Edge 437: Inside BlackMamba, One of the Most Important SSM Models Ever Created
The model combines SSMs, MoEs in a single architecture.
In this issue:
An introduction BlackMamba that combines MoEs and SSMs in a single architecture.
A review of the BlackMamba paper.
An overview of Princeton’s University SWE-Bench for software engineering task.
A small self-serving note before we start 😉:
For the past year, I’ve been working on several ideas in AI evaluation and benchmarking—an area that, as many of you know, presents a massive challenge in today’s AI landscape. After experimenting with various approaches, I decided to incubate LayerLens, a new AI company focused on streamlining the evaluation and benchmarking of foundation models. This marks my third venture-backed AI project in the last 18 months. We've assembled a phenomenal team, with experience at companies like Google, Microsoft, and Cisco, as well as top universities. We’ve also raised a sizable pre-seed round. More details about that in the next few weeks.
We are currently hiring across the board, particularly for roles in AI research and engineering with a focus on benchmarking and evaluation. If you’re interested in this space and looking for a new challenge, feel free to reach out to me at jr@layerlens.ai. I look forward to hearing from some of you!
💡 ML Concept of the Day: BlackMamba Combines MoEs and SSMs in a Single Architecture
The combination of mixture-of-experts(MoEs) and transformers in an incredible popular architecture choice in generative AI. Can we do the same with state space models(SSMs) ?
BlackMamba introduces a new approach to processing long sequences and handling diverse AI tasks. It merges the strengths of SSMs MoE frameworks. Specifically, BlackMamba builds on the Mamba SSM, known for its efficiency in managing long sequences, and incorporates MoE strategies to streamline performance while addressing scalability.