approaches have been explored to address this issue, including Grouped-Query Attention (GQA) (Ainslie et al., 2023) and Multi-Query Attention (MQA) (Shazeer, 2019). However, these methods often compromise limit the inference efficiency. In order to reduce the KV cache, Multi-Query Atten- tion (MQA) (Shazeer, 2019) and Grouped-Query Attention (GQA) (Ainslie et al., 2023) are proposed. They require a smaller respectively: q? = ??h?, (1) k? = ? ?h?, (2) v? = ??h?, (3) 6 Grouped-Query Attention (GQA) Multi-Head Attention (MHA) Multi-Query Attention (MQA) Multi-Head Latent Attention (MLA) Keys Queries Values

original optimizer in a LossScale0ptimizer . loss_scale_optimizer = LossScaleOptimizer(opt，1oss_scale_manager) # Call minimize() on the loss scale optimizer. train_op = loss_scale_optimizer.minimize(1oss) PLATFORM INT8 Inference on PAI- 引FTe[= PAI-Blade Model Analysis Graph optimization Blade Graph Optimizer TensorRT Customized OptimizeT TAO Compiler (XLA) cuUBLAS/VcuDNNVCUTL， Blade Kernel Lib S，

Param Frontends Operators Algorithm &Schedule CUDA TOPI Backends Machine Learning Automated Optimizer Schedule explorer Cost model Mali TOPI ROCM TOPI PVRTOPI Alibaba Al.Labs 阿里巴巴人工智能实验室 PVR TOPI

optimization potential benefit: 1.5x speedup Engineering intensiveMachine Learning based Program Optimizer TVM: Learning-based Learning System High-level data flow graph and optimizations Directly generate

to computing, calculating or counting patents. Google patents data changes somewhat between each query so numbers are rounded and should be viewed as directionally accurate. Source: USA Patent & Trademark and video into a shared representation and generate outputs in any of those formats. A single query can reference a paragraph and a diagram, and the model can respond with a spoken summary or an annotated structured report draft; and an analyst can combine charts, transcripts, and audio clips in a single query. Compared with text-only models, multimodal systems cut context switching, capture richer detail

Examples Data Enable agents to retrieve context and information necessary for executing the workflow. Query transaction databases or systems like CRMs, read PDF documents, or search the web. Action Enable

specific aspects of the RAG system that impact what content was inserted into the prompt, including the query, chunk settings, chunk output, and other information. Once you feel the prompt is close to perfect