, et al.
Published: (2025)
by: McDermott, Luke, et al.
Published: (2025)
Training-Free Bayesianization for Low-Rank Adapters of Large Language Models
by: Shi, Haizhou, et al.
Published: (2024)
by: Shi, Haizhou, et al.
Published: (2024)
zFLoRA: Zero-Latency Fused Low-Rank Adapters
by: Gowda, Dhananjaya, et al.
Published: (2025)
by: Gowda, Dhananjaya, et al.
Published: (2025)
AF Adapter: Continual Pretraining for Building Chinese Biomedical Language Model
by: Yan, Yongyu, et al.
Published: (2022)
by: Yan, Yongyu, et al.
Published: (2022)
LCQ: Low-Rank Codebook based Quantization for Large Language Models
by: Cai, Wen-Pu, et al.
Published: (2024)
by: Cai, Wen-Pu, et al.
Published: (2024)
Low-Rank Quantization-Aware Training for LLMs
by: Bondarenko, Yelysei, et al.
Published: (2024)
by: Bondarenko, Yelysei, et al.
Published: (2024)
Revealing Fine-Grained Values and Opinions in Large Language Models
by: Wright, Dustin, et al.
Published: (2024)
by: Wright, Dustin, et al.
Published: (2024)
LoLCATs: On Low-Rank Linearizing of Large Language Models
by: Zhang, Michael, et al.
Published: (2024)
by: Zhang, Michael, et al.
Published: (2024)
LoTR: Low Tensor Rank Weight Adaptation
by: Bershatsky, Daniel, et al.
Published: (2024)
by: Bershatsky, Daniel, et al.
Published: (2024)
LoRMA: Low-Rank Multiplicative Adaptation for LLMs
by: Bihany, Harsh, et al.
Published: (2025)
by: Bihany, Harsh, et al.
Published: (2025)
ResQ: Mixed-Precision Quantization of Large Language Models with Low-Rank Residuals
by: Saxena, Utkarsh, et al.
Published: (2024)
by: Saxena, Utkarsh, et al.
Published: (2024)
Training Acceleration of Low-Rank Decomposed Networks using Sequential Freezing and Rank Quantization
by: Hajimolahoseini, Habib, et al.
Published: (2023)
by: Hajimolahoseini, Habib, et al.
Published: (2023)
DropLoRA: Sparse Low-Rank Adaptation for Parameter-Efficient Fine-Tuning
by: Zhang, Haojie
Published: (2025)
by: Zhang, Haojie
Published: (2025)
HiP-LoRA: Budgeted Spectral Plasticity for Robust Low-Rank Adaptation
by: Chen, Lixian, et al.
Published: (2026)
by: Chen, Lixian, et al.
Published: (2026)
Finer Parameter Steps for Low-Rank PEFT: A Controlled Study with CP Tensor Adapters
by: Wang, Xinjue, et al.
Published: (2026)
by: Wang, Xinjue, et al.
Published: (2026)
Learning Adapter Rank via Symmetry Breaking
by: Doyle, Cooper, et al.
Published: (2025)
by: Doyle, Cooper, et al.
Published: (2025)
LoRA+: Efficient Low Rank Adaptation of Large Models
by: Hayou, Soufiane, et al.
Published: (2024)
by: Hayou, Soufiane, et al.
Published: (2024)
Queryable LoRA: Instruction-Regularized Routing Over Shared Low-Rank Update Atoms
by: Vaidya, Omatharv Bharat, et al.
Published: (2026)
by: Vaidya, Omatharv Bharat, et al.
Published: (2026)
LQ-LoRA: Low-rank Plus Quantized Matrix Decomposition for Efficient Language Model Finetuning
by: Guo, Han, et al.
Published: (2023)
by: Guo, Han, et al.
Published: (2023)
SwitchLoRA: Switched Low-Rank Adaptation Can Learn Full-Rank Information
by: Zhou, Kaiye, et al.
Published: (2024)
by: Zhou, Kaiye, et al.
Published: (2024)
MTL-LoRA: Low-Rank Adaptation for Multi-Task Learning
by: Yang, Yaming, et al.
Published: (2024)
by: Yang, Yaming, et al.
Published: (2024)
LoRe: Personalizing LLMs via Low-Rank Reward Modeling
by: Bose, Avinandan, et al.
Published: (2025)
by: Bose, Avinandan, et al.
Published: (2025)
AdaPreLoRA: Adafactor Preconditioned Low-Rank Adaptation
by: Liu, Ziyun, et al.
Published: (2026)
by: Liu, Ziyun, et al.
Published: (2026)
Basis Selection: Low-Rank Decomposition of Pretrained Large Language Models for Target Applications
by: Li, Yang, et al.
Published: (2024)
by: Li, Yang, et al.
Published: (2024)
AutoLoRA: Automatically Tuning Matrix Ranks in Low-Rank Adaptation Based on Meta Learning
by: Zhang, Ruiyi, et al.
Published: (2024)
by: Zhang, Ruiyi, et al.
Published: (2024)
LoX: Low-Rank Extrapolation Robustifies LLM Safety Against Fine-tuning
by: Perin, Gabriel J., et al.
Published: (2025)
by: Perin, Gabriel J., et al.
Published: (2025)
Similar Items
-
Contrastive Decoding for Synthetic Data Generation in Low-Resource Language Modeling
by: Ulm, Jannek, et al.
Published: (2025) -
Coarse-To-Fine Tensor Trains for Compact Visual Representations
by: Loeschcke, Sebastian, et al.
Published: (2024) -
Assessing Neural Network Robustness via Adversarial Pivotal Tuning
by: Christensen, Peter Ebert, et al.
Published: (2022) -
Gumbel Counterfactual Generation From Language Models
by: Ravfogel, Shauli, et al.
Published: (2024) -
LoRA-Pro: Are Low-Rank Adapters Properly Optimized?
by: Wang, Zhengbo, et al.
Published: (2024)