Feb 1, 2024 · With tool calling For tool / function calling, SGLang is the recommended backend — MiniCPM5-1B emits XML-style tool calls and SGLang's built-in minicpm5 parser converts them to …

The main goal of llama.cpp is to enable LLM inference with minimal setup and state-of-the-art performance on a wide range of hardware - locally and in the cloud.

MiniCPM5-1B is a language model that generates content based on learned statistical patterns from training data. It may produce inaccurate, biased, or unsafe outputs, and generated content should be …

May 26, 2026 · It is designed for local assistants, coding agents, tool-use workflows, and reasoning scenarios where a compact model is preferred. The model keeps a small deployment footprint while …

In this tutorial, you will learn how to use local LLMs via Tool Calling with Mathematical, story, Python code and terminal function examples. Inference is done locally via llama.cpp, llama-server and …

Jul 3, 2026 · This document covers running quantized MiniCPM models using llama.cpp for efficient CPU, edge, and consumer-GPU deployment. llama.cpp provides a lightweight inference engine that …

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