-
QMoE: Practical Sub-1-Bit Compression of Trillion-Parameter Models
Paper • 2310.16795 • Published • 26 -
Pre-gated MoE: An Algorithm-System Co-Design for Fast and Scalable Mixture-of-Expert Inference
Paper • 2308.12066 • Published • 4 -
Towards MoE Deployment: Mitigating Inefficiencies in Mixture-of-Expert (MoE) Inference
Paper • 2303.06182 • Published • 1 -
EvoMoE: An Evolutional Mixture-of-Experts Training Framework via Dense-To-Sparse Gate
Paper • 2112.14397 • Published • 1
Collections
Discover the best community collections!
Collections including paper arxiv:2311.13171
-
Experts Weights Averaging: A New General Training Scheme for Vision Transformers
Paper • 2308.06093 • Published • 2 -
Platypus: Quick, Cheap, and Powerful Refinement of LLMs
Paper • 2308.07317 • Published • 23 -
Beyond Attentive Tokens: Incorporating Token Importance and Diversity for Efficient Vision Transformers
Paper • 2211.11315 • Published • 1 -
LoraHub: Efficient Cross-Task Generalization via Dynamic LoRA Composition
Paper • 2307.13269 • Published • 31
-
Deja Vu: Contextual Sparsity for Efficient LLMs at Inference Time
Paper • 2310.17157 • Published • 11 -
Dynamic Context Pruning for Efficient and Interpretable Autoregressive Transformers
Paper • 2305.15805 • Published • 1 -
Compress, Then Prompt: Improving Accuracy-Efficiency Trade-off of LLM Inference with Transferable Prompt
Paper • 2305.11186 • Published • 1 -
Composable Sparse Fine-Tuning for Cross-Lingual Transfer
Paper • 2110.07560 • Published • 1
-
QMoE: Practical Sub-1-Bit Compression of Trillion-Parameter Models
Paper • 2310.16795 • Published • 26 -
Pre-gated MoE: An Algorithm-System Co-Design for Fast and Scalable Mixture-of-Expert Inference
Paper • 2308.12066 • Published • 4 -
Towards MoE Deployment: Mitigating Inefficiencies in Mixture-of-Expert (MoE) Inference
Paper • 2303.06182 • Published • 1 -
EvoMoE: An Evolutional Mixture-of-Experts Training Framework via Dense-To-Sparse Gate
Paper • 2112.14397 • Published • 1
-
LoftQ: LoRA-Fine-Tuning-Aware Quantization for Large Language Models
Paper • 2310.08659 • Published • 22 -
QA-LoRA: Quantization-Aware Low-Rank Adaptation of Large Language Models
Paper • 2309.14717 • Published • 44 -
ModuLoRA: Finetuning 3-Bit LLMs on Consumer GPUs by Integrating with Modular Quantizers
Paper • 2309.16119 • Published • 1 -
LoRA ensembles for large language model fine-tuning
Paper • 2310.00035 • Published • 2