The Gist Talk

• Jamba-1.5: Hybrid Transformer-Mamba Models at Scale

  This episode introduces Jamba-1.5, a new series of instruction-tuned large language models built on the Jamba hybrid Transformer-Mamba mixture-of-experts architecture. These models, available in Large (94B active parameters) and Mini (12B active parameters) sizes, are highlighted for their high efficiency, superior throughput, and remarkably low memory usage over long context lengths, up to 256K tokens. A key technical innovation is ExpertsInt8, a novel quantization technique enabling the large model to run efficiently on standard GPU hardware without compromising quality. Evaluations consistently show that Jamba-1.5 models achieve competitive performance on academic and chatbot benchmarks while excelling in long-context tasks compared to other similarly sized open-weight models. The authors also share insights into the model's training stages, multilingual capabilities, and alignment safety considerations

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• Google's Titans+Miras: Learning to Memorize at Test Time

  Over more than a decade there has been an extensive research effort of how effectively utilize recurrent models andattentions. While recurrent models aim to compress the data into a fixed-size memory (called hidden state), attention allowsattending to the entire context window, capturing the direct dependencies of all tokens. This more accurate modelingof dependencies, however, comes with a quadratic cost, limiting the model to a fixed-length context. We present a newneural long-term memory module that learns to memorize historical context and helps an attention to attend to thecurrent context while utilizing long past information. We show that this neural memory has the advantage of a fastparallelizable training while maintaining a fast inference. From a memory perspective, we argue that attention due to itslimited context but accurate dependency modeling performs as a short-term memory, while neural memory due to itsability to memorize the data, acts as a long-term, more persistent, memory. Based on these two modules, we introducea new family of architectures, called Titans, and present three variants to address how one can effectively incorporatememory into this architecture. Our experimental results on language modeling, common-sense reasoning, genomics,and time series tasks show that Titans are more effective than Transformers and recent modern linear recurrent models.They further can effectively scale to larger than 2M context window size with higher accuracy in needle-in-haystack taskscompared to baselines

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• LLM Architectures: Attention, Mamba, and Efficiency Tradeoffs

  This episode examines the architecture and efficiency of Large Language Models (LLMs), focusing heavily on optimizing the attention mechanism and exploring alternatives like State Space Models (SSMs). Several papers introduce and analyze methods to overcome the quadratic complexity of standard self-attention, including Grouped-Query Attention (GQA), Sliding Window Attention (SWA), and the hardware-aware optimizations of FlashAttention. A significant portion of the research centers on Mamba-based models and hybrid architectures that combine SSMs with attention layers, demonstrating that these hybrids, such as the Mamba-2-Hybrid, can achieve better performance on memory recall and long-context tasks than pure Transformers while maintaining efficiency. Finally, one source investigates the internal reasoning of attention mechanisms, proposing that a "preplan-and-anchor" rhythm can be identified and leveraged to create more effective reinforcement learning strategies for fine-grained policy optimization

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• Grouped-Query Attention: Speed and Quality Through Uptraining

  The source presents a technical paper addressing the significant memory bandwidth overhead that slows down autoregressive decoder inference in large Transformer models. This work offers two core solutions: first, a method called uptraining allows existing high-quality multi-head attention (MHA) checkpoints to be converted into faster models using only a small percentage of their original training compute. Second, the authors introduce grouped-query attention (GQA), which serves as a generalization and quality-preserving intermediate step between MHA and the faster but less stable multi-query attention (MQA). GQA operates by dividing query heads into small groups, each sharing a single key and value head derived through mean pooling the original heads. Experimental results confirm that these uptrained GQA models achieve performance comparable to MHA while delivering inference speeds nearly as fast as MQA, successfully balancing quality and computational efficiency

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• Cross-Layer Attention for KV Cache Optimization

  The research introduces Cross-Layer Attention (CLA) as a novel architectural modification designed to mitigate the substantial memory overhead associated with the Key-Value (KV) cache during the decoding phase of large language models (LLMs). Unlike established methods such as Multi-Query Attention (MQA) and Grouped-Query Attention (GQA), which reduce the cache size by sharing heads within a layer, CLA achieves memory savings by sharing key and value activations across adjacent layers. Extensive experiments conducted on 1B- and 3B-parameter models show that combining CLA with MQA achieves a 2× reduction in KV cache size with minimal impact on accuracy metrics like perplexity. The authors argue that this new technique provides a significant improvement on the accuracy/memory Pareto frontier compared to existing transformer designs. By making LLM serving more memory-efficient, CLA promises to enable practitioners to use models supporting both longer sequence lengths and larger batch sizes

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