kriskrisliu/PAT
[AAAI 2025] PAT: Pruning-Aware Tuning for Large Language Models
This project offers a method for making Large Language Models (LLMs) like Llama2 and Gemma more efficient without losing much performance. It takes an existing LLM and training data, then outputs a smaller, faster LLM that's easier to deploy. This is for machine learning engineers and researchers who are developing and deploying custom LLMs.
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Use this if you need to reduce the size and computational cost of a Large Language Model for easier deployment while maintaining its core capabilities.
Not ideal if you are looking for a pre-trained, ready-to-use LLM or if you do not have the technical expertise to fine-tune and prune models.
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Python
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Last pushed
Feb 01, 2025
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