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4 repositorios

Awesome GitHub RepositoriesPure Function Model Execution

Execution patterns where model outputs are derived from state and inputs through stateless functions.

Distinct from Functional Transformations: Focuses on the application of pure functions to neural network forward passes for JIT and autograd compatibility.

Explore 4 awesome GitHub repositories matching artificial intelligence & ml · Pure Function Model Execution. Refine with filters or upvote what's useful.

Awesome Pure Function Model Execution GitHub Repositories

Encuentra los mejores repositorios con IA.Buscaremos los repositorios que mejor coincidan usando IA.
  • google/flaxAvatar de google

    google/flax

    7,238Ver en GitHub↗

    Flax is a deep learning framework and JAX neural network library designed for building complex machine learning models. It functions as a distributed training library and model state manager, providing a toolkit for defining flexible neural network architectures and scaling their training across multiple hardware devices. The project is characterized by a design that separates network logic from parameter values to remain compatible with pure functions. It uses hierarchical module composition to organize networks as trees of nested modules and employs a reference-based state management system

    Transforms model state and inputs into outputs through stateless functions to enable JAX transformations.

    Jupyter Notebook
    Ver en GitHub↗7,238
  • crytic/slitherAvatar de crytic

    crytic/slither

    6,141Ver en GitHub↗

    Trail of Bits identifies functions declared as constant/pure/view that change the state, potentially trapping contracts compiled with Solidity 0.5.

    Pythonethereumsoliditystatic-analysis
    Ver en GitHub↗6,141
  • sanchit-gandhi/whisper-jaxAvatar de sanchit-gandhi

    sanchit-gandhi/whisper-jax

    4,687Ver en GitHub↗

    whisper-jax es una implementación de alto rendimiento del modelo de reconocimiento automático de voz Whisper, reescrita utilizando el framework JAX. Está diseñada para una inferencia acelerada y utiliza la compilación XLA para optimizar la ejecución del modelo en aceleradores de hardware. El proyecto se centra en la transcripción optimizada para TPU para lograr un alto rendimiento y velocidad. Incluye un pipeline de traducción de pesos que convierte los parámetros del modelo preentrenado de PyTorch en arrays compatibles con JAX. El sistema admite la transcripción de audio a texto, la traducción de voz en varios idiomas y la generación de marcas de tiempo de audio. Permite el procesamiento de audio por lotes y escala el rendimiento mediante el procesamiento por lotes paralelo a los datos y la partición de tensores paralela al modelo. El proyecto proporciona un método para desplegar el modelo de transcripción como un endpoint de inferencia remoto con una interfaz web.

    Employs stateless functions for model execution to ensure compatibility with JIT compilation and automatic differentiation.

    Jupyter Notebookdeep-learningjaxspeech-recognition
    Ver en GitHub↗4,687
  • googleapis/python-genaiAvatar de googleapis

    googleapis/python-genai

    3,819Ver en GitHub↗

    This project is a Python software development kit and framework for building applications that integrate with large language models. It serves as a multimodal content generator and vector embedding library, enabling the production and editing of text, images, audio, and video. The toolkit provides specialized capabilities for adapting base models through supervised and reinforcement training. It further distinguishes itself by offering tools for orchestrating complex workflows, including stateful chat sessions, the enforcement of structured output via schemas, and the integration of external

    Connects a model to custom functions or external tools to request and receive real-world data.

    Python
    Ver en GitHub↗3,819
  1. Home
  2. Artificial Intelligence & ML
  3. Pure Function Model Execution

Explorar subetiquetas

  • Tool CallingMechanisms allowing models to request the execution of external functions to retrieve real-world data or perform actions. **Distinct from Pure Function Model Execution:** Distinct from Pure Function Model Execution: focuses on external tool integration and function calling rather than internal stateless model execution patterns.
  • View/Pure Function Mislabel DetectorsIdentifies functions declared as constant/pure/view that use assembly code, which may trap contracts compiled with Solidity 0.5. **Distinct from Pure Function Model Execution:** Distinct from Pure Function Model Execution: focuses on detecting mislabeled view/pure functions in Solidity, not model execution patterns.