2 Repos
Awesome GitHub RepositoriesMacro-Driven Type Computations
Using type macros to define recursive type-level operations like appending heterogeneous lists without manual trait boilerplate.
Distinct from Recursive Type Processing: Distinct from Recursive Type Processing: uses macros for type-level computation rather than runtime recursive type aliases.
Explore 2 awesome GitHub repositories matching software engineering & architecture · Macro-Driven Type Computations. Refine with filters or upvote what's useful.
Awesome Macro-Driven Type Computations GitHub Repositories
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- rust-lang/rfcs
rust-lang/rfcs
6,406Auf GitHub ansehenThe Rust RFCs repository is the formal home for the Rust language evolution process, housing the structured design documents and community review mechanisms that govern changes to the Rust programming language, its compiler, and its standard library. It defines the complete lifecycle for proposing, discussing, and implementing substantial changes through RFC documents, from initial submission and community feedback through final comment periods and sub-team sign-offs. The repository codifies the governance and collaboration processes that shape Rust's development, including mechanisms for com
Uses type macros to define recursive type-level operations like appending heterogeneous lists without manual trait boilerplate.
Markdownrfcrfc-processrust - opennmt/ctranslate2
OpenNMT/CTranslate2
4,319Auf GitHub ansehenCTranslate2 is a C++ inference engine and runtime for Transformer models, designed to execute models on both CPU and GPU with optimizations for speed and memory efficiency. It functions as a model format converter, quantization tool, and REST API server, enabling deployment of neural machine translation, automatic speech recognition, and text generation models. The engine distinguishes itself through a suite of runtime optimizations including layer fusion, weight-matrix quantization, batch-by-length grouping, and a caching allocator that reuses GPU memory. It supports tensor-parallel model di
Automatically selects the fastest supported quantization type for the current hardware and backend when loading a model.
C++avxavx2cpp
