Awesome GitHub RepositoriesModel Initialization Utilities

This project is a comprehensive platform for hosting and interacting with large language models directly on local hardware. It provides a web-based graphical interface that allows users to manage model loading, configure generation parameters, and execute text or chat interactions entirely offline. By running models locally, the software ensures complete data privacy and eliminates reliance on external cloud services for generative tasks. Beyond basic inference, the platform functions as a versatile workbench for generative AI development. It includes an integrated pipeline for fine-tuning mo

This is a configuration library for JVM applications that parses HOCON, JSON, and Java properties files into an immutable tree structure. It resolves ${...} placeholders by traversing the configuration tree and falling back to environment variables and system properties, and validates loaded configurations against a reference schema. The library loads configuration from classpath resources, files, URLs, system properties, and environment variables, merging them with priority-based override semantics. It provides typed value access with automatic type coercion, supports dot-path navigation,

Anomalib is a PyTorch-based library for visual anomaly detection, offering a modular framework, a comprehensive model zoo, and a benchmarking suite designed for industrial defect detection. It provides a wide range of algorithms—including generative, discriminative, teacher-student, and vision-language approaches—that support unsupervised, few-shot, and zero-shot settings. The library enables deployment through model export to ONNX and OpenVINO for edge devices, and includes a no-code web application for training and inference. It also features a command-line interface for orchestrating multi

LiteRT-LM este un framework de inferență de înaltă performanță conceput pentru a executa modele de limbaj mari local pe hardware mobil, desktop și IoT. Acesta servește ca un runtime de model on-device care utilizează accelerarea CPU, GPU și NPU pentru a oferi procesare cu latență scăzută. Framework-ul se distinge prin capacitatea sa de a procesa intrări de text, viziune și audio printr-un singur motor de inferență multimodal. Dispune de un server HTTP local care emulează endpoint-uri API compatibile cu OpenAI și un runtime bazat pe WebGPU pentru executarea modelelor direct într-un browser web. Pentru a asigura fiabilitatea output-ului, include un generator de text constrâns care impune scheme JSON sau reguli gramaticale asupra răspunsurilor modelului. Proiectul oferă capabilități largi pentru gestionarea conversațiilor stateful, decodare speculativă pentru viteze crescute de generare a token-urilor și o interfață de apelare a instrumentelor (tool-calling) care mapează cererile modelului către funcții externe. Include, de asemenea, integrare specializată pentru ecosistemul Apple și un plugin dedicat pentru rularea modelelor în Flutter. Utilizatorii pot executa modele printr-o interfață în linie de comandă (CLI) sau le pot integra în aplicații prin API-uri native.

PyTorch Forecasting is a deep learning framework designed for building and training neural network architectures specifically for time series forecasting. It serves as a comprehensive toolkit for implementing autoregressive models, multi-horizon forecasting, and probabilistic prediction intervals using PyTorch tensors. The library distinguishes itself through a probabilistic forecasting toolkit that generates prediction intervals and quantile forecasts using both parametric and non-parametric distributions. It further provides a neural network model optimizer for automated hyperparameter tuni

picoGPT is a lightweight, low-level runtime environment and inference engine designed to load pre-trained checkpoints and execute generative transformer model inference. It provides a minimal implementation of the generative pre-trained transformer architecture to facilitate local language model execution. The project includes a C++ machine learning library for converting model parameters and executing greedy token generation without heavy external dependencies. It handles remote asset synchronization by downloading pre-trained weights, hyperparameters, and vocabulary files from remote server

TransformerLens is a library for mechanistic interpretability research designed to reverse engineer the learned algorithms within large language models. It provides a standardized framework for wrapping diverse transformer architectures, allowing researchers to extract, manipulate, and analyze internal activations and weights through a consistent interface. The project distinguishes itself through a comprehensive system of activation hooks that can capture, patch, and ablate internal tensors during the forward pass. It includes specialized utilities for decomposing fused projections, material