Analyze the performance of Mixtral 8x7B against Llama 2 and GPT-3.5 across various benchmarks, including commonsense reasoning, math, and code generation.

Discover the architectural details of Mixtral, a transformer-based language model that employs SMoE layers, supporting a dense context length of 32k tokens.

Discover Mixtral 8x7B, a Sparse Mixture of Experts (SMoE) language model, trained with a context size of 32k tokens with access to 47B parameters.

Transformers are limited by sequence length due to quadratic scaling. Explore solutions like sparse attention, low-rank approximations, and spectral methods.

Learn how new techniques make AI models faster, smarter, and more efficient by reducing memory use and speeding up training.

Learn how incorporating input-dependent parameters transforms state space models from time-invariant to time-varying.

Discover how SSMs improve sequence modeling by leveraging context compression, selectivity, and hardware-aware algorithms for efficient AI performance.

Explore state space models (SSMs), their structured architecture, and innovations like H3, Hyena, and RWKV that revolutionize AI sequence modeling efficiency.

Mamba, a new linear-time model, matches Transformer performance with 5× higher efficiency, excelling in language, audio, and genomics tasks.

Explore the simplified state space model (SSM) architecture that combines linear attention and MLP components into a unified block.

Mamba matches GPT-3 in language modeling, outperforming leading models without using traditional attention mechanisms, setting a new standard for efficiency.

Mamba’s memory system excels in solving sequence tasks, outperforming other models with its ability to remember key data and handle longer sequences.

New research shows real-valued SSMs outperform complex ones in discrete tasks. We explore initialization strategies and their impact on SSM efficiency.