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5 dépôts

Awesome GitHub RepositoriesHot-Swappable Providers

Switches between different AI model providers at runtime by changing a single configuration parameter.

Distinct from AI Provider Interfaces: Distinct from AI Provider Interfaces: focuses on runtime hot-swapping between providers rather than the registration interface itself.

Explore 5 awesome GitHub repositories matching artificial intelligence & ml · Hot-Swappable Providers. Refine with filters or upvote what's useful.

Awesome Hot-Swappable Providers GitHub Repositories

Trouvez les meilleurs dépôts grâce à l'IA.Nous recherchons les dépôts les plus pertinents grâce à l'IA.
  • nesquena/hermes-webuiAvatar de nesquena

    nesquena/hermes-webui

    14,912Voir sur GitHub↗

    Hermes-webui is a self-hosted AI orchestrator and web interface for managing autonomous agents. It serves as a multi-provider gateway that connects cloud and local large language models, providing a central hub to execute scheduled background jobs, run shell commands, and manage agent memory on private hardware. The system distinguishes itself through a persistent memory manager that utilizes knowledge graphs and markdown files for long-term context across sessions. It features a model context protocol host for extending agent capabilities with standardized tools and supports the orchestratio

    Allows switching between different AI model providers at runtime via configuration without losing state.

    Pythonagentai-agentshermes
    Voir sur GitHub↗14,912
  • apple/ml-fastvlmAvatar de apple

    apple/ml-fastvlm

    7,375Voir sur GitHub↗

    This project is a vision language model framework and vision-to-text pipeline designed for deploying and optimizing models that process both images and text. It provides an on-device inference engine and a vision language model framework to run quantized models locally on mobile and desktop hardware accelerators. The framework features a model quantization toolkit to reduce weight precision for lower memory footprints and increased execution speed on specialized silicon. It also includes an efficient vision encoder utilizing a hybrid encoding system to compress image tokens, which reduces pro

    Allows switching neural network weight sets at runtime by loading specific checkpoints to optimize for local hardware.

    Python
    Voir sur GitHub↗7,375
  • getstream/vision-agentsAvatar de GetStream

    GetStream/Vision-Agents

    6,029Voir sur GitHub↗

    Enables hot-swapping between different realtime AI models with a single configuration change.

    Pythonagentic-aiagentsai
    Voir sur GitHub↗6,029
  • jeeliz/jeelizfacefilterAvatar de jeeliz

    jeeliz/jeelizFaceFilter

    2,883Voir sur GitHub↗

    jeelizFaceFilter is a browser-based computer vision engine and WebGL face tracking library designed for AR filters and real-time facial movement tracking. It functions as a neural network face detector that identifies multiple faces and monitors mouth movements and rotation within a web browser. The system distinguishes itself through a model-swappable detection pipeline, allowing the exchange of neural network weights to balance accuracy and performance across different camera angles and devices. It features real-time lighting synchronization to match the illumination of 3D overlays with the

    Allows switching between different neural network weights to optimize for specific camera angles or device performance.

    JavaScript3daugmented-realitycamera
    Voir sur GitHub↗2,883
  • xusenlinzy/api-for-open-llmAvatar de xusenlinzy

    xusenlinzy/api-for-open-llm

    2,460Voir sur GitHub↗

    This project provides a unified server environment and gateway for hosting and executing open-source large language models on private infrastructure. It functions as a standardized interface that exposes locally deployed models through widely-adopted API protocols, allowing existing applications to interact with them without requiring code modifications. The platform distinguishes itself by acting as a compatibility layer that translates standard REST requests into model-specific execution calls. It supports advanced interaction patterns including real-time token streaming, function calling f

    Allows hot-swapping of model weights by mapping local directories into the runtime environment.

    Pythonbaichuanchatglmcode-llama
    Voir sur GitHub↗2,460
  1. Home
  2. Artificial Intelligence & ML
  3. Model Capability Extensions
  4. AI Provider Interfaces
  5. Hot-Swappable Providers

Explorer les sous-tags

  • Model Weight SwappingSwitching neural network weight sets at runtime to optimize for specific environments or devices. **Distinct from Hot-Swappable Providers:** Focuses on swapping specific model weights rather than switching entire AI service providers.