OpenSQZ/MiniCPM-V-CookBook

What it solves

This project provides a comprehensive set of implementation guides and deployment recipes for the MiniCPM series of models (MiniCPM-V, MiniCPM-o, and MiniCPM LLM). It simplifies the process of integrating multimodal AI capabilities—including vision, speech, and text—into applications, ranging from local edge devices like iPhones to high-throughput enterprise GPU clusters.

How it works

The cookbook acts as a centralized hub of "recipes" (tutorials and configuration guides) that cover the entire model lifecycle:

• Inference: Ready-to-run examples for tasks like single/multi-image QA, video understanding, PDF parsing, OCR, and speech-to-text/text-to-speech.
• Deployment: Instructions for various frameworks including vLLM, SGLang, llama.cpp, Ollama, and MLX for Apple Silicon.
• Fine-tuning: Guides for customizing models using Transformers, LLaMA-Factory, SWIFT, and Align-anything.
• Quantization: Methods to compress models using GGUF, BNB, AWQ, and GPTQ to reduce resource consumption.

Who it’s for

• Individuals: Users wanting to run multimodal models locally with minimal setup via Ollama or llama.cpp.
• Enterprises: Developers needing scalable, high-throughput performance using vLLM or SGLang.
• Researchers: AI practitioners looking to fine-tune and experiment with the model architecture using advanced training frameworks.

Highlights

• Omni-modal support: Covers text, vision, and audio/speech capabilities.
• Edge-ready: Specific guides for deploying on iOS, Android, and HarmonyOS NEXT.
• Versatile quantization: Supports multiple formats (GGUF, AWQ, etc.) for efficient local execution.
• Broad framework compatibility: Integrates with popular tools like FastAPI, Gradio, and Open WebUI for interactive demos.