4 repository-uri
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
Găsește cele mai bune repo-uri cu AI.Vom căuta cele mai potrivite repository-uri folosind AI.
- google/flax
google/flax
7,238Vezi pe GitHubFlax 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 - crytic/slither
crytic/slither
6,141Vezi pe GitHubTrail of Bits identifies functions declared as constant/pure/view that change the state, potentially trapping contracts compiled with Solidity 0.5.
Pythonethereumsoliditystatic-analysis - sanchit-gandhi/whisper-jax
sanchit-gandhi/whisper-jax
4,687Vezi pe GitHubwhisper-jax este o implementare de înaltă performanță a modelului de recunoaștere automată a vorbirii Whisper, rescris folosind framework-ul JAX. Este conceput pentru inferență accelerată și utilizează compilarea XLA pentru a optimiza execuția modelului pe acceleratoare hardware. Proiectul se concentrează pe transcrierea optimizată pentru TPU pentru a obține un throughput și o viteză ridicate. Include un pipeline de traducere a ponderilor care convertește parametrii modelului pre-antrenat din PyTorch în array-uri compatibile cu JAX. Sistemul suportă transcrierea audio în text, traducerea vorbirii în mai multe limbi și generarea de timestamp-uri audio. Permite procesarea audio în loturi și scalează performanța prin batching paralel pe date și partiționarea tensorilor în paralel pe model. Proiectul oferă o metodă de a implementa modelul de transcriere ca endpoint de inferență la distanță cu o interfață web.
Employs stateless functions for model execution to ensure compatibility with JIT compilation and automatic differentiation.
Jupyter Notebookdeep-learningjaxspeech-recognition - googleapis/python-genai
googleapis/python-genai
3,819Vezi pe GitHubThis 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
