Summary of Zamba: a Compact 7b Ssm Hybrid Model, by Paolo Glorioso et al.
Zamba: A Compact 7B SSM Hybrid Model
by Paolo Glorioso, Quentin Anthony, Yury Tokpanov, James Whittington, Jonathan Pilault, Adam Ibrahim, Beren Millidge
First submitted to arxiv on: 26 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); 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 This technical report presents Zamba, a novel hybrid model that achieves competitive performance with leading models at a comparable scale. Trained on 1T tokens from openly available datasets, Zamba is the best non-transformer model at this scale. The unique architecture combines Mamba and shared attention, providing benefits of attention at minimal parameter cost. Zamba outperforms transformer models in inference speed and memory requirements for generating long sequences. Pretraining occurs in two phases: initial training on web datasets followed by annealing over high-quality instruct and synthetic datasets with rapid learning rate decay. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Zamba is a new model that can process large amounts of text quickly and efficiently. It’s like a superpower for computers! Zamba works by combining different techniques to understand language, which helps it learn from lots of data. This means it can be used in many applications, such as generating text or answering questions. The best part is that Zamba is fast and uses less memory than other models, making it perfect for big tasks. |
Keywords
» Artificial intelligence » Attention » Inference » Pretraining » Transformer
