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Collections including paper arxiv:2310.11453

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Self-Play Preference Optimization for Language Model Alignment

Paper • 2405.00675 • Published May 1 • 22
FlashAttention: Fast and Memory-Efficient Exact Attention with IO-Awareness

Paper • 2205.14135 • Published May 27, 2022 • 9
Attention Is All You Need

Paper • 1706.03762 • Published Jun 12, 2017 • 41
FlashAttention-2: Faster Attention with Better Parallelism and Work Partitioning

Paper • 2307.08691 • Published Jul 17, 2023 • 7

The Era of 1-bit LLMs: All Large Language Models are in 1.58 Bits

Paper • 2402.17764 • Published Feb 27 • 587
BitNet: Scaling 1-bit Transformers for Large Language Models

Paper • 2310.11453 • Published Oct 17, 2023 • 96
Mixture-of-Depths: Dynamically allocating compute in transformer-based language models

Paper • 2404.02258 • Published Apr 2 • 102
TransformerFAM: Feedback attention is working memory

Paper • 2404.09173 • Published Apr 14 • 43

Self-Supervised Learning with Lie Symmetries for Partial Differential Equations

Paper • 2307.05432 • Published Jul 11, 2023 • 14
BitNet: Scaling 1-bit Transformers for Large Language Models

Paper • 2310.11453 • Published Oct 17, 2023 • 96

BitNet: Scaling 1-bit Transformers for Large Language Models

Paper • 2310.11453 • Published Oct 17, 2023 • 96

BitNet: Scaling 1-bit Transformers for Large Language Models

Paper • 2310.11453 • Published Oct 17, 2023 • 96
Phi-3 Technical Report: A Highly Capable Language Model Locally on Your Phone

Paper • 2404.14219 • Published Apr 22 • 245

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 • 587
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 • 109

paper_collection

The Era of 1-bit LLMs: All Large Language Models are in 1.58 Bits

Paper • 2402.17764 • Published Feb 27 • 587
Yi: Open Foundation Models by 01.AI

Paper • 2403.04652 • Published Mar 7 • 61
Simple and Scalable Strategies to Continually Pre-train Large Language Models

Paper • 2403.08763 • Published Mar 13 • 48
Stealing Part of a Production Language Model

Paper • 2403.06634 • Published Mar 11 • 90

BitNet: Scaling 1-bit Transformers for Large Language Models

Paper • 2310.11453 • Published Oct 17, 2023 • 96
The Era of 1-bit LLMs: All Large Language Models are in 1.58 Bits

Paper • 2402.17764 • Published Feb 27 • 587

Efficient Training

DoRA: Weight-Decomposed Low-Rank Adaptation

Paper • 2402.09353 • Published Feb 14 • 24
The Era of 1-bit LLMs: All Large Language Models are in 1.58 Bits

Paper • 2402.17764 • Published Feb 27 • 587
BitNet: Scaling 1-bit Transformers for Large Language Models

Paper • 2310.11453 • Published Oct 17, 2023 • 96

BitNet: Scaling 1-bit Transformers for Large Language Models

Paper • 2310.11453 • Published Oct 17, 2023 • 96

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