I’ve recently come across something called the Mamba Transformer and I’m curious to know more about it. Can anyone here shed some light on what it is and how it works? I’ve heard it’s related to tech or maybe a new product line? Any insights or experiences with the Mamba Transformer would be greatly appreciated!
Mamba Transformer is a natural language processing model developed by Meta (formerly Facebook) as a part of their AI research efforts. It is designed to handle various language tasks such as translation, summarization, and text generation. The Mamba Transformer is part of a series of models built upon the Transformer architecture, which has proven highly effective for many NLP applications due to its ability to process and generate human-like text.
I’ve read the paper. The S6 layers of Mamba have a memory that they modify with each new token. All state space modeling networks work this way, but S6 can control how each token is remembered (if at all), rather than just memorizing a compressed version of the entire input sequence. This means it can theoretically retain important information from a million tokens ago while only keeping short-term details as long as they’re needed.
Whether this works in practice for models of practical size remains to be seen, but even if it doesn’t, more sophisticated memory states will be developed. Intuitively, it makes sense for a system to accept input one token at a time and have an internal memory (instead of taking in the entire uncompressed sequence at once, as attention does), so I’m optimistic.
In my opinion, any fixed memory strategy would eventually deteriorate when compared to actual attention in lengthy circumstances. And correct me if I’m wrong, but for sequences shorter than the network’s hidden width (4096 for llama), real attention is actually more computationally efficient than Mamba.
Therefore, Mamba is limited to this context length range where it could be more effective.
Hi, Benjamin. Mamba is a unique deep-learning architecture that focuses on sequence modeling. It was developed by academics at Carnegie Mellon University and Princeton University to solve some of the limitations of transformer models, particularly when processing extended sequences.
Mamba continues to perform worse than transformer, and as transformers had little attention prior to bert, it is unlikely that ssms will surpass transformers until it has its own bert moment.
Furthermore, sub quadratic scaling with respect to length is no longer a selling point (not that it was anyhow). That problem is resolved by fa2, and as model size increases, attention cost becomes progressively marginal. In frontier models, attention cost is negligible in comparison to the matmuls even in the absence of fa.
A new state of the art space model architecture known as the Mamba it is comparable to the iconic Transformers. With an effective hardware-aware design and implementation in the spirit of Flash Attention, it is based on the line of advancement on structured state space models. Mixer layers are Mamba’s version of Attention layers.
Mamba is a deep learning architecture that focuses on sequence modelling. It was developed by academics at Carnegie Mellon University and Princeton University to solve some of the limitations of transformer models, particularly when processing extended sequences.