The goal of this feature is to develop a highly efficient, pure-Go implementation for inference using Microsoft's BitNet b1.58‑2B 4T language model, optimized specifically for CPU environments, with potential future support for GPU acceleration. This implementation will handle language model inference with a context length of up to 4096 tokens, enabling practical text-generation and completion tasks. Leveraging BitNet's 2-bit ternary quantization, it aims to achieve exceptionally low memory usage and high throughput by extensively using Go's native bitwise operations and scalable goroutine-based concurrency across multiple CPU cores. The resulting inference engine will be lightweight, scalable, and suitable for both edge and cloud environments.
This roadmap outlines a sequence of small, sequential tasks to implement Microsoft’s BitNet b1.58‑2B 4T model in pure Go (inference-only). The implementation aims to support a 4096-token context and leverage goroutine-based concurrency to utilize multiple CPU cores.
The goal of this feature is to develop a highly efficient, pure-Go implementation for inference using Microsoft's BitNet b1.58‑2B 4T language model, optimized specifically for CPU environments, with potential future support for GPU acceleration. This implementation will handle language model inference with a context length of up to 4096 tokens, enabling practical text-generation and completion tasks. Leveraging BitNet's 2-bit ternary quantization, it aims to achieve exceptionally low memory usage and high throughput by extensively using Go's native bitwise operations and scalable goroutine-based concurrency across multiple CPU cores. The resulting inference engine will be lightweight, scalable, and suitable for both edge and cloud environments.
This roadmap outlines a sequence of small, sequential tasks to implement Microsoft’s BitNet b1.58‑2B 4T model in pure Go (inference-only). The implementation aims to support a 4096-token context and leverage goroutine-based concurrency to utilize multiple CPU cores.
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