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Collections including paper arxiv:2401.06104

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MM1: Methods, Analysis & Insights from Multimodal LLM Pre-training

Paper • 2403.09611 • Published Mar 14 • 124
Evolutionary Optimization of Model Merging Recipes

Paper • 2403.13187 • Published Mar 19 • 50
MobileVLM V2: Faster and Stronger Baseline for Vision Language Model

Paper • 2402.03766 • Published Feb 6 • 12
LLM Agent Operating System

Paper • 2403.16971 • Published Mar 25 • 65

Interesting things.

AtP*: An efficient and scalable method for localizing LLM behaviour to components

Paper • 2403.00745 • Published Mar 1 • 11
The Era of 1-bit LLMs: All Large Language Models are in 1.58 Bits

Paper • 2402.17764 • Published Feb 27 • 602
MobiLlama: Towards Accurate and Lightweight Fully Transparent GPT

Paper • 2402.16840 • Published Feb 26 • 23
LongRoPE: Extending LLM Context Window Beyond 2 Million Tokens

Paper • 2402.13753 • Published Feb 21 • 111

Transformers alternatives

Transformers are Multi-State RNNs

Paper • 2401.06104 • Published Jan 11 • 36
Linear Transformers with Learnable Kernel Functions are Better In-Context Models

Paper • 2402.10644 • Published Feb 16 • 78
In Search of Needles in a 10M Haystack: Recurrent Memory Finds What LLMs Miss

Paper • 2402.10790 • Published Feb 16 • 40
The Era of 1-bit LLMs: All Large Language Models are in 1.58 Bits

Paper • 2402.17764 • Published Feb 27 • 602

Transformers are Multi-State RNNs

Paper • 2401.06104 • Published Jan 11 • 36

Transformers are Multi-State RNNs

Paper • 2401.06104 • Published Jan 11 • 36
Blending Is All You Need: Cheaper, Better Alternative to Trillion-Parameters LLM

Paper • 2401.02994 • Published Jan 4 • 48
TinyLlama: An Open-Source Small Language Model

Paper • 2401.02385 • Published Jan 4 • 89

ParadigmShift-Inquiry

Transformers are Multi-State RNNs

Paper • 2401.06104 • Published Jan 11 • 36
Repeat After Me: Transformers are Better than State Space Models at Copying

Paper • 2402.01032 • Published Feb 1 • 22
Can Large Language Models Understand Context?

Paper • 2402.00858 • Published Feb 1 • 21

Transformers are Multi-State RNNs

Paper • 2401.06104 • Published Jan 11 • 36

Transformers are Multi-State RNNs

Paper • 2401.06104 • Published Jan 11 • 36
Chronos: Learning the Language of Time Series

Paper • 2403.07815 • Published Mar 12 • 45

Understanding LLMs: A Comprehensive Overview from Training to Inference

Paper • 2401.02038 • Published Jan 4 • 62
DocLLM: A layout-aware generative language model for multimodal document understanding

Paper • 2401.00908 • Published Dec 31, 2023 • 181
LLaMA Beyond English: An Empirical Study on Language Capability Transfer

Paper • 2401.01055 • Published Jan 2 • 54
LLM Maybe LongLM: Self-Extend LLM Context Window Without Tuning

Paper • 2401.01325 • Published Jan 2 • 26

Inference speed

FlashDecoding++: Faster Large Language Model Inference on GPUs

Paper • 2311.01282 • Published Nov 2, 2023 • 35
Co-training and Co-distillation for Quality Improvement and Compression of Language Models

Paper • 2311.02849 • Published Nov 6, 2023 • 3
Prompt Cache: Modular Attention Reuse for Low-Latency Inference

Paper • 2311.04934 • Published Nov 7, 2023 • 28
Exponentially Faster Language Modelling

Paper • 2311.10770 • Published Nov 15, 2023 • 118

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