LowRA: Accurate and Efficient LoRA Fine-Tuning of LLMs under 2 Bits | Paper Citation Record

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This paper cites A General Language Assistant as a Laboratory for Alignment.

LowRA: Accurate and Efficient LoRA Fine-Tuning of LLMs under 2 Bits A General Language Assistant as a Laboratory for Alignment

Reference 1

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Observation 9abab17f-77bd-4d0e-a91c-861f6ccbcf46 · outbound

This paper cites BinaryBERT: Pushing the Limit of BERT Quantization.

LowRA: Accurate and Efficient LoRA Fine-Tuning of LLMs under 2 Bits BinaryBERT: Pushing the Limit of BERT Quantization

Reference 2

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This paper cites Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback.

LowRA: Accurate and Efficient LoRA Fine-Tuning of LLMs under 2 Bits Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback

Reference 3

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This paper cites Flexquant: Elastic quantization framework for locally hosted llm on edge devices, 2025.

LowRA: Accurate and Efficient LoRA Fine-Tuning of LLMs under 2 Bits Flexquant: Elastic quantization framework for locally hosted llm on edge devices, 2025

Reference 4

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Observation 23f88b5c-ff22-461d-a4c9-da189fa6a9f4 · outbound

This paper cites Punica: Multi-tenant lora serving.

LowRA: Accurate and Efficient LoRA Fine-Tuning of LLMs under 2 Bits Punica: Multi-tenant lora serving

Reference 5

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Observation c7c7cffb-e576-4097-960d-4348e178a6a3 · outbound

This paper cites Tesla P100 GPU Accelerator.

LowRA: Accurate and Efficient LoRA Fine-Tuning of LLMs under 2 Bits Tesla P100 GPU Accelerator

Reference 6

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Observation 73b673c1-a8e4-423a-af09-ff75019a2336 · outbound

This paper cites Tesla V100 GPU Accelerator Datasheet.

LowRA: Accurate and Efficient LoRA Fine-Tuning of LLMs under 2 Bits Tesla V100 GPU Accelerator Datasheet

Reference 7

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Observation 31bebd33-6b8b-4123-8c26-e9ad8542eeb3 · outbound

This paper cites NVIDIA T4 Virtualization Datasheet.

LowRA: Accurate and Efficient LoRA Fine-Tuning of LLMs under 2 Bits NVIDIA T4 Virtualization Datasheet

Reference 8

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Observation abdc270d-ead1-417c-9388-c76def8d3b4b · outbound

This paper cites Qlora: Efficient finetuning of quantized llms.

LowRA: Accurate and Efficient LoRA Fine-Tuning of LLMs under 2 Bits Qlora: Efficient finetuning of quantized llms

Reference 9

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Observation 7f50ccf5-f7dd-4b9e-b5e1-378bdbc3842a · outbound

This paper cites The Llama 3 Herd of Models.

LowRA: Accurate and Efficient LoRA Fine-Tuning of LLMs under 2 Bits The Llama 3 Herd of Models

Reference 10

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Observation a684b590-357e-4f2b-823d-84224357e7ff · outbound

This paper cites Learned Step Size Quantization.

LowRA: Accurate and Efficient LoRA Fine-Tuning of LLMs under 2 Bits Learned Step Size Quantization

Reference 11

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This paper cites Raspberry Pi 4 Model B.

LowRA: Accurate and Efficient LoRA Fine-Tuning of LLMs under 2 Bits Raspberry Pi 4 Model B

Reference 12

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Observation eb18376b-748c-4542-9b11-6e01f3117408 · outbound

This paper cites Teaching machines to read and comprehend.

LowRA: Accurate and Efficient LoRA Fine-Tuning of LLMs under 2 Bits Teaching machines to read and comprehend

Reference 13

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Observation af8e7f52-34a9-4568-a7bc-dd60bb8b3467 · outbound

This paper cites LoRA: Low-Rank Adaptation of Large Language Models.

LowRA: Accurate and Efficient LoRA Fine-Tuning of LLMs under 2 Bits LoRA: Low-Rank Adaptation of Large Language Models

Reference 14

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Observation 5098cfae-e73a-4342-93af-15739d74458a · outbound

This paper cites Mitigating Large Language Model Hallucination with Faithful Finetuning.

LowRA: Accurate and Efficient LoRA Fine-Tuning of LLMs under 2 Bits Mitigating Large Language Model Hallucination with Faithful Finetuning

Reference 15

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Observation 61b82f9b-76ce-42c4-a723-d65fc4e5b694 · outbound

This paper cites Accurate post training quantization with small calibration sets.

LowRA: Accurate and Efficient LoRA Fine-Tuning of LLMs under 2 Bits Accurate post training quantization with small calibration sets

Reference 16

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Observation f3a392f7-95dc-4c83-b71e-024c83b9fa6a · outbound

This paper cites L4Q: Parameter Efficient Quantization-Aware Fine-Tuning on Large Language Models.

LowRA: Accurate and Efficient LoRA Fine-Tuning of LLMs under 2 Bits L4Q: Parameter Efficient Quantization-Aware Fine-Tuning on Large Language Models

Reference 17

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Observation 79c5014d-cf73-4840-831c-58aed8a892a3 · outbound

This paper cites The singular value decomposition: Its computation and some applications.

LowRA: Accurate and Efficient LoRA Fine-Tuning of LLMs under 2 Bits The singular value decomposition: Its computation and some applications

Reference 18

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Observation a5629304-6a57-46b4-b57d-bb4ac4e0f5b3 · outbound

This paper cites Openassistant conversations-democratizing large language model alignment.

LowRA: Accurate and Efficient LoRA Fine-Tuning of LLMs under 2 Bits Openassistant conversations-democratizing large language model alignment

Reference 19

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Observation 6690ef9f-0803-4b8c-b84d-325a3b84a39b · outbound

This paper cites BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension.

LowRA: Accurate and Efficient LoRA Fine-Tuning of LLMs under 2 Bits BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension

Reference 20

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Observation 93bde81c-815e-46cb-8ca3-7af5041ab17c · outbound

This paper cites BART-Large Model Card.

LowRA: Accurate and Efficient LoRA Fine-Tuning of LLMs under 2 Bits BART-Large Model Card

Reference 21

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Observation 0a836f6d-54f9-4d5c-a003-5edab1b8c0a3 · outbound

This paper cites Svdqunat: Absorbing outliers by low-rank components for 4-bit diffusion models.

LowRA: Accurate and Efficient LoRA Fine-Tuning of LLMs under 2 Bits Svdqunat: Absorbing outliers by low-rank components for 4-bit diffusion models

Reference 22

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Observation 618795ae-8671-44ed-974f-c1d124e659d9 · outbound

This paper cites LoftQ: LoRA-Fine-Tuning-Aware Quantization for Large Language Models.

LowRA: Accurate and Efficient LoRA Fine-Tuning of LLMs under 2 Bits LoftQ: LoRA-Fine-Tuning-Aware Quantization for Large Language Models

Reference 23

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This paper cites ApiQ: Finetuning of 2-Bit Quantized Large Language Model.

LowRA: Accurate and Efficient LoRA Fine-Tuning of LLMs under 2 Bits ApiQ: Finetuning of 2-Bit Quantized Large Language Model

Reference 24

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Observation 2786401a-3b35-43b9-adb6-26099075eb8a · outbound

This paper cites DeepSeek-V3 Technical Report.

LowRA: Accurate and Efficient LoRA Fine-Tuning of LLMs under 2 Bits DeepSeek-V3 Technical Report

Reference 25

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This paper cites Mitigating hallucination in large multi-modal models via robust instruction tuning.

LowRA: Accurate and Efficient LoRA Fine-Tuning of LLMs under 2 Bits Mitigating hallucination in large multi-modal models via robust instruction tuning

Reference 26

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Observation b1b19805-8c90-476c-8ba4-79d95a1446d6 · outbound

This paper cites Few-shot parameter-efficient fine-tuning is better and cheaper than in-context learning.

LowRA: Accurate and Efficient LoRA Fine-Tuning of LLMs under 2 Bits Few-shot parameter-efficient fine-tuning is better and cheaper than in-context learning

Reference 27

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This paper cites Nonuniform- to-uniform quantization: Towards accurate quantization via generalized straight-through estimation.

LowRA: Accurate and Efficient LoRA Fine-Tuning of LLMs under 2 Bits Nonuniform- to-uniform quantization: Towards accurate quantization via generalized straight-through estimation

Reference 28

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Observation 27b7448e-6f32-43d3-b61e-8737223ede63 · outbound

This paper cites Least squares quantization in pcm.

LowRA: Accurate and Efficient LoRA Fine-Tuning of LLMs under 2 Bits Least squares quantization in pcm

Reference 29

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Observation 4d2f4fbf-10c6-48e1-9e7d-02ed8692ae76 · outbound

This paper cites UniPELT: A Unified Framework for Parameter-Efficient Language Model Tuning.

LowRA: Accurate and Efficient LoRA Fine-Tuning of LLMs under 2 Bits UniPELT: A Unified Framework for Parameter-Efficient Language Model Tuning

Reference 30

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LowRA: Accurate and Efficient LoRA Fine-Tuning of LLMs under 2 Bits Quantizing for minimum distortion

Reference 31

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Observation ce1a28f6-cccc-48b0-a779-5da4c5c6e6f2 · outbound

This paper cites PiSSA: Principal Singular Values and Singular Vectors Adaptation of Large Language Models.

LowRA: Accurate and Efficient LoRA Fine-Tuning of LLMs under 2 Bits PiSSA: Principal Singular Values and Singular Vectors Adaptation of Large Language Models

Reference 32

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LowRA: Accurate and Efficient LoRA Fine-Tuning of LLMs under 2 Bits Pointer Sentinel Mixture Models

Reference 33

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LowRA: Accurate and Efficient LoRA Fine-Tuning of LLMs under 2 Bits Pulp: a linear programming toolkit for python

Reference 34

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This paper cites Don't Give Me the Details, Just the Summary! Topic-Aware Convolutional Neural Networks for Extreme Summarization.

LowRA: Accurate and Efficient LoRA Fine-Tuning of LLMs under 2 Bits Don't Give Me the Details, Just the Summary! Topic-Aware Convolutional Neural Networks for Extreme Summarization

Reference 35

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This paper cites Towards Modular LLMs by Building and Reusing a Library of LoRAs.

LowRA: Accurate and Efficient LoRA Fine-Tuning of LLMs under 2 Bits Towards Modular LLMs by Building and Reusing a Library of LoRAs

Reference 36

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Observation 4b5db568-8ec7-4a57-89e1-3398c235b2fd · outbound

This paper cites Accurate LoRA-Finetuning Quantization of LLMs via Information Retention.

LowRA: Accurate and Efficient LoRA Fine-Tuning of LLMs under 2 Bits Accurate LoRA-Finetuning Quantization of LLMs via Information Retention

Reference 37

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LowRA: Accurate and Efficient LoRA Fine-Tuning of LLMs under 2 Bits Coin-or: an open-source library for optimization

Reference 38

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Observation fbb1305e-759d-432b-b8d3-1374f76f65c9 · outbound

This paper cites Not all bits have equal value: Heterogeneous precisions via trainable noise.

LowRA: Accurate and Efficient LoRA Fine-Tuning of LLMs under 2 Bits Not all bits have equal value: Heterogeneous precisions via trainable noise

Reference 39

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This paper cites Q-bert: Hessian based ultra low precision quantization of bert.

LowRA: Accurate and Efficient LoRA Fine-Tuning of LLMs under 2 Bits Q-bert: Hessian based ultra low precision quantization of bert

Reference 40

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This paper cites Agile-quant: Activation-guided quantization for faster inference of llms on the edge, 2023.

LowRA: Accurate and Efficient LoRA Fine-Tuning of LLMs under 2 Bits Agile-quant: Activation-guided quantization for faster inference of llms on the edge, 2023

Reference 41

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Observation e8aa1cdc-a75e-471f-8a90-ec11923f4885 · outbound

This paper cites Slora: Scalable serving of thousands of lora adapters.

LowRA: Accurate and Efficient LoRA Fine-Tuning of LLMs under 2 Bits Slora: Scalable serving of thousands of lora adapters

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T10:26:46.000479Z

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This paper cites Mobilequant: Mobile-friendly quantization for on-device language models, 2024.

LowRA: Accurate and Efficient LoRA Fine-Tuning of LLMs under 2 Bits Mobilequant: Mobile-friendly quantization for on-device language models, 2024

Reference 43

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Observation 05c142c6-d0a2-4f45-9832-01906fe54a72 · outbound

This paper cites LLaMA: Open and Efficient Foundation Language Models.

LowRA: Accurate and Efficient LoRA Fine-Tuning of LLMs under 2 Bits LLaMA: Open and Efficient Foundation Language Models

Reference 44

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This paper cites Llama 2: Open Foundation and Fine-Tuned Chat Models.

LowRA: Accurate and Efficient LoRA Fine-Tuning of LLMs under 2 Bits Llama 2: Open Foundation and Fine-Tuned Chat Models

Reference 45

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This paper cites LoRA-GA: Low-Rank Adaptation with Gradient Approximation.

LowRA: Accurate and Efficient LoRA Fine-Tuning of LLMs under 2 Bits LoRA-GA: Low-Rank Adaptation with Gradient Approximation

Reference 46

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This paper cites Bitstack: Any-size compression of large language models in variable memory environments, 2025.

LowRA: Accurate and Efficient LoRA Fine-Tuning of LLMs under 2 Bits Bitstack: Any-size compression of large language models in variable memory environments, 2025

Reference 47

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Observation 0c42d943-5018-4403-a3db-a25a1705382b · outbound

This paper cites Super-NaturalInstructions: Generalization via Declarative Instructions on 1600+ NLP Tasks.

LowRA: Accurate and Efficient LoRA Fine-Tuning of LLMs under 2 Bits Super-NaturalInstructions: Generalization via Declarative Instructions on 1600+ NLP Tasks

Reference 48

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This paper cites Attention is all you need.

LowRA: Accurate and Efficient LoRA Fine-Tuning of LLMs under 2 Bits Attention is all you need

Reference 49

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This paper cites Finetuned Language Models Are Zero-Shot Learners.

LowRA: Accurate and Efficient LoRA Fine-Tuning of LLMs under 2 Bits Finetuned Language Models Are Zero-Shot Learners

Reference 50

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This paper cites BSQ: Exploring Bit-Level Sparsity for Mixed-Precision Neural Network Quantization.

LowRA: Accurate and Efficient LoRA Fine-Tuning of LLMs under 2 Bits BSQ: Exploring Bit-Level Sparsity for Mixed-Precision Neural Network Quantization

Reference 51

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This paper cites Bitfit: Simple parameter-efficient fine-tuning for transformer-based masked language-models.

LowRA: Accurate and Efficient LoRA Fine-Tuning of LLMs under 2 Bits Bitfit: Simple parameter-efficient fine-tuning for transformer-based masked language-models

Reference 52

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Observation 0e1f3ca7-b3f7-4062-8cc8-595ec780edcd · outbound

This paper cites Atom: Low-bit quantization for efficient and accurate llm serving.

LowRA: Accurate and Efficient LoRA Fine-Tuning of LLMs under 2 Bits Atom: Low-bit quantization for efficient and accurate llm serving

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T10:26:45.975534Z

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Observation b98012bd-752a-4427-a768-9d682f6ab1f7 · outbound

This paper cites Sysmol: A hardware-software co-design framework for ultra-low and fine-grained mixed-precision neural networks.

LowRA: Accurate and Efficient LoRA Fine-Tuning of LLMs under 2 Bits Sysmol: A hardware-software co-design framework for ultra-low and fine-grained mixed-precision neural networks

Reference 54

Resolution: unresolved; no resolver link, observed 2026-08-08T10:26:45.535838Z

Source-reported events for the cited work

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Observation 6651fd52-025c-4696-ba56-b122cca831a2 · outbound

This paper cites Fine-Tuning Language Models from Human Preferences.

LowRA: Accurate and Efficient LoRA Fine-Tuning of LLMs under 2 Bits Fine-Tuning Language Models from Human Preferences

Reference 55

Resolution: malformed identifier; no resolver link, observed 2026-08-08T10:26:45.538339Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3a5b2d99-c993-42a2-804d-18fa8ee9ce54 · outbound

This paper cites an unresolved cited work.

LowRA: Accurate and Efficient LoRA Fine-Tuning of LLMs under 2 Bits Unresolved cited work

Reference 57

Resolution: unresolved; raw_fallback, observed 2026-08-08T10:26:45.968656Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 28a5915a-6acb-436e-af3d-57e230934208 · outbound

This paper cites initializes.

LowRA: Accurate and Efficient LoRA Fine-Tuning of LLMs under 2 Bits initializes

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T10:26:45.960914Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 59aad7f4-5823-4c6b-9a56-337fb51d88cd · outbound

This paper cites an unresolved cited work.

LowRA: Accurate and Efficient LoRA Fine-Tuning of LLMs under 2 Bits Unresolved cited work

Reference 59

Resolution: unresolved; raw_fallback, observed 2026-08-08T10:26:45.953592Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 500d6b44-9e82-418b-9257-e1a42eaa1fc5 · outbound

This paper cites (M-step) These steps are repeated until convergence or until a stopping criterion (e.g., a maximum number of iterations) is met.

LowRA: Accurate and Efficient LoRA Fine-Tuning of LLMs under 2 Bits (M-step) These steps are repeated until convergence or until a stopping criterion (e.g., a maximum number of iterations) is met

Reference 60

Resolution: malformed identifier; raw_fallback, observed 2026-08-08T10:26:45.945979Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 2eead5f4-9ade-45bb-a442-c6085a03ee64 · outbound

This paper cites an unresolved cited work.

LowRA: Accurate and Efficient LoRA Fine-Tuning of LLMs under 2 Bits Unresolved cited work

Reference 2017

Resolution: parse uncertain; raw_fallback, observed 2026-08-08T10:26:46.128752Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

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