In this tutorial, we implement how to run the Bonsai 1-bit large language model efficiently using GPU acceleration and PrismML’s optimized GGUF deployment stack. We set up the environment, install the required dependencies, and download the prebuilt llama.cpp binaries, and load the Bonsai-1.7B model for fast inference on CUDA. As we progress, we examine how 1-bit quantization works under the hood, why the Q1_0_g128 format is so memory-efficient, and how this makes Bonsai practical for lightweight yet capable language model deployment. We also test core inference, benchmarking, multi-turn chat, structured JSON generation, code generation, OpenAI-compatible server mode, and a small retrieval-augmented generation workflow, giving us a complete, hands-on view of how Bonsai operates in real-world use.
├── opencode.json # OpenCode服务器配置
Владислав Китов (редактор отдела Мир)
generator issue instead of fixing it."