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Collections including paper arxiv:2402.18668

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 • 110
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

Papers I find interesting

Scaling Instruction-Finetuned Language Models

Paper • 2210.11416 • Published Oct 20, 2022 • 6
Mamba: Linear-Time Sequence Modeling with Selective State Spaces

Paper • 2312.00752 • Published Dec 1, 2023 • 138
Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context

Paper • 2403.05530 • Published Mar 8 • 59
Yi: Open Foundation Models by 01.AI

Paper • 2403.04652 • Published Mar 7 • 61

Attention in LLM

Simple linear attention language models balance the recall-throughput tradeoff

Paper • 2402.18668 • Published Feb 28 • 18
BurstAttention: An Efficient Distributed Attention Framework for Extremely Long Sequences

Paper • 2403.09347 • Published Mar 14 • 20

StarCoder 2 and The Stack v2: The Next Generation

Paper • 2402.19173 • Published Feb 29 • 132
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
Priority Sampling of Large Language Models for Compilers

Paper • 2402.18734 • Published Feb 28 • 16

Large Language Models

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

daily_paper_coll

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

Paper • 2402.19427 • Published Feb 29 • 52
Beyond Language Models: Byte Models are Digital World Simulators

Paper • 2402.19155 • Published Feb 29 • 49
StarCoder 2 and The Stack v2: The Next Generation

Paper • 2402.19173 • Published Feb 29 • 132
Simple linear attention language models balance the recall-throughput tradeoff

Paper • 2402.18668 • Published Feb 28 • 18

MegaScale: Scaling Large Language Model Training to More Than 10,000 GPUs

Paper • 2402.15627 • Published Feb 23 • 33
Sora: A Review on Background, Technology, Limitations, and Opportunities of Large Vision Models

Paper • 2402.17177 • Published Feb 27 • 88
Beyond Language Models: Byte Models are Digital World Simulators

Paper • 2402.19155 • Published Feb 29 • 49
Hydragen: High-Throughput LLM Inference with Shared Prefixes

Paper • 2402.05099 • Published Feb 7 • 18

These language model checkpoints are trained at the 360M and 1.3Bn parameter scales for up to 50Bn tokens on the Pile corpus, for research purposes.

hazyresearch/based-360m

Updated Apr 21 • 209 • 3
hazyresearch/attn-360m

Updated Apr 21 • 122
hazyresearch/mamba-360m

Updated Apr 21 • 57
hazyresearch/based-1b

Updated Apr 21 • 43 • 8

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 • 78
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

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

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