💬 chat

MNN-LLM Android App

A lightweight multi-modal language model Android application.

#Artificial Intelligence
#deep learning
#Privacy protection
#multimodal
#mobile application
MNN-LLM Android App

Product Details

MNN-LLM is an efficient inference framework designed to optimize and accelerate the deployment of large language models on mobile devices and local PCs. It solves the problem of high memory consumption and computational cost through model quantization, hybrid storage and hardware-specific optimization. MNN-LLM performs well in CPU benchmarks and is significantly faster, making it suitable for users who require privacy protection and efficient inference.

Main Features

1
Multi-modal support: Supports a variety of tasks such as text, image and audio generation.
2
CPU inference optimization: Achieve significant speed improvements on Android devices.
3
Broad model compatibility: Supports multiple mainstream model providers.
4
Privacy First: Runs entirely on-device, ensuring data privacy.
5
History: You can browse and review previous chat records.
6
Custom settings: Users can customize the sampler type and other parameters.
7
Support multi-turn dialogue: Enhanced interactive capabilities of multi-modal models.
8
User-friendly interface: The chat interface is optimized and supports multi-line input.

How to Use

1
Download the app and install it.
2
Open the app and browse supported models.
3
Select the model you want to use and download it.
4
Interact with the model and enter questions or instructions.
5
View chat history and continue previous conversations.

Target Users

This product is suitable for users who have high requirements for data privacy, such as developers, AI enthusiasts, and creators who need to use multi-modal generation tools. It provides a safe and efficient way to use large language models without worrying about data leakage.

Examples

Text-to-text generation tasks using MNN-LLM.

Real-time audio conversion on Android devices.

Generate textual descriptions related to images through the application.

Quick Access

Visit Website →

Categories

💬 chat
› Development and Tools
› Model training and deployment

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