The Best Semantic Cache Router Alternatives in 2026

A JavaScript Native PyTorch-aligned Machine Learning Framework, built from scratch on WebGPU.

This repository contains utilities intended for use with Llama models.

PromptSite provides prompt version management for tracking, experimentation, and debugging LLM prompts.

DeepEval is an LLM evaluation framework, according to its repository description.

OneCompression is a Python package for compressing large language models.

Notte provides cloud browser infrastructure and web automation for AI and coding agents.

QKeras provides quantization tools for deep learning with TensorFlow Keras.

Adaptive Reliability Layer decides when to retrain a model based on drift signals and delayed labels.

The linfa project provides a machine learning framework written in Rust.

PaddlePaddle provides a deep learning framework for training models across devices and platforms.

FATE supports federated learning through an industrial grade framework.

MLX provides an array computing framework designed for Apple silicon devices.

Mosec serves machine learning models with dynamic batching and CPU or GPU pipelines for inference workloads.

The fuku-ml project provides a machine learning library described as simple to use.

List of awesome compiler projects and papers for tensor computation and deep learning.

ClearML combines experiment and data management, pipelines, scheduling, orchestration, and model serving for AI workloads.

Kedro helps create reproducible, maintainable, and modular data science and data engineering pipelines.

Sinabs supports fast training of spiking neural networks with PyTorch and inference on neuromorphic hardware.

ClearML manages AI experiments, data, pipelines, orchestration, scheduling, and model serving in one MLOps platform.

Tensors and Dynamic neural networks in Python with strong GPU acceleration.

PyTorch TNT supplies lightweight tools and utilities for training with PyTorch.

PyTorch Ignite provides a high level library for flexibly training and evaluating neural networks.

Maze provides an applied reinforcement learning framework for developing projects in Python.

BentoML helps build and serve AI apps and models through inference APIs, job queues, and multi-model pipelines.