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Mamba [a] is a deep learning architecture focused on sequence modeling. It was developed by researchers from Carnegie Mellon University and Princeton University to address some limitations of transformer models , especially in processing long sequences.
Codestral Mamba 7B. Codestral Mamba is based on the Mamba 2 architecture, which allows it to generate responses even with longer input. [42] Unlike Codestral, it was released under the Apache 2.0 license. While previous releases often included both the base model and the instruct version, only the instruct version of Codestral Mamba was ...
llama.cpp began development in March 2023 by Georgi Gerganov as an implementation of the Llama inference code in pure C/C++ with no dependencies. This improved performance on computers without GPU or other dedicated hardware, which was a goal of the project.
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In theory, classic RNNs can keep track of arbitrary long-term dependencies in the input sequences. The problem with classic RNNs is computational (or practical) in nature: when training a classic RNN using back-propagation, the long-term gradients which are back-propagated can "vanish", meaning they can tend to zero due to very small numbers creeping into the computations, causing the model to ...
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This was optimized into the transformer architecture, published by Google researchers in Attention Is All You Need (2017). [27] That development led to the emergence of large language models such as BERT (2018) [ 28 ] which was a pre-trained transformer (PT) but not designed to be generative (BERT was an " encoder-only " model).