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Collections including paper arxiv:2401.01325

Long context LLM

Sequence Parallelism: Long Sequence Training from System Perspective

Paper • 2105.13120 • Published May 26, 2021 • 5
Ring Attention with Blockwise Transformers for Near-Infinite Context

Paper • 2310.01889 • Published Oct 3, 2023 • 10
Striped Attention: Faster Ring Attention for Causal Transformers

Paper • 2311.09431 • Published Nov 15, 2023 • 4
DeepSpeed Ulysses: System Optimizations for Enabling Training of Extreme Long Sequence Transformer Models

Paper • 2309.14509 • Published Sep 25, 2023 • 17

Lee's RoPE Tricks / Context Extension Reads

Set of Long Context (RoPE or otherwise) I'm collecting off of HF

LongRoPE: Extending LLM Context Window Beyond 2 Million Tokens

Paper • 2402.13753 • Published Feb 21 • 111
Data Engineering for Scaling Language Models to 128K Context

Paper • 2402.10171 • Published Feb 15 • 21
LongAgent: Scaling Language Models to 128k Context through Multi-Agent Collaboration

Paper • 2402.11550 • Published Feb 18 • 15
The What, Why, and How of Context Length Extension Techniques in Large Language Models -- A Detailed Survey

Paper • 2401.07872 • Published Jan 15 • 2

Chain-of-Thought Reasoning Without Prompting

Paper • 2402.10200 • Published Feb 15 • 99
How to Train Data-Efficient LLMs

Paper • 2402.09668 • Published Feb 15 • 38
BitDelta: Your Fine-Tune May Only Be Worth One Bit

Paper • 2402.10193 • Published Feb 15 • 17
A Human-Inspired Reading Agent with Gist Memory of Very Long Contexts

Paper • 2402.09727 • Published Feb 15 • 35

E^2-LLM: Efficient and Extreme Length Extension of Large Language Models

Paper • 2401.06951 • Published Jan 13 • 24
LLM Maybe LongLM: Self-Extend LLM Context Window Without Tuning

Paper • 2401.01325 • Published Jan 2 • 26
Extending LLMs' Context Window with 100 Samples

Paper • 2401.07004 • Published Jan 13 • 14
LongAlign: A Recipe for Long Context Alignment of Large Language Models

Paper • 2401.18058 • Published Jan 31 • 21

Griffin: Mixing Gated Linear Recurrences with Local Attention for Efficient Language Models

Paper • 2402.19427 • Published Feb 29 • 52
Simple linear attention language models balance the recall-throughput tradeoff

Paper • 2402.18668 • Published Feb 28 • 18
ChunkAttention: Efficient Self-Attention with Prefix-Aware KV Cache and Two-Phase Partition

Paper • 2402.15220 • Published Feb 23 • 19
Linear Transformers are Versatile In-Context Learners

Paper • 2402.14180 • Published Feb 21 • 6

Medusa: Simple LLM Inference Acceleration Framework with Multiple Decoding Heads

Paper • 2401.10774 • Published Jan 19 • 53
APAR: LLMs Can Do Auto-Parallel Auto-Regressive Decoding

Paper • 2401.06761 • Published Jan 12 • 1
Infinite-LLM: Efficient LLM Service for Long Context with DistAttention and Distributed KVCache

Paper • 2401.02669 • Published Jan 5 • 14
MambaByte: Token-free Selective State Space Model

Paper • 2401.13660 • Published Jan 24 • 50

Self-Rewarding Language Models

Paper • 2401.10020 • Published Jan 18 • 143
ReFT: Reasoning with Reinforced Fine-Tuning

Paper • 2401.08967 • Published Jan 17 • 27
Tuning Language Models by Proxy

Paper • 2401.08565 • Published Jan 16 • 20
TrustLLM: Trustworthiness in Large Language Models

Paper • 2401.05561 • Published Jan 10 • 64

Soaring from 4K to 400K: Extending LLM's Context with Activation Beacon

Paper • 2401.03462 • Published Jan 7 • 26
MEGABYTE: Predicting Million-byte Sequences with Multiscale Transformers

Paper • 2305.07185 • Published May 12, 2023 • 9
YaRN: Efficient Context Window Extension of Large Language Models

Paper • 2309.00071 • Published Aug 31, 2023 • 65
Infinite-LLM: Efficient LLM Service for Long Context with DistAttention and Distributed KVCache

Paper • 2401.02669 • Published Jan 5 • 14

LLM Maybe LongLM: Self-Extend LLM Context Window Without Tuning

Paper • 2401.01325 • Published Jan 2 • 26

LLM Maybe LongLM: Self-Extend LLM Context Window Without Tuning

Paper • 2401.01325 • Published Jan 2 • 26
WaveCoder: Widespread And Versatile Enhanced Instruction Tuning with Refined Data Generation

Paper • 2312.14187 • Published Dec 20, 2023 • 49
Depth Anything: Unleashing the Power of Large-Scale Unlabeled Data

Paper • 2401.10891 • Published Jan 19 • 58
MiniCPM: Unveiling the Potential of Small Language Models with Scalable Training Strategies

Paper • 2404.06395 • Published Apr 9 • 21

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