Summary of Jamba: a Hybrid Transformer-mamba Language Model, by Opher Lieber et al.
Jamba: A Hybrid Transformer-Mamba Language Model
by Opher Lieber, Barak Lenz, Hofit Bata, Gal Cohen, Jhonathan Osin, Itay Dalmedigos, Erez Safahi, Shaked Meirom, Yonatan Belinkov, Shai Shalev-Shwartz, Omri Abend, Raz Alon, Tomer Asida, Amir Bergman, Roman Glozman, Michael Gokhman, Avashalom Manevich, Nir Ratner, Noam Rozen, Erez Shwartz, Mor Zusman, Yoav Shoham
First submitted to arxiv on: 28 Mar 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Machine Learning (cs.LG)
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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 The paper presents Jamba, a new base large language model that combines the benefits of Transformer and Mamba architectures with a hybrid Transformer-Mamba mixture-of-experts (MoE) design. This flexible architecture allows for resource- and objective-specific configurations, leading to high throughput and small memory footprint compared to vanilla Transformers, while achieving state-of-the-art performance on standard language model benchmarks and long-context evaluations. The authors study various architectural decisions and reveal several interesting properties of the novel architecture. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Jamba is a new type of AI model that combines two existing models to create something even better. It’s like a superpowerful computer that can understand and generate human-like text, but it only needs a single 80GB GPU to work. This means it can be used for many different tasks, from answering questions to generating creative writing. The researchers behind Jamba are sharing their findings so others can build on this new technology. |
Keywords
* Artificial intelligence * Language model * Large language model * Mixture of experts * Transformer
