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Collections including paper arxiv:2403.17297

LLMLingua-2: Data Distillation for Efficient and Faithful Task-Agnostic Prompt Compression

Paper • 2403.12968 • Published Mar 19 • 24
PERL: Parameter Efficient Reinforcement Learning from Human Feedback

Paper • 2403.10704 • Published Mar 15 • 56
Alignment Studio: Aligning Large Language Models to Particular Contextual Regulations

Paper • 2403.09704 • Published Mar 8 • 31
RAFT: Adapting Language Model to Domain Specific RAG

Paper • 2403.10131 • Published Mar 15 • 66

InternLM2 Technical Report

Paper • 2403.17297 • Published Mar 26 • 28
Prometheus 2: An Open Source Language Model Specialized in Evaluating Other Language Models

Paper • 2405.01535 • Published May 2 • 114

Papers - Fine-tuning - Report

InternLM2 Technical Report

Paper • 2403.17297 • Published Mar 26 • 28
Scaling Down to Scale Up: A Guide to Parameter-Efficient Fine-Tuning

Paper • 2303.15647 • Published Mar 28, 2023 • 4
LoRA Land: 310 Fine-tuned LLMs that Rival GPT-4, A Technical Report

Paper • 2405.00732 • Published Apr 29 • 118

Papers - Fine-tuning - SFT

InternLM2 Technical Report

Paper • 2403.17297 • Published Mar 26 • 28
sDPO: Don't Use Your Data All at Once

Paper • 2403.19270 • Published Mar 28 • 38
Learn Your Reference Model for Real Good Alignment

Paper • 2404.09656 • Published Apr 15 • 82
OpenBezoar: Small, Cost-Effective and Open Models Trained on Mixes of Instruction Data

Paper • 2404.12195 • Published Apr 18 • 11

RAFT: Adapting Language Model to Domain Specific RAG

Paper • 2403.10131 • Published Mar 15 • 66
InternLM2 Technical Report

Paper • 2403.17297 • Published Mar 26 • 28
LoRA Learns Less and Forgets Less

Paper • 2405.09673 • Published May 15 • 87

Papers - Fine-tuning

Unleashing the Power of Pre-trained Language Models for Offline Reinforcement Learning

Paper • 2310.20587 • Published Oct 31, 2023 • 16
SELF: Language-Driven Self-Evolution for Large Language Model

Paper • 2310.00533 • Published Oct 1, 2023 • 2
QLoRA: Efficient Finetuning of Quantized LLMs

Paper • 2305.14314 • Published May 23, 2023 • 45
QA-LoRA: Quantization-Aware Low-Rank Adaptation of Large Language Models

Paper • 2309.14717 • Published Sep 26, 2023 • 43

GaLore: Memory-Efficient LLM Training by Gradient Low-Rank Projection

Paper • 2403.03507 • Published Mar 6 • 182
RAFT: Adapting Language Model to Domain Specific RAG

Paper • 2403.10131 • Published Mar 15 • 66
LlamaFactory: Unified Efficient Fine-Tuning of 100+ Language Models

Paper • 2403.13372 • Published Mar 20 • 58
InternLM2 Technical Report

Paper • 2403.17297 • Published Mar 26 • 28

Papers - Training Research

Measuring the Effects of Data Parallelism on Neural Network Training

Paper • 1811.03600 • Published Nov 8, 2018 • 2
Adafactor: Adaptive Learning Rates with Sublinear Memory Cost

Paper • 1804.04235 • Published Apr 11, 2018 • 2
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks

Paper • 1905.11946 • Published May 28, 2019 • 3
Yi: Open Foundation Models by 01.AI

Paper • 2403.04652 • Published Mar 7 • 61

Beyond Language Models: Byte Models are Digital World Simulators

Paper • 2402.19155 • Published Feb 29 • 49
Griffin: Mixing Gated Linear Recurrences with Local Attention for Efficient Language Models

Paper • 2402.19427 • Published Feb 29 • 52
VisionLLaMA: A Unified LLaMA Interface for Vision Tasks

Paper • 2403.00522 • Published Mar 1 • 44
Resonance RoPE: Improving Context Length Generalization of Large Language Models

Paper • 2403.00071 • Published Feb 29 • 22

Nemotron-4 15B Technical Report

Paper • 2402.16819 • Published Feb 26 • 42
InternLM2 Technical Report

Paper • 2403.17297 • Published Mar 26 • 28
Chinese Tiny LLM: Pretraining a Chinese-Centric Large Language Model

Paper • 2404.04167 • Published Apr 5 • 12
MobileLLM: Optimizing Sub-billion Parameter Language Models for On-Device Use Cases

Paper • 2402.14905 • Published Feb 22 • 108

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