Mamba LLM Architecture

From GM-RKB
(Redirected from Mamba architecture)
Jump to navigation Jump to search

A Mamba LLM Architecture is a large language model architecture that optimizes sequence processing efficiency through a state-space model (SSM) approach, allowing linear-time complexity in data handling which contrasts with the quadratic-time complexity seen in traditional Transformers.



References

2024

  • (DataCamp, 2024) ⇒ "An Introduction to the Mamba LLM Architecture: A New Paradigm in Machine Learning." In: DataCamp.
    • NOTE: Discusses the Mamba architecture's use of state-space models to enhance efficiency in processing long sequences, contrasting it with traditional Transformer architectures.

2024

  • (GitHub - state-spaces/mamba) ⇒ "Mamba: A new state space model architecture for LLMs." Available online at: [GitHub - state-spaces/mamba](https://github.com/state-spaces/mamba).
    • NOTE: Provides implementation details of the Mamba architecture, including its hardware-aware design and optimizations for specific computational environments.

2024

  • (Krohn, 2024) ⇒ Jon Krohn. (2024). “The Mamba Architecture: Superior to Transformers in LLMs." In: Jon Krohn's Blog, February 16, 2024. Available online at: [Jon Krohn - The Mamba Architecture](https://www.jonkrohn.com).
    • NOTE: Explores the benefits of the Mamba architecture over Transformers, particularly in its ability to process long sequences more efficiently due to its linear-time complexity.

2024

  • (Wikipedia) ⇒ "Mamba (deep learning architecture)." In: Wikipedia. Available online at: [Wikipedia - Mamba Architecture](https://en.wikipedia.org/wiki/Mamba_(deep_learning_architecture)).
    • NOTE: Offers a general overview of the Mamba architecture, highlighting its approach to handling long sequences and its potential to simplify the preprocessing steps in language modeling.