awesome-repositories.com
Blog
MCP
awesome-repositories.com

Découvrez les meilleurs dépôts open-source grâce à notre recherche par IA.

ExplorerRecherches sélectionnéesAlternatives open sourceLogiciels auto-hébergésBlogPlan du site
ProjetServeur MCPÀ proposNotre méthodologiePresse
Mentions légalesConfidentialitéConditions d'utilisation
© 2026 Bringes Technology SRL·VAT RO45896025·hello@awesome-repositories.com
·
a-ghorbani avatar

a-ghorbani/pocketpal-ai

0
View on GitHub↗
5,719 stars·555 forks·TypeScript·mit·12 vues

Pocketpal Ai

PocketPal AI is an on-device LLM chat application for Android that runs small language models locally, enabling private AI conversations without requiring an internet connection. It functions as an offline inference engine that downloads and executes quantized language models directly on the device, with adjustable parameters like temperature and chat templates to control how the AI behaves.

The application lets users create custom AI personalities by configuring unique system prompts and contextual settings for different conversational roles. It integrates with the Hugging Face Hub to download and load both public and gated models, supporting authentication tokens for models that require special permissions. Users can download, load, and switch between multiple small language models from a built-in list or external hub, and benchmark model performance by measuring tokens per second and memory usage on the device.

Features

  • Offline Chat Clients - Runs small language models directly on the device for private conversations without needing an internet connection.
  • Custom AI Assistant Development - Building personalized AI personalities with custom system prompts and settings for different conversational roles.
  • Inference Parameters - Adjusts model parameters like system prompt, temperature, and chat templates to control how the AI behaves.
  • Local Chat Applications - An Android application that runs small language models locally for private AI conversations without internet connectivity.
  • Inference Configuration Parameters - Adjusts model behavior through configurable parameters like system prompt, temperature, and chat templates.
  • On-Device Inference Engines - Runs quantized language models on-device with adjustable temperature and chat template parameters.
  • On-Device Models - Runs small language models directly on the device using local inference without requiring an internet connection.
  • Personal AI Assistants - Builds custom AI personalities by setting unique system prompts and contextual settings for different conversation roles.
  • Model Downloaders - Downloads and loads models from the Hugging Face Hub, including gated models requiring authentication tokens.
  • Model Performance Benchmarking - Measures tokens-per-second and memory usage of loaded models to compare local AI performance.
  • Runtime Model Swapping - Supports downloading, loading, and switching between multiple small language models from a built-in list or external hub.
  • Model Downloaders - Downloads, loads, and switches between multiple small language models from a built-in list or the Hugging Face Hub.
  • Hugging Face Authenticators - Authenticates with a Hugging Face token to download and run models that require special permissions.

Historique des stars

Graphique de l'historique des stars pour a-ghorbani/pocketpal-aiGraphique de l'historique des stars pour a-ghorbani/pocketpal-ai

Recherche par IA

Explorez plus de dépôts awesome

Décrivez vos besoins en langage naturel — l'IA classe des milliers de projets open source sélectionnés par pertinence.

Start searching with AI

Alternatives open source à Pocketpal Ai

Projets open source similaires, classés selon le nombre de fonctionnalités partagées avec Pocketpal Ai.
  • mobile-artificial-intelligence/maidAvatar de Mobile-Artificial-Intelligence

    Mobile-Artificial-Intelligence/maid

    2,550Voir sur GitHub↗

    Maid is a mobile large language model chat client and local runner. It provides a unified interface for interacting with AI models via cloud APIs or by executing model files directly on mobile hardware for offline generation. The project functions as a multi-provider manager that handles API keys and a system for downloading and loading curated model files to the device. It utilizes a provider-agnostic interface to allow switching between local and remote backends. The application includes tools for organizing and exporting chat histories, synchronizing user data across multiple devices, and

    TypeScriptandroidanthropicchatbot
    Voir sur GitHub↗2,550
  • microsoft/onnxruntimeAvatar de microsoft

    microsoft/onnxruntime

    19,347Voir sur GitHub↗

    This project is a cross-platform machine learning inference engine designed to execute pre-trained models across diverse operating systems and hardware environments. It functions as a standardized execution framework that manages the entire lifecycle of model inference, from loading and graph optimization to hardware-accelerated execution and generative sequence management. The runtime distinguishes itself through a highly modular architecture that decouples model logic from hardware-specific kernels. By utilizing an execution provider abstraction, it enables developers to offload computation

    C++ai-frameworkdeep-learninghardware-acceleration
    Voir sur GitHub↗19,347
  • runanywhereai/runanywhere-sdksAvatar de RunanywhereAI

    RunanywhereAI/runanywhere-sdks

    8,781Voir sur GitHub↗

    This project is an on-device AI SDK providing a framework for running large language models, vision models, and speech models locally. It serves as an orchestration layer for local LLM execution, ensuring data privacy and offline availability by utilizing hardware acceleration on the device. The SDK is distinguished by its comprehensive voice and multimodal capabilities, including a coordinated voice pipeline for activity detection, speech-to-text, and text-to-speech synthesis. It also provides a dedicated implementation kit for local retrieval-augmented generation and tools for processing co

    C++androidapple-intelligencecpp
    Voir sur GitHub↗8,781
  • alibaba/mnnAvatar de alibaba

    alibaba/MNN

    14,242Voir sur GitHub↗

    MNN is a high-performance inference engine and framework designed for on-device machine learning. It provides a comprehensive environment for executing, optimizing, and deploying neural network models directly on mobile and resource-constrained edge devices. The framework distinguishes itself through a robust model optimization toolkit that supports quantization, compression, and structural graph manipulation to minimize memory footprint and maximize execution speed. It features a modular architecture that abstracts hardware-specific backends, allowing models to run efficiently across diverse

    C++armconvolutiondeep-learning
    Voir sur GitHub↗14,242
Voir les 30 alternatives à Pocketpal Ai→

Questions fréquentes

Que fait a-ghorbani/pocketpal-ai ?

PocketPal AI is an on-device LLM chat application for Android that runs small language models locally, enabling private AI conversations without requiring an internet connection. It functions as an offline inference engine that downloads and executes quantized language models directly on the device, with adjustable parameters like temperature and chat templates to control how the AI behaves.

Quelles sont les fonctionnalités principales de a-ghorbani/pocketpal-ai ?

Les fonctionnalités principales de a-ghorbani/pocketpal-ai sont : Offline Chat Clients, Custom AI Assistant Development, Inference Parameters, Local Chat Applications, Inference Configuration Parameters, On-Device Inference Engines, On-Device Models, Personal AI Assistants.

Quelles sont les alternatives open-source à a-ghorbani/pocketpal-ai ?

Les alternatives open-source à a-ghorbani/pocketpal-ai incluent : mobile-artificial-intelligence/maid — Maid is a mobile large language model chat client and local runner. It provides a unified interface for interacting… microsoft/onnxruntime — This project is a cross-platform machine learning inference engine designed to execute pre-trained models across… runanywhereai/runanywhere-sdks — This project is an on-device AI SDK providing a framework for running large language models, vision models, and speech… alibaba/mnn — MNN is a high-performance inference engine and framework designed for on-device machine learning. It provides a… codota/tabnine — TabNine is an AI-powered code completion engine that runs a deep-learning model to generate real-time code suggestions… pytorch/executorch — ExecuTorch is a lightweight C++ runtime for deploying PyTorch models on mobile, embedded, and edge hardware. It…