princeton-nlp/CoFiPruning
[ACL 2022] Structured Pruning Learns Compact and Accurate Models https://arxiv.org/abs/2204.00408
This project helps machine learning engineers and researchers create smaller, faster, and more efficient language models for tasks like text classification and question answering. It takes a pre-trained, large language model and outputs a significantly more compact version that runs faster while maintaining competitive accuracy. This is ideal for those deploying language models in resource-constrained environments.
198 stars. No commits in the last 6 months.
Use this if you need to deploy large language models on devices with limited memory or processing power, or if you want to reduce inference costs and latency for your NLP applications.
Not ideal if your primary goal is to train a brand-new model from scratch or if you require the absolute highest accuracy without any compromise on model size or speed.
Stars
198
Forks
31
Language
Python
License
MIT
Category
Last pushed
May 09, 2023
Commits (30d)
0
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