Summary of Sparse Upcycling: Inference Inefficient Finetuning, by Sasha Doubov et al.
Sparse Upcycling: Inference Inefficient Finetuning
by Sasha Doubov, Nikhil Sardana, Vitaliy Chiley
First submitted to arxiv on: 13 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computation and Language (cs.CL)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel approach called sparse upcycling transforms pre-trained dense language models into Mixture-of-Experts (MoE) architectures, increasing parameter count and quality. Researchers compared this method to continued pretraining (CPT) across various model sizes, compute budgets, and pretraining durations. The results show that sparse upcycling can achieve better quality, with improvements of over 20% relative to CPT in certain scenarios. However, this comes at a significant inference cost, leading to 40% slowdowns for larger models in high-demand settings. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Small language models are widely used because they’re efficient, but making them even better is hard. A new way called sparse upcycling takes a pre-trained model and makes it better by adding more parts that work together. This helps the model be smarter, but it also slows down how fast it can make predictions. The researchers compared this to another method called continued pretraining (CPT). They found that sparse upcycling is good at making models smarter, but it’s not as efficient. |
Keywords
» Artificial intelligence » Inference » Mixture of experts » Pretraining
