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