by @RachidAR
RuAR
RachidAR
AI & ML interests
1.58 bit LLM
Organizations
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The Era of 1-bit LLMs: All Large Language Models are in 1.58 Bits
Paper • 2402.17764 • Published • 627 -
GaLore: Memory-Efficient LLM Training by Gradient Low-Rank Projection
Paper • 2403.03507 • Published • 189 -
Griffin: Mixing Gated Linear Recurrences with Local Attention for Efficient Language Models
Paper • 2402.19427 • Published • 56 -
ResLoRA: Identity Residual Mapping in Low-Rank Adaption
Paper • 2402.18039 • Published • 11
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Addition is All You Need for Energy-efficient Language Models
Paper • 2410.00907 • Published • 151 -
The Era of 1-bit LLMs: All Large Language Models are in 1.58 Bits
Paper • 2402.17764 • Published • 627 -
LayerSkip: Enabling Early Exit Inference and Self-Speculative Decoding
Paper • 2404.16710 • Published • 80 -
Beyond Scaling Laws: Understanding Transformer Performance with Associative Memory
Paper • 2405.08707 • Published • 34
by @RachidAR
-
Addition is All You Need for Energy-efficient Language Models
Paper • 2410.00907 • Published • 151 -
The Era of 1-bit LLMs: All Large Language Models are in 1.58 Bits
Paper • 2402.17764 • Published • 627 -
LayerSkip: Enabling Early Exit Inference and Self-Speculative Decoding
Paper • 2404.16710 • Published • 80 -
Beyond Scaling Laws: Understanding Transformer Performance with Associative Memory
Paper • 2405.08707 • Published • 34
-
The Era of 1-bit LLMs: All Large Language Models are in 1.58 Bits
Paper • 2402.17764 • Published • 627 -
GaLore: Memory-Efficient LLM Training by Gradient Low-Rank Projection
Paper • 2403.03507 • Published • 189 -
Griffin: Mixing Gated Linear Recurrences with Local Attention for Efficient Language Models
Paper • 2402.19427 • Published • 56 -
ResLoRA: Identity Residual Mapping in Low-Rank Adaption
Paper • 2402.18039 • Published • 11