Document the core architecture and component interactions of the uzu inference engine.
npx skills add https://github.com/trymirai/uzu --skill architecture-trymirai
Please help me install this Agent Skill. Skill: architecture Source: https://github.com/trymirai/uzu/tree/main/agents/skills/architecture Command: npx skills add https://github.com/trymirai/uzu --skill architecture-trymirai
Document the core architecture and component interactions of the uzu inference engine.
Review the Architecture section to align your changes with the architecture principles and performance goals.
High-intent search queries and answers about installing and using this skill.
The core architecture of the uzu inference engine consists of the Session, Generator, Encodables, and Kernels. These components collaborate to deliver high-performance inference across Apple Silicon backends.
The Session and Generator components collaborate with Encodables and Kernels to manage and execute inference tasks. This interaction is designed to ensure high-performance execution and scalability across Apple Silicon backends.
You align development contributions by reviewing the architecture principles governing the Session, Generator, Encodables, and Kernels. This enforces consistency and performance requirements for backend integration.
Yes, the inference engine architecture supports Apple Silicon backends. The design emphasizes optimization and scalability to deliver high-performance inference specifically across this hardware.
Kernel optimization is important because Kernels collaborate directly with the Session and Generator to execute high-performance inference. The architecture enforces strict performance and consistency requirements for development and debugging.
OpenLAIRRun CPU-based LLM inference with GGUF quantization using C/C++.
codes1gnEnforce a finite state machine loop for NVIDIA GPU kernel tuning.
pedestrianloveManage tensor parallelism, inference optimizations, and mixed-precision support for Megatron-LM.
manutejEvaluate prompt strategies on MATH, GSM8K, and Game of 24 benchmarks.
