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References (35)

[1]
SlimGPT: Layer-wise Structured Pruning for Large Language Models
2024Gui Ling, Ziyang Wang et al.
[2]
ScaleOT: Privacy-utility-scalable Offsite-tuning with Dynamic LayerReplace and Selective Rank Compression
2024Kai Yao, Zhaorui Tan et al.
[3]
NegMerge: Sign-Consensual Weight Merging for Machine Unlearning
2024Hyoseo Kim, Dongyoon Han et al.
[4]
The Llama 3 Herd of Models
2024Abhimanyu Dubey, Abhinav Jauhri et al.
[5]
Compact Language Models via Pruning and Knowledge Distillation
2024Saurav Muralidharan, Sharath Turuvekere Sreenivas et al.
[6]
Pruning via Merging: Compressing LLMs via Manifold Alignment Based Layer Merging
2024Deyuan Liu, Zhanyue Qin et al.
[7]
What Matters in Transformers? Not All Attention is Needed
2024Shwai He, Guoheng Sun et al.
[8]
Sparsity-Accelerated Training for Large Language Models
2024Da Ma, Lu Chen et al.
[9]
Parameter-Efficient Fine-Tuning for Large Models: A Comprehensive Survey
2024Zeyu Han, Chao Gao et al.
[10]
ShortGPT: Layers in Large Language Models are More Redundant Than You Expect
2024Xin Men, Mingyu Xu et al.
[11]
LaCo: Large Language Model Pruning via Layer Collapse
2024Yifei Yang, Zouying Cao et al.
[12]
APT: Adaptive Pruning and Tuning Pretrained Language Models for Efficient Training and Inference
2024Bowen Zhao, Hannaneh Hajishirzi et al.
[13]
Model Merging by Uncertainty-Based Gradient Matching
2023Nico Daheim, Thomas Möllenhoff et al.
[14]
NASH: A Simple Unified Framework of Structured Pruning for Accelerating Encoder-Decoder Language Models
2023Jongwoo Ko, Seungjoon Park et al.
[15]
Instruction Tuning for Large Language Models: A Survey
2023Shengyu Zhang, Linfeng Dong et al.
[16]
Zhongjing: Enhancing the Chinese Medical Capabilities of Large Language Model through Expert Feedback and Real-world Multi-turn Dialogue
2023Songhua Yang, Hanjia Zhao et al.
[17]
FinGPT: Open-Source Financial Large Language Models
2023Hongyang Yang, Xiao-Yang Liu et al.
[18]
TIES-Merging: Resolving Interference When Merging Models
2023Prateek Yadav, Derek Tam et al.
[19]
LLM-Pruner: On the Structural Pruning of Large Language Models
2023Xinyin Ma, Gongfan Fang et al.
[20]
The State of Sparse Training in Deep Reinforcement Learning
2022Laura Graesser, Utku Evci et al.

Showing 20 of 35 references

Founder's Pitch

"GradPruner offers a gradient-guided layer pruning tool to efficiently fine-tune and run LLMs with significant parameter reduction and minimal accuracy loss."

AI Model OptimizationScore: 7View PDF ↗

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0-10 scale

High Potential

1/4 signals

2.5

Quick Build

4/4 signals

10

Series A Potential

2/4 signals

5

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Why It Matters

Fine-tuning large language models can be resource-intensive and slow. GradPruner addresses this by offering a method to prune model layers early, making the process faster and more efficient, ultimately reducing the time and computational cost associated with LLMs for downstream tasks.

Product Angle

Develop an API that integrates with existing ML frameworks like PyTorch, allowing developers to use GradPruner's method to optimize models with a simple interface.

Disruption

GradPruner could replace traditional, more resource-intensive fine-tuning methods, offering a more affordable and efficient alternative for model optimization.

Product Opportunity

With growing adoption of AI, companies and research labs face high costs for model fine-tuning. A tool that reduces these costs has a vast market, particularly benefiting small to medium AI enterprises and research institutions.

Use Case Idea

Create a SaaS for AI developers to quickly optimize their language models using GradPruner, reducing operational costs and accelerating deployment in resource-constrained environments.

Science

GradPruner leverages gradients computed during the initial phase of model fine-tuning to assess layer importance. It then prunes less important layers, using an accumulation matrix to guide this process while maintaining model performance—a novel and efficient take on structured pruning.

Method & Eval

GradPruner was tested on two LLMs over eight datasets, including benchmarks in the medical and financial domains, showing a significant 40% reduction in model parameters with a negligible 0.99% drop in accuracy.

Caveats

The reduction in parameters might lead to minimal accuracy loss, which could be unacceptable for critical applications. Moreover, compatibility across various model architectures hasn't been fully explored.

Author Intelligence

Wei Huang

Anda Cheng

Yinggui Wang