CASIA-LMC-Lab/FLAP
[AAAI 2024] Fluctuation-based Adaptive Structured Pruning for Large Language Models
This project helps machine learning engineers and researchers reduce the computational cost of deploying large language models (LLMs). It takes an existing LLM, such as LLaMA or Vicuna, and produces a smaller, more efficient version without needing extensive retraining. This makes LLMs faster and less resource-intensive to run, while maintaining strong performance on language tasks.
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Use this if you need to deploy large language models more efficiently on devices with limited computational resources or when you want to reduce inference costs.
Not ideal if you need to prune a custom LLM architecture not based on the supported LLaMA or Vicuna families, or if you require full control over the fine-tuning process after pruning.
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70
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17
Language
Python
License
Apache-2.0
Category
Last pushed
Jan 06, 2024
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