37 repositorios
Tools for translating between native database types and standard array formats.
Distinct from Vector Data Formats: Distinct from Vector Data Formats: focuses on the conversion process rather than the format definition.
Explore 37 awesome GitHub repositories matching data & databases · Format Conversion Utilities. Refine with filters or upvote what's useful.
Vector similarity search extension for PostgreSQL.
Translates data between native database types and standard array formats for simplified importing and exporting.
Calibre-web is a self-hosted web application that provides a browser-based interface for browsing, managing, and reading digital book collections stored in a library database. It functions as a comprehensive library management system, allowing users to organize large collections, edit metadata, and perform automated content updates through a centralized administrative dashboard. The platform distinguishes itself by integrating directly with external infrastructure to extend the capabilities of a standard digital library. It supports remote storage mapping to host files on cloud providers, uti
Utilizes external binary tools to perform on-the-fly e-book format conversions for improved device compatibility.
DeDRM_tools is a software suite designed to automate the removal of digital rights management from personal e-book files. By stripping restrictive encryption layers during the import process, the utility enables users to manage their digital libraries and convert protected files into open formats for use across various reading devices. The system operates through a modular architecture that utilizes plugin-based event interception to hook into host applications. It performs cryptographic key extraction by scanning local configuration files and memory, applying algorithm-specific decryption to
Removes digital rights management from e-book files to enable format conversion and cross-device reading.
This tool is a command-line processor designed for querying, updating, and transforming structured data files. It functions as a versatile engine for manipulating YAML, JSON, TOML, and XML documents, allowing users to perform complex operations directly from the terminal. By utilizing a path-based expression language, it enables precise navigation and modification of data structures within configuration files and infrastructure-as-code workflows. What distinguishes this tool is its ability to perform in-place document mutations while preserving original formatting, comments, and metadata. It
The tool transforms structured data into property file format, supporting custom separators, array bracket notation, and automatic string encapsulation.
This project is a digital collection of academic material on deep learning provided as a machine learning educational resource. It delivers the complete textbook and individual chapters in portable document format for offline study and research. The repository includes electronic publication versions of the textbooks optimized for digital reading devices and e-book readers. It functions as a segmented document repository, providing the text both as a full volume and split into individual chapters to allow for targeted reading.
Provides converted electronic publication versions of the textbook for compatibility with e-book readers.
YOLOv10 is a PyTorch computer vision library and real-time vision framework designed for locating and identifying multiple objects in images and video streams. It functions as an end-to-end object detector that optimizes for high-speed deployment and detection precision. The project is distinguished by an NMS-free detection architecture that predicts a single bounding box per object, eliminating the need for non-maximum suppression post-processing to reduce inference latency. It further optimizes for edge hardware through scalable weights and a quantization-friendly structure that facilitates
Provides utilities for converting model weights into optimized formats compatible with various hardware accelerators.
ChatRWKV is an open-source frontend and GPU-accelerated inference engine designed for interacting with RWKV recurrent neural network language models. It provides a self-hosted web chat interface and a specialized client for generating human-like text using a linear-complexity architecture. The project utilizes a GPU-accelerated backend that employs custom CUDA kernels and dynamic model format conversion to increase processing speed and reduce memory overhead. It manages conversation history through state-based context management, updating a fixed-size hidden state to maintain a constant memor
Transforms model weights into specialized formats to accelerate loading and optimize GPU memory allocation.
jetson-inference is a set of libraries and tools for executing optimized deep learning models on embedded GPU hardware. Its primary purpose is to enable real-time computer vision and AI inference at the edge with low latency and high throughput. The project distinguishes itself through high-performance streaming analytics and the ability to execute concurrent AI pipelines on auto-grade silicon. It provides specialized support for multi-sensor stream processing, utilizing zero-copy data transport to load camera frames directly into GPU memory. The codebase covers a broad surface of capabiliti
Translates model weights between different formats to ensure interoperability between training frameworks and inference engines.
PowerInfer is a high-performance local large language model inference engine and sparse inference framework. It provides a runtime for executing models on consumer-grade hardware, utilizing a GPU acceleration backend to optimize tensor operations for graphics processors. The system distinguishes itself through a sparse inference framework that increases generation speed by skipping computations based on activation sparsity in model weights. It includes a GGUF model converter for transforming weights and metadata into a unified binary format, as well as an OpenAI API compatible server for inte
Transforms model weights into specialized formats required for optimized sparse inference.
BasicSR is a PyTorch-based image restoration toolbox and framework designed for training and deploying deep learning models to upscale, denoise, and deblur images and videos. It serves as a comprehensive system for image super-resolution and video quality restoration, providing the necessary infrastructure to recover fine visual details and increase pixel density. The project distinguishes itself through specialized toolkits for facial image enhancement and high-fidelity face synthesis, as well as a dedicated video quality restoration suite that utilizes deformable convolutions and generative
Translates neural network weights and architectures between different versions to ensure compatibility.
This repository provides a collection of reference implementations and code examples for training and deploying machine learning models using the MLX framework. It serves as a practical guide for executing distributed training, fine-tuning large language models, converting model weights, and implementing multimodal generative workflows. The project distinguishes itself through specialized examples for local hardware execution, featuring weight quantization to reduce memory usage and low-rank adaptation for parameter-efficient fine-tuning. It also includes scripts for transforming external mod
Ships scripts for transforming model weights and formats into MLX-compatible versions with precision quantization.
Dream Textures is a Stable Diffusion integration for Blender that provides tools for text-to-image generation, depth projection, and node-based processing within a 3D environment. It functions as an AI texture generator capable of producing image textures and concept art from text prompts and scene renders. The system features a depth-to-image projection tool that maps generated imagery onto 3D models using depth data for spatial alignment. It also includes a node-based AI image processor for creating procedural visual effects and a dedicated toolset for AI-assisted inpainting and outpainting
Transforms external model weights into a compatible internal format for use within the environment.
This project is a comprehensive collection of educational examples and reference implementations for building vision and language models using PyTorch. It serves as a deep learning tutorial covering the end-to-end process of developing neural networks, from initial architecture definition to final production deployment. The repository provides detailed guides on implementing a wide range of domain-specific models, including convolutional neural networks for object detection and segmentation, as well as transformer and recurrent architectures for natural language processing. It emphasizes gene
Transforms PyTorch weights into binary formats compatible with high-performance engines like TensorRT.
Audiblez is a text-to-speech audiobook generator that converts digital e-books into spoken audio files. The system processes written documents using speech synthesis and configurable voice profiles to produce audiobooks. The tool utilizes a graphical interface to manage the conversion workflow and task orchestration. It employs CUDA-accelerated processing to offload neural network computations to the GPU, increasing the speed of audio generation. The system includes capabilities for chapter-based file parsing and selective chapter conversion. Users can adjust synthesis parameters, including
Offers a visual interface for transforming digital book chapters into audio files.
tensorrtx is a computer vision inference engine and model implementation library designed for graphics processor acceleration. It provides a framework for optimizing deep learning models through a GPU inference optimizer, a deep learning model converter for transforming weights from frameworks like TensorFlow and PyTorch, and a custom plugin library to implement operations not natively supported by the TensorRT API. The project distinguishes itself through a comprehensive collection of pre-defined network implementations, ranging from various YOLO versions and DETR transformers for object det
Converts PyTorch pretrained weights into specialized execution formats for GPU optimization.
sd-scripts is a suite of utilities designed for fine-tuning generative models, preprocessing datasets, and converting model weights. It provides a collection of scripts for executing Stable Diffusion training through methods such as DreamBooth, textual inversion, and full fine-tuning, alongside a framework for creating and managing Low-Rank Adaptation weights. The project features specialized capabilities for model weight conversion between different architectures and precision formats. It includes tools for merging adaptation weights into base models, extracting weights from trained models,
Transforms model weights between different architectures and precision formats for compatibility.
This project is an object detection framework implementing the YOLOv3 architecture using Keras and TensorFlow. It functions as a deep learning vision model and computer vision toolset designed to locate and classify multiple entities within images and video streams using bounding boxes. The system includes a multi-GPU inference engine to distribute computational loads across several graphics processing units. It also provides a pipeline for creating custom object detectors by retraining pre-trained weights on annotated datasets to recognize user-defined object classes. The framework covers m
Provides utilities to convert model weights between different deep learning frameworks to ensure environment compatibility.
Metaseq es un toolkit de modelado de secuencias transformer diseñado para entrenar, ajustar y desplegar modelos de secuencia a secuencia utilizando pesos pre-entrenados abiertos. Proporciona un framework integral para el entrenamiento de modelos de lenguaje de gran tamaño, incluyendo herramientas dedicadas para el procesamiento de datasets de secuencias y un servidor de inferencia independiente para generar texto mediante solicitudes API. El proyecto cuenta con utilidades especializadas para la cuantización de modelos para reducir la precisión de los parámetros a ocho bits, lo que reduce el uso de memoria y aumenta la velocidad de inferencia. También incluye un pipeline de conversión de checkpoints para transformar pesos de modelos en estructuras optimizadas para motores de inferencia de alto rendimiento. El framework soporta entrenamiento a gran escala a través de clústeres de GPU mediante el uso de paralelismo de tensores y paralelismo de datos fragmentados. Capacidades adicionales cubren la preparación de datasets de NLP, carga de pesos pre-entrenados para transfer learning y seguimiento de métricas de entrenamiento para la visualización del progreso.
Provides tools to transform model weights from standard formats into specialized versions for high-performance inference.
Libation is a comprehensive audiobook management system designed to download, decrypt, and organize Audible audiobooks. It integrates account authentication, library synchronization, DRM removal, and format transcoding into a single desktop application with a plugin-free graphical interface and a built-in theme editor. The project distinguishes itself through a batch library synchronization engine that scans all configured Audible accounts in one pass, a metadata tag rewriting system that corrects chapter markers and cover art after decryption, and a template-based file naming engine that con
Downloads audiobooks in multiple audio formats based on user configuration.
stable-diffusion.cpp is a high-performance C++ inference engine designed for generating images and video from text prompts using Stable Diffusion models. It functions as a latent diffusion model runtime and a lightweight machine learning framework that enables local diffusion model execution on consumer hardware. The project distinguishes itself as a CPU-based image generator capable of running without a dedicated GPU. It employs a specialized C++ tensor backend and cross-backend hardware abstraction to dispatch compute tasks across different processor instruction sets and graphics APIs. The
Transforms complex model tensors into streamlined binary layouts optimized for sequential memory access.