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Showing 87 open source projects for "loading"
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MongoDB Atlas runs apps anywhereMongoDB Atlas gives you the freedom to build and run modern applications anywhere—across AWS, Azure, and Google Cloud. With global availability in over 115 regions, Atlas lets you deploy close to your users, meet compliance needs, and scale with confidence across any geography.
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Go from Code to Production URL in SecondsSkip the Kubernetes configs. Cloud Run handles HTTPS, scaling, and infrastructure automatically. Two million requests free per month.
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DALI
A GPU-accelerated library containing highly optimized building blocks
The NVIDIA Data Loading Library (DALI) is a library for data loading and pre-processing to accelerate deep learning applications. It provides a collection of highly optimized building blocks for loading and processing image, video and audio data. It can be used as a portable drop-in replacement for built-in data loaders and data iterators in popular deep learning frameworks. -
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Scene Framework
Android Single Activity Framework compatible with Fragment
...The repository may aim to help developers manage complex state, transitions, or UI/navigation flows in applications, or provide a structured framework for rendering or content pipelines. As such, Scene could serve as a backbone for media apps, games, or modular user interface projects, offering abstractions for scene management, resource loading, event dispatching, and lifecycle handling. Because of its open-source status, developers may adapt it to their own use-cases — customizing rendering, UI transitions, or content management — though the maturity and documentation may vary. -
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ComfyUI
The most powerful and modular diffusion model GUI, api and backend
The most powerful and modular diffusion model is GUI and backend. This UI will let you design and execute advanced stable diffusion pipelines using a graph/nodes/flowchart-based interface. We are a team dedicated to iterating and improving ComfyUI, supporting the ComfyUI ecosystem with tools like node manager, node registry, cli, automated testing, and public documentation. Open source AI models will win in the long run against closed models and we are only at the beginning. Our core mission... -
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JMusicBot
A Discord music bot that's easy to set up and run yourself
A cross-platform Discord music bot with a clean interface, and that is easy to set up and run yourself! Easy to run (just make sure Java is installed, and run!) Fast loading of songs. No external keys are needed (besides a Discord Bot token) Smooth playback. Server-specific setup for the "DJ" role that can moderate the music. Clean and beautiful menus. Supports many sites, including Youtube, Soundcloud, and more. Supports many online radio/streams. Supports local files. Playlist support (both web/youtube and local) This bot (and the source code here) might not be easy to edit for inexperienced programmers. ... -
Forever Free Full-Stack Observability | Grafana CloudBuilt on open standards like Prometheus and OpenTelemetry, Grafana Cloud includes Kubernetes Monitoring, Application Observability, Incident Response, plus the AI-powered Grafana Assistant. Get started with our generous free tier today.
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Gemma in PyTorch
The official PyTorch implementation of Google's Gemma models
gemma_pytorch provides the official PyTorch reference for running and fine-tuning Google’s Gemma family of open models. It includes model definitions, configuration files, and loading utilities for multiple parameter scales, enabling quick evaluation and downstream adaptation. The repository demonstrates text generation pipelines, tokenizer setup, quantization paths, and adapters for low-rank or parameter-efficient fine-tuning. Example notebooks walk through instruction tuning and evaluation so teams can benchmark and iterate rapidly. ... -
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PyTorch3D
PyTorch3D is FAIR's library of reusable components for deep learning
...The library provides fast GPU-accelerated implementations of rendering pipelines, transformations, rasterization, and lighting—making it possible to compute gradients through full 3D rendering processes. Researchers use it for tasks like shape generation, reconstruction, view synthesis, and visual reasoning. PyTorch3D also includes utilities for loading, transforming, and sampling 3D assets, so models can be trained end-to-end from 2D supervision or partial data. Its modular design allows easy extension—components like differentiable rasterizers, mesh blending, or signed distance field (SDF) modules can be swapped or combined to test new architectures quickly. -
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Ruler AI
Centralize and sync AI coding rules across tools and projects
...Instead of maintaining separate configuration files for tools like GitHub Copilot, Claude, or Cursor, it stores all rules in a single .ruler/ directory and distributes them automatically. This reduces duplication, avoids inconsistent outputs, and keeps guidance aligned as projects evolve. Ruler supports nested rule loading, allowing teams to define context-specific instructions for different parts of a codebase. It also manages MCP server settings, automates .gitignore updates, and provides simple commands to initialize, apply, and revert configurations. Designed for teams using multiple AI agents, it improves workflow consistency, simplifies onboarding, and ensures all assistants follow the same standards across projects. -
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High-Level Training Utilities Pytorch
High-level training, data augmentation, and utilities for Pytorch
...This package provides a few things. A high-level module for Keras-like training with callbacks, constraints, and regularizers. Comprehensive data augmentation, transforms, sampling, and loading. Utility tensor and variable functions so you don't need numpy as often. Have any feature requests? Submit an issue! I'll make it happen. Specifically, any data augmentation, data loading, or sampling functions. ModuleTrainer. The ModuleTrainer class provides a high-level training interface that abstracts away the training loop while providing callbacks, constraints, initializers, regularizers, and more. ... -
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Axolotl
Go ahead and axolotl questions
Axolotl is a powerful and flexible framework for fine-tuning large language models on custom datasets. Built for researchers and developers, Axolotl simplifies the process of adapting LLMs for specific tasks, including chat, code generation, and instruction following. It supports a wide variety of model architectures and offers out-of-the-box optimization strategies for efficient training. -
Train ML Models With SQL You Already KnowBuild and deploy ML models using familiar SQL. Automate data prep with built-in Gemini. Query 1 TB and store 10 GB free monthly.
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Text Generation Web UI
Oobabooga - The definitive Web UI for local AI, with powerful features
...Layers splitting across GPU(s), CPU, and disk. CPU mode, FlexGen, DeepSpeed ZeRO-3, API with streaming and without streaming. LLaMA model, including 4-bit GPTQ. RWKV model, LoRA (loading and training), Softprompts, and extensions. -
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nanobot
🐈 nanobot: The Ultra-Lightweight Clawdbot / OpenClaw
nanobot is an ultra-lightweight personal AI assistant designed to deliver powerful agent capabilities without unnecessary complexity. Built in just ~4,000 lines of clean, readable code, it offers a minimalist alternative to heavyweight agent frameworks while retaining core intelligence and extensibility. nanobot is optimized for speed and efficiency, enabling fast startup times and low resource usage across environments. Its research-ready architecture makes it easy for developers to... -
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CoPaw
Your Personal AI Assistant; easy to install, deploy on local or coud
...It includes a browser-based Console for chatting, configuring models, managing memory, and extending capabilities with custom skills. With built-in cron scheduling, heartbeat check-ins, and extensible skill loading, CoPaw grows with your workflow over time. Easy installation options—including pip, one-line scripts, Docker, and cloud deployment—make it accessible for both developers and non-technical users. -
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FLUX.1
Official inference repo for FLUX.1 models
...The project is part of a larger family of FLUX models developed by Black Forest Labs, designed to produce high-quality, detailed visuals from text descriptions with competitive prompt adherence and artistic fidelity. This repo focuses on running the open-source model variants efficiently, providing scripts, model loading logic, and examples for local installations, and supports integration with Python toolchains like PyTorch and popular generative pipelines. Users can launch CLI tools to generate images, experiment with different FLUX variants, and extend the base code for research-oriented applications. -
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HunyuanImage-3.0
A Powerful Native Multimodal Model for Image Generation
...The model is intended to be competitive with closed-source image generation systems, aiming for high fidelity, prompt adherence, fine detail, and even “world knowledge” reasoning (i.e. leveraging context, semantics, or common sense in generation). The GitHub repo includes code, scripts, model loading instructions, inference utilities, prompt handling, and integration with standard ML tooling (e.g. Hugging Face / Transformers). -
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MCP Image Compression
A high-performance image compression microservice based on MCP
The MCP Image Compression server is a high-performance microservice based on the Model Context Protocol architecture. It focuses on providing fast and high-quality image compression capabilities to help developers optimize image resources for websites and applications, improving loading speed and user experience. -
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ChatGLM-6B
ChatGLM-6B: An Open Bilingual Dialogue Language Model
ChatGLM-6B is an open bilingual (Chinese + English) conversational language model based on the GLM architecture, with approximately 6.2 billion parameters. The project provides inference code, demos (command line, web, API), quantization support for lower memory deployment, and tools for finetuning (e.g., via P-Tuning v2). It is optimized for dialogue and question answering with a balance between performance and deployability in consumer hardware settings. Support for quantized inference... -
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TorchAudio
Data manipulation and transformation for audio signal processing
The aim of torchaudio is to apply PyTorch to the audio domain. By supporting PyTorch, torchaudio follows the same philosophy of providing strong GPU acceleration, having a focus on trainable features through the autograd system, and having consistent style (tensor names and dimension names). Therefore, it is primarily a machine learning library and not a general signal processing library. The benefits of PyTorch can be seen in torchaudio through having all the computations be through PyTorch... -
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MLX-Audio
A text-to-speech, speech-to-text and speech-to-speech library
MLX-Audio is a speech library built on Apple’s MLX framework and optimized for Apple Silicon machines (M-series Macs). It focuses on text-to-speech and speech-to-speech workflows, with APIs and a command-line interface that make it easy to generate high-quality audio from text. Because it uses MLX and targets Apple Silicon, inference is fast and can take advantage of hardware acceleration and quantization for efficient on-device performance. The project provides a straightforward CLI... -
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qvac-fabric-llm.cpp
QVAC Fabric: cross-platform LLM inference and fine-tuning
qvac-fabric-llm.cpp is a cross-platform large language model inference and fine-tuning engine built as an advanced fork of llama.cpp, designed to run efficiently across desktops, mobile devices, and heterogeneous GPU environments. The project focuses on removing hardware limitations traditionally associated with LLM deployment by enabling support for a wide range of backends, including Vulkan, Metal, CUDA, and CPU, making it accessible on devices ranging from smartphones to enterprise... -
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JDA
Java wrapper for the popular chat & VOIP service
...We have officially dropped support for client login as of version 4.2.0! Note that JDA is not a good tool to build a custom discord client as it loads all servers/guilds on startup, unlike a client which does this via lazy loading instead. If you need a bot, use a bot account from the Application Dashboard. Creating the JDA Object is done via the JDABuilder class. After setting the token and other options via setters, the JDA Object is then created by calling the build() method. When build() returns, JDA might not have finished starting up. However, you can use await ready() on the JDA object to ensure that the entire cache is loaded before proceeding. -
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LLM Scraper
Extract structured data from webpages using LLM-powered scraping
LLM Scraper is a TypeScript library designed to extract structured data from webpages using large language models. Instead of relying on fragile HTML selectors or manual parsing rules, the tool interprets webpage content with language models and converts it into structured data according to a defined schema. Developers can specify the data structure using tools such as Zod or JSON Schema, enabling the model to extract relevant information directly into typed objects. LLM Scraper integrates... -
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Pedalboard
A Python library for audio
pedalboard is a Python library for working with audio: reading, writing, rendering, adding effects, and more. It supports the most popular audio file formats and a number of common audio effects out of the box and also allows the use of VST3® and Audio Unit formats for loading third-party software instruments and effects. pedalboard was built by Spotify’s Audio Intelligence Lab to enable using studio-quality audio effects from within Python and TensorFlow. Internally at Spotify, pedalboard is used for data augmentation to improve machine learning models and to help power features like Spotify’s AI DJ and AI Voice Translation. pedalboard also helps in the process of content creation, making it possible to add effects to audio without using a Digital Audio Workstation. -
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DataProfiler
Extract schema, statistics and entities from datasets
DataProfiler is an AI-powered tool for automatic data analysis and profiling, designed to detect patterns, anomalies, and schema inconsistencies in structured and unstructured datasets. The DataProfiler is a Python library designed to make data analysis, monitoring, and sensitive data detection easy. Loading Data with a single command, the library automatically formats & loads files into a DataFrame. Profiling the Data, the library identifies the schema, statistics, entities (PII / NPI), and more. Data Profiles can then be used in downstream applications or reports. -
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wllama
WebAssembly binding for llama.cpp - Enabling on-browser LLM inference
wllama is a WebAssembly-based library that enables large language model inference directly inside a web browser. Built as a binding for the llama.cpp inference engine, the project allows developers to run LLM models locally without requiring a server backend or dedicated GPU hardware. The library leverages WebAssembly SIMD capabilities to achieve efficient execution within modern browsers while maintaining compatibility across platforms. By running models locally on the user’s device, wllama... -
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VLMEvalKit
Open-source evaluation toolkit of large multi-modality models (LMMs)
...Instead of requiring complex data preparation pipelines or multiple repositories for each benchmark, the system enables evaluation through simple commands that automatically handle dataset loading, model inference, and metric computation. VLMEvalKit supports generation-based evaluation methods, allowing models to produce textual responses to visual inputs while measuring performance through techniques such as exact matching or language-model-assisted answer extraction.
