This readme introduces a Go repository named “go-attention” developed by Takara AI, focusing on providing an efficient and dependency-free implementation of attention mechanisms in Golang. It includes dot-product attention, multi-head attention support, full transformer layers with layer normalization, positionwise feedforward networks (FFNs), residual connections, batched operations for improved performance without external dependencies.
The repository aims to cater to various use cases such as edge computing due to zero dependency management requirements; real-time processing because of its pure Go implementation ensuring predictable performance; cloud-native applications with high throughput scaling capabilities in cloud environments; embedded systems owing to minimal memory allocations and efficient resource usage. Future improvements may include positional encoding implementations, dropout support, CUDA acceleration support, additional transformer variants, pretrained models, training utilities etc.
Contributions are welcome via pull requests while inquiries related to research or press can be directed towards [[email protected]](mailto:[email protected]). The project is licensed under the MIT License for further use and modification.\
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