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Jun 12, 2025
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Discover the best community collections!
Collections including paper arxiv:2310.18547
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Oct 24, 2025
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Jan 3, 2024
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A Unified View of Long-Sequence Models towards Modeling Million-Scale Dependencies
Paper • 2302.06218 • Published • 1 -
ZeRO++: Extremely Efficient Collective Communication for Giant Model Training
Paper • 2306.10209 • Published • 2 -
SE-MoE: A Scalable and Efficient Mixture-of-Experts Distributed Training and Inference System
Paper • 2205.10034 • Published • 1 -
A Hybrid Tensor-Expert-Data Parallelism Approach to Optimize Mixture-of-Experts Training
Paper • 2303.06318 • Published • 1
Collection by
Jul 9, 2024
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QMoE: Practical Sub-1-Bit Compression of Trillion-Parameter Models
Paper • 2310.16795 • Published • 27 -
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
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Oct 21, 2025
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A LoRA-Based Approach to Fine-Tuning LLMs for Educational Guidance in Resource-Constrained Settings
Paper • 2504.15610 • Published • 1 -
Train Small, Infer Large: Memory-Efficient LoRA Training for Large Language Models
Paper • 2502.13533 • Published • 13 -
LoRA-SP: Streamlined Partial Parameter Adaptation for Resource-Efficient Fine-Tuning of Large Language Models
Paper • 2403.08822 • Published -
LoRA-Pro: Are Low-Rank Adapters Properly Optimized?
Paper • 2407.18242 • Published
Collection by
May 2, 2024
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LoRAShear: Efficient Large Language Model Structured Pruning and Knowledge Recovery
Paper • 2310.18356 • Published • 24 -
LoftQ: LoRA-Fine-Tuning-Aware Quantization for Large Language Models
Paper • 2310.08659 • Published • 27 -
ModuLoRA: Finetuning 3-Bit LLMs on Consumer GPUs by Integrating with Modular Quantizers
Paper • 2309.16119 • Published • 1 -
QA-LoRA: Quantization-Aware Low-Rank Adaptation of Large Language Models
Paper • 2309.14717 • Published • 46
Collection by
Jun 4, 2024
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S^{3}: Increasing GPU Utilization during Generative Inference for Higher Throughput
Paper • 2306.06000 • Published • 1 -
Fast Distributed Inference Serving for Large Language Models
Paper • 2305.05920 • Published • 1 -
Response Length Perception and Sequence Scheduling: An LLM-Empowered LLM Inference Pipeline
Paper • 2305.13144 • Published • 1 -
Towards MoE Deployment: Mitigating Inefficiencies in Mixture-of-Expert (MoE) Inference
Paper • 2303.06182 • Published • 1
Collection by
May 29, 2025
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LoftQ: LoRA-Fine-Tuning-Aware Quantization for Large Language Models
Paper • 2310.08659 • Published • 27 -
QA-LoRA: Quantization-Aware Low-Rank Adaptation of Large Language Models
Paper • 2309.14717 • Published • 46 -
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
Collection by
Jun 12, 2025
Collection by
Oct 21, 2025
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A LoRA-Based Approach to Fine-Tuning LLMs for Educational Guidance in Resource-Constrained Settings
Paper • 2504.15610 • Published • 1 -
Train Small, Infer Large: Memory-Efficient LoRA Training for Large Language Models
Paper • 2502.13533 • Published • 13 -
LoRA-SP: Streamlined Partial Parameter Adaptation for Resource-Efficient Fine-Tuning of Large Language Models
Paper • 2403.08822 • Published -
LoRA-Pro: Are Low-Rank Adapters Properly Optimized?
Paper • 2407.18242 • Published
Collection by
Oct 24, 2025
Collection by
May 2, 2024
-
LoRAShear: Efficient Large Language Model Structured Pruning and Knowledge Recovery
Paper • 2310.18356 • Published • 24 -
LoftQ: LoRA-Fine-Tuning-Aware Quantization for Large Language Models
Paper • 2310.08659 • Published • 27 -
ModuLoRA: Finetuning 3-Bit LLMs on Consumer GPUs by Integrating with Modular Quantizers
Paper • 2309.16119 • Published • 1 -
QA-LoRA: Quantization-Aware Low-Rank Adaptation of Large Language Models
Paper • 2309.14717 • Published • 46
Collection by
Jan 3, 2024
-
A Unified View of Long-Sequence Models towards Modeling Million-Scale Dependencies
Paper • 2302.06218 • Published • 1 -
ZeRO++: Extremely Efficient Collective Communication for Giant Model Training
Paper • 2306.10209 • Published • 2 -
SE-MoE: A Scalable and Efficient Mixture-of-Experts Distributed Training and Inference System
Paper • 2205.10034 • Published • 1 -
A Hybrid Tensor-Expert-Data Parallelism Approach to Optimize Mixture-of-Experts Training
Paper • 2303.06318 • Published • 1
Collection by
Jun 4, 2024
-
S^{3}: Increasing GPU Utilization during Generative Inference for Higher Throughput
Paper • 2306.06000 • Published • 1 -
Fast Distributed Inference Serving for Large Language Models
Paper • 2305.05920 • Published • 1 -
Response Length Perception and Sequence Scheduling: An LLM-Empowered LLM Inference Pipeline
Paper • 2305.13144 • Published • 1 -
Towards MoE Deployment: Mitigating Inefficiencies in Mixture-of-Expert (MoE) Inference
Paper • 2303.06182 • Published • 1
Collection by
Jul 9, 2024
-
QMoE: Practical Sub-1-Bit Compression of Trillion-Parameter Models
Paper • 2310.16795 • Published • 27 -
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
Collection by
May 29, 2025
-
LoftQ: LoRA-Fine-Tuning-Aware Quantization for Large Language Models
Paper • 2310.08659 • Published • 27 -
QA-LoRA: Quantization-Aware Low-Rank Adaptation of Large Language Models
Paper • 2309.14717 • Published • 46 -
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