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Collections including paper arxiv:2402.17764

BitDelta: Your Fine-Tune May Only Be Worth One Bit

Paper • 2402.10193 • Published Feb 15 • 17
OneBit: Towards Extremely Low-bit Large Language Models

Paper • 2402.11295 • Published Feb 17 • 21
BiLLM: Pushing the Limit of Post-Training Quantization for LLMs

Paper • 2402.04291 • Published Feb 6 • 48
GPTVQ: The Blessing of Dimensionality for LLM Quantization

Paper • 2402.15319 • Published Feb 23 • 19

Large Language Model Alignment: A Survey

Paper • 2309.15025 • Published Sep 26, 2023 • 2
Aligning Large Language Models with Human: A Survey

Paper • 2307.12966 • Published Jul 24, 2023 • 1
Direct Preference Optimization: Your Language Model is Secretly a Reward Model

Paper • 2305.18290 • Published May 29, 2023 • 38
SteerLM: Attribute Conditioned SFT as an (User-Steerable) Alternative to RLHF

Paper • 2310.05344 • Published Oct 9, 2023 • 1

Distributed Inference and Fine-tuning of Large Language Models Over The Internet

Paper • 2312.08361 • Published Dec 13, 2023 • 23
S-LoRA: Serving Thousands of Concurrent LoRA Adapters

Paper • 2311.03285 • Published Nov 6, 2023 • 27
Efficient Memory Management for Large Language Model Serving with PagedAttention

Paper • 2309.06180 • Published Sep 12, 2023 • 25
The Era of 1-bit LLMs: All Large Language Models are in 1.58 Bits

Paper • 2402.17764 • Published Feb 27 • 571

DoRA: Weight-Decomposed Low-Rank Adaptation

Paper • 2402.09353 • Published Feb 14 • 23
MobileLLM: Optimizing Sub-billion Parameter Language Models for On-Device Use Cases

Paper • 2402.14905 • Published Feb 22 • 81
Resonance RoPE: Improving Context Length Generalization of Large Language Models

Paper • 2403.00071 • Published Feb 29 • 19
AtP*: An efficient and scalable method for localizing LLM behaviour to components

Paper • 2403.00745 • Published Mar 1 • 8

OpenMoE: An Early Effort on Open Mixture-of-Experts Language Models

Paper • 2402.01739 • Published Jan 29 • 26
Rethinking Interpretability in the Era of Large Language Models

Paper • 2402.01761 • Published Jan 30 • 19
Self-Discover: Large Language Models Self-Compose Reasoning Structures

Paper • 2402.03620 • Published Feb 6 • 104
Aya Model: An Instruction Finetuned Open-Access Multilingual Language Model

Paper • 2402.07827 • Published Feb 12 • 43

VeRA: Vector-based Random Matrix Adaptation

Paper • 2310.11454 • Published Oct 17, 2023 • 27
The Era of 1-bit LLMs: All Large Language Models are in 1.58 Bits

Paper • 2402.17764 • Published Feb 27 • 571

Tag-LLM: Repurposing General-Purpose LLMs for Specialized Domains

Paper • 2402.05140 • Published Feb 6 • 18
BitDelta: Your Fine-Tune May Only Be Worth One Bit

Paper • 2402.10193 • Published Feb 15 • 17
QLoRA: Efficient Finetuning of Quantized LLMs

Paper • 2305.14314 • Published May 23, 2023 • 41
OpenCodeInterpreter: Integrating Code Generation with Execution and Refinement

Paper • 2402.14658 • Published Feb 22 • 78

Offline Actor-Critic Reinforcement Learning Scales to Large Models

Paper • 2402.05546 • Published Feb 8 • 4
The Era of 1-bit LLMs: All Large Language Models are in 1.58 Bits

Paper • 2402.17764 • Published Feb 27 • 571

BiLLM: Pushing the Limit of Post-Training Quantization for LLMs

Paper • 2402.04291 • Published Feb 6 • 48
The Era of 1-bit LLMs: All Large Language Models are in 1.58 Bits

Paper • 2402.17764 • Published Feb 27 • 571

Simple linear attention language models balance the recall-throughput tradeoff

Paper • 2402.18668 • Published Feb 28 • 18
Linear Transformers with Learnable Kernel Functions are Better In-Context Models

Paper • 2402.10644 • Published Feb 16 • 74
Repeat After Me: Transformers are Better than State Space Models at Copying

Paper • 2402.01032 • Published Feb 1 • 22
Zoology: Measuring and Improving Recall in Efficient Language Models

Paper • 2312.04927 • Published Dec 8, 2023 • 2

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