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๐๐ก๐ ๐ซ๐๐ญ๐ฎ๐ซ๐ง ๐จ๐ ๐ญ๐ก๐ ๐๐๐๐ฌ โ ๐๐๐ฐ ๐๐๐ฆ๐๐-๐๐๐ฌ๐๐ ๐๐ซ๐๐ก๐ข๐ญ๐๐๐ญ๐ฎ๐ซ๐ "๐๐๐ฆ๐๐"
Since the release of BERT by Google in 2019, Transformers architecture have taken over machine learning thanks to their ๐ฎ๐๐๐ฒ๐ป๐๐ถ๐ผ๐ป ๐บ๐ฒ๐ฐ๐ต๐ฎ๐ป๐ถ๐๐บ, that gives them the ability to focus on important points of the input. But ๐๐ฉ๐ฉ๐๐ฃ๐ฉ๐๐ค๐ฃ ๐๐ค๐ข๐ฅ๐ช๐ฉ๐๐ฉ๐๐ค๐ฃ ๐๐จ ๐ฆ๐ช๐๐๐ง๐๐ฉ๐๐ ๐๐ฃ ๐ฉ๐๐ ๐๐ฃ๐ฅ๐ช๐ฉ ๐ก๐๐ฃ๐๐ฉ๐.
๐ซ The Mamba paper, published in December 2023, announced the return of the RNNs: it has no attention, but integrates a selection mechanism, which should be able to reproduce the โfocusโ ability of attention, in an architecture for which the compute requirements ๐ด๐ฟ๐ผ๐ ๐ผ๐ป๐น๐ ๐น๐ถ๐ป๐ฒ๐ฎ๐ฟ๐น๐ ๐ถ๐ป ๐ถ๐ป๐ฝ๐๐ ๐น๐ฒ๐ป๐ด๐๐ต!
๐ค Would this work? We had yet to see a large Mamba model recovering the performance of Attention-based Transformers.
๐ฅ But now it's done! A (Mamba + Transformers) hybrid just beat Transformers!
The AI21 Labs team just released Jamba.
They insert a few Transformer layers to inject some attention in a big pile of Mamba layers, thus getting the best of both worlds.
๐๐;๐ฟ๐:
๐๏ธ ๐ก๐ฒ๐ ๐ ๐ผ๐ ๐ฎ๐ฟ๐ฐ๐ต๐ถ๐๐ฒ๐ฐ๐๐๐ฟ๐ฒ: 4 Jamba blocks, each of these being 7 Mamba layers for 1 Transformer.
๐๏ธ ๐ฑ๐ฎ๐ ๐ฝ๐ฎ๐ฟ๐ฎ๐บ๐ฒ๐๐ฒ๐ฟ๐, ๐ญ๐ฎ๐ ๐ฎ๐ฐ๐๐ถ๐๐ฒ ๐ฎ๐ ๐ถ๐ป๐ณ๐ฒ๐ฟ๐ฒ๐ป๐ฐ๐ฒ: This reduction is enabled by Mixture of Experts, and similar to Mixtral (47B parameters - 13B active).
๐๏ธ ๐ฆ๐ฝ๐ฒ๐ฒ๐ฑ: ๐ ๐ฏ ๐๐ต๐ฟ๐ผ๐๐ด๐ต๐ฝ๐๐. Jamba is much faster than similar-sized Transformer models on long contexts.
๐ ๐๐ผ๐ป๐๐ฒ๐ ๐ ๐น๐ฒ๐ป๐ด๐๐ต: ๐ญ๐ฐ๐ฌ๐ ๐๐ผ๐ธ๐ฒ๐ป๐ on a single 80GB A100!
๐ช ๐ฃ๐ฒ๐ฟ๐ณ๐ผ๐ฟ๐บ๐ฎ๐ป๐ฐ๐ฒ: ๐๐๐ฎ๐๐ฒ-๐ผ๐ณ-๐๐ต๐ฒ-๐ฎ๐ฟ๐ ๐ณ๐ผ๐ฟ ๐๐ต๐ถ๐ ๐๐ถ๐๐ฒ. The small injection of attention seems sufficient since Jamba beats the open-source reference Mixtral-8x7B on many benchmarks!
Try it here ๐ ai21labs/Jamba-v0.1
Since the release of BERT by Google in 2019, Transformers architecture have taken over machine learning thanks to their ๐ฎ๐๐๐ฒ๐ป๐๐ถ๐ผ๐ป ๐บ๐ฒ๐ฐ๐ต๐ฎ๐ป๐ถ๐๐บ, that gives them the ability to focus on important points of the input. But ๐๐ฉ๐ฉ๐๐ฃ๐ฉ๐๐ค๐ฃ ๐๐ค๐ข๐ฅ๐ช๐ฉ๐๐ฉ๐๐ค๐ฃ ๐๐จ ๐ฆ๐ช๐๐๐ง๐๐ฉ๐๐ ๐๐ฃ ๐ฉ๐๐ ๐๐ฃ๐ฅ๐ช๐ฉ ๐ก๐๐ฃ๐๐ฉ๐.
๐ซ The Mamba paper, published in December 2023, announced the return of the RNNs: it has no attention, but integrates a selection mechanism, which should be able to reproduce the โfocusโ ability of attention, in an architecture for which the compute requirements ๐ด๐ฟ๐ผ๐ ๐ผ๐ป๐น๐ ๐น๐ถ๐ป๐ฒ๐ฎ๐ฟ๐น๐ ๐ถ๐ป ๐ถ๐ป๐ฝ๐๐ ๐น๐ฒ๐ป๐ด๐๐ต!
๐ค Would this work? We had yet to see a large Mamba model recovering the performance of Attention-based Transformers.
๐ฅ But now it's done! A (Mamba + Transformers) hybrid just beat Transformers!
The AI21 Labs team just released Jamba.
They insert a few Transformer layers to inject some attention in a big pile of Mamba layers, thus getting the best of both worlds.
๐๐;๐ฟ๐:
๐๏ธ ๐ก๐ฒ๐ ๐ ๐ผ๐ ๐ฎ๐ฟ๐ฐ๐ต๐ถ๐๐ฒ๐ฐ๐๐๐ฟ๐ฒ: 4 Jamba blocks, each of these being 7 Mamba layers for 1 Transformer.
๐๏ธ ๐ฑ๐ฎ๐ ๐ฝ๐ฎ๐ฟ๐ฎ๐บ๐ฒ๐๐ฒ๐ฟ๐, ๐ญ๐ฎ๐ ๐ฎ๐ฐ๐๐ถ๐๐ฒ ๐ฎ๐ ๐ถ๐ป๐ณ๐ฒ๐ฟ๐ฒ๐ป๐ฐ๐ฒ: This reduction is enabled by Mixture of Experts, and similar to Mixtral (47B parameters - 13B active).
๐๏ธ ๐ฆ๐ฝ๐ฒ๐ฒ๐ฑ: ๐ ๐ฏ ๐๐ต๐ฟ๐ผ๐๐ด๐ต๐ฝ๐๐. Jamba is much faster than similar-sized Transformer models on long contexts.
๐ ๐๐ผ๐ป๐๐ฒ๐ ๐ ๐น๐ฒ๐ป๐ด๐๐ต: ๐ญ๐ฐ๐ฌ๐ ๐๐ผ๐ธ๐ฒ๐ป๐ on a single 80GB A100!
๐ช ๐ฃ๐ฒ๐ฟ๐ณ๐ผ๐ฟ๐บ๐ฎ๐ป๐ฐ๐ฒ: ๐๐๐ฎ๐๐ฒ-๐ผ๐ณ-๐๐ต๐ฒ-๐ฎ๐ฟ๐ ๐ณ๐ผ๐ฟ ๐๐ต๐ถ๐ ๐๐ถ๐๐ฒ. The small injection of attention seems sufficient since Jamba beats the open-source reference Mixtral-8x7B on many benchmarks!
Try it here ๐ ai21labs/Jamba-v0.1