ZhengaoLi/DISP-LLM-Dimension-Independent-Structural-Pruning
An implementation of the DISP-LLM method from the NeurIPS 2024 paper: Dimension-Independent Structural Pruning for Large Language Models.
This project helps machine learning engineers and researchers reduce the computational cost and memory footprint of large language models like LLaMA 7B/13B and LLaMA-2 7B/13B. By taking a pre-trained LLaMA model and applying a technique called structural pruning, it outputs a smaller, more efficient version of the model that maintains its performance. This is ideal for those deploying LLMs in resource-constrained environments or seeking to optimize existing models.
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Use this if you need to make LLaMA 7B/13B or LLaMA-2 7B/13B models smaller and faster without significant performance loss.
Not ideal if you are working with other large language models outside the LLaMA family or if your primary goal is to improve model accuracy rather than efficiency.
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Language
Python
License
Apache-2.0
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Last pushed
Aug 06, 2025
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