From Transformers to Jamba: How Hybrid Architectures Solve the Long-Context Problem

This video provides a comprehensive overview of the evolution of large language model architectures, focusing on the shift from the traditional, computationally expensive Transformer model to the more efficient Mamba model. They explain that Transformers suffer from quadratic complexity in memory and computation for long sequences due to their attention mechanism, while Mamba, derived from Selective State Space Models (SSMs), offers linear complexity and constant memory usage during inference. The texts highlight the Jamba model, developed by AI21, as a cutting-edge hybrid architecture that strategically interleaves Mamba and Transformer layers to capture the efficiency of Mamba and the strong in-context learning capabilities of Attention, enabling it to process extraordinarily long contexts of up to 256K tokens. Finally, one source acts as a quick guide for a DeepLearning.AI course on Jamba, offering practical tips for accessing files and efficient learning.

For Step by Step learning read the blog https://medium.com/state-of-the-art-technology/from-transformers-to-jamba-how-hybrid-architectures-solve-the-long-context-problem-part-i-cd694677e9f1

Видео From Transformers to Jamba: How Hybrid Architectures Solve the Long-Context Problem канала AI, Career Growth and Life Hacks