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We introduce Llamba, a family of efficient recurrent language models distilled from Llama-3.x into the Mamba architecture.
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Albert Gu and Tri Dao · 2023
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Llama: Open and efficient foundation language models, 2023
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Jamba: A hybrid transformer-mamba language model, 2024
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, and Yoav Shoham · 2024
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Linearizing large language models, 2024
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The fineweb datasets: Decanting the web for the finest text data at scale, 2024
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Paolo Glorioso, Quentin Anthony, Yury Tokpanov, Anna Golubeva, Vasudev Shyam, James Whittington, Jonathan Pilault, and Beren Millidge
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Zamba: A compact 7b ssm hybrid model, 2024b
Paolo Glorioso, Quentin Anthony, Yury Tokpanov, James Whittington, Jonathan Pilault, Adam Ibrahim, and Beren Millidge
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Roger Waleffe, Wonmin Byeon, Duncan Riach, Brandon Norick, Vijay Korthikanti, Tri Dao, Albert Gu, Ali Hatamizadeh, Sudhakar Singh, Deepak Narayanan, Garvit Kulshreshtha, Vartika Singh, Jared Casper, Jan Kautz, Mohammad Shoeybi, and Bryan Catanzaro · 2024
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The mamba in the llama: Distilling and accelerating hybrid models, 2024
Junxiong Wang, Daniele Paliotta, Avner May, Alexander M. Rush, and Tri Dao · 2024
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Rnns are not transformers (yet): The key bottleneck on in-context retrieval, 2024
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Lolcats: On low-rank linearizing of large language models, 2024
Michael Zhang, Simran Arora, Rahul Chalamala, Alan Wu, Benjamin Spector, Aaryan Singhal, Krithik Ramesh, and Christopher Ré · 2024
Later among the works it cites.
The mamba in the llama: Distilling and accelerating hybrid models, 2025
Junxiong Wang, Daniele Paliotta, Avner May, Alexander M. Rush, and Tri Dao · 2025
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