Search Results for "cdm-core"
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Showing 196 open source projects for "cdm-core"
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Instill Core
Instill Core is a full-stack AI infrastructure tool for data
Instill Core is an open-source, full-stack AI infrastructure platform designed to orchestrate data pipelines, machine learning models, and unstructured data processing into a unified, production-ready system. It provides an end-to-end solution that enables developers to build, deploy, and manage AI-powered applications without needing to manually stitch together multiple tools across the data and model lifecycle. -
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Seldon Core
An MLOps framework to package, deploy, monitor and manage models
The de facto standard open-source platform for rapidly deploying machine learning models on Kubernetes. Seldon Core, our open-source framework, makes it easier and faster to deploy your machine learning models and experiments at scale on Kubernetes. Seldon Core serves models built in any open-source or commercial model building framework. You can make use of powerful Kubernetes features like custom resource definitions to manage model graphs. And then connect your continuous integration and deployment (CI/CD) tools to scale and update your deployment. ... -
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Core ML Tools
Core ML tools contain supporting tools for Core ML model conversion
Use Core ML Tools (coremltools) to convert machine learning models from third-party libraries to the Core ML format. This Python package contains the supporting tools for converting models from training libraries. Core ML is an Apple framework to integrate machine learning models into your app. Core ML provides a unified representation for all models. -
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SGR Agent Core
Schema-Guided Reasoning (SGR) has agentic system design
SGR Agent Core is an open-source framework for building intelligent AI research agents based on a methodology known as Schema-Guided Reasoning (SGR). The framework provides a core library that allows developers to design autonomous agents capable of structured reasoning and complex task execution. Instead of relying solely on free-form prompts, the system organizes reasoning processes around schemas that guide how agents analyze problems, gather information, and generate outputs. ... -
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Ultimate Vocal Remover (UVR5)
GUI for a Vocal Remover that uses Deep Neural Networks
This application uses state-of-the-art source separation models to remove vocals from audio files. UVR's core developers trained all of the models provided in this package (except for the Demucs v3 and v4 4-stem models). -
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OpenVoiceOS
OpenVoiceOS Core, the FOSS Artificial Intelligence platform
OpenVoiceOS is the central component of OpenVoiceOS, an open-source platform for smart speakers and voice-centric devices. It continues the spirit of Mycroft by giving developers and enthusiasts a hackable voice assistant foundation. The core package can be installed standalone for custom assistant development or as part of a fuller OVOS system through installer, Raspberry Pi, or embedded Linux workflows. OVOS is powered by skills, including OpenVoiceOS skills and many classic Mycroft-compatible skills. It can also use persona support to route fallback conversations to LLM-backed solvers when normal skills do not answer a request. ovos-core is useful for builders who want a community-driven, extensible voice assistant stack instead of a closed smart-speaker platform. -
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ComfyUI
The most powerful and modular diffusion model GUI, api and backend
...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 is to advance and democratize AI tooling. We believe that the future of AI tooling is open-source and community-driven. -
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Arcade AI
Arcade Tool Development Kit (TDK), Worker, Evals, and CLI
Arcade AI Platform is a developer-oriented toolkit for building, deploying, and managing tools tailored to AI agents, structured as modular Python packages for flexibility and extensibility. Core platform functionality and schemas. This repository contains the core Arcade libraries, organized as separate packages for maximum flexibility and modularity. Evaluation framework for testing tool performance. Test your MCP server's tools, resources, prompts, elicitation, and OAuth 2. MCPJam is compliant with the latest MCP specs. ... -
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LTX-Video
Official repository for LTX-Video
...The toolkit is built with both real-time and offline workflows in mind, enabling applications from consumer editing to professional content creation and batch processing. Internally optimized for multi-core processors and hardware acceleration where available, LTX-Video makes it feasible to work with high-resolution content and complex timelines without sacrificing responsiveness. -
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Hindsight
Hindsight: Agent Memory That Learns
...The system operates through three core mechanisms—retain, recall, and reflect—which respectively handle storing information, retrieving relevant context, and generating new insights based on accumulated experience. -
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zvt
Modular quant framework
...Once you are familiar with the core concepts of the system, you can apply it to any target in the market. -
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VibeTensor
Our first fully AI generated deep learning system
VibeTensor is a groundbreaking open-source research system software stack for deep learning that was uniquely generated almost entirely by AI coding agents under guided human supervision, demonstrating a new frontier in AI-assisted software engineering. It implements a PyTorch-style eager tensor library with a modern C++20 core that supports both CPU and CUDA backends, giving it the ability to manage tensors, automatic differentiation (autograd), and complex computation flows similar to mainstream frameworks. What makes VibeTensor remarkable is that every major component, from core libraries and dispatch systems to CUDA runtime support, caching allocators, and language bindings, was created and validated by coding agents using automated builds and tests rather than manual line-by-line human coding. ... -
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TorchCode
Practice implementing softmax, attention, GPT-2 and more
TorchCode is an interactive learning and practice platform designed to help developers master PyTorch by implementing core machine learning operations and architectures from scratch. It is structured similarly to competitive programming platforms like LeetCode but focuses specifically on tensor operations and deep learning concepts. The platform provides a collection of curated problems that cover fundamental topics such as activation functions, normalization layers, attention mechanisms, and full transformer architectures. ... -
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Applio
A simple, high-quality voice conversion tool focused on ease of use
...Within that interface, users can train and run voice conversion models for tasks like singing conversion, speech-to-speech transformation, and voice cloning. The project is structured to be flexible through plugins and configurations so users can extend functionality without touching the core code. Applio is considered stable and mature; ongoing development is now centered on security patches, dependency maintenance, and occasional improvements, which makes it attractive for production or repeatable workflows. It also includes TensorBoard helper scripts so people training custom models can monitor metrics and experiment more systematically. -
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Nerfstudio
A collaboration friendly studio for NeRFs
Nerfstudio provides a simple API that allows for a simplified end-to-end process of creating, training, and testing NeRFs. The library supports a more interpretable implementation of NeRFs by modularizing each component. With more modular NeRFs, we hope to create a more user-friendly experience in exploring the technology. This is a contributor-friendly repo with the goal of building a community where users can more easily build upon each other’s contributions. Nerfstudio initially launched... -
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Universal Tool Calling Protocol (UTCP)
Official python implementation of UTCP. UTCP is an open standard
...UTCP is an open, modern standard designed to let AI agents call any tool or API directly—over HTTP, CLI, WebSocket, gRPC, and more—without the overhead of extra wrapper layers or middleware. It leverages a modular, plugin-based architecture built around Pydantic models and separates the core functionality into a lightweight client and extensible protocol plugins, enabling secure, scalable, and low-latency direct tool calls. A pluggable architecture allows developers to easily add new communication protocols, tool storage mechanisms, and search strategies without modifying the core library. -
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Learn Claude Code
Bash is all you need, write a claude code with only 16 line code
Learn Claude Code is an educational repository that teaches how modern AI coding agents work by walking learners through a sequence of progressively more complex agent implementations, starting with a minimal Bash-based agent and culminating in agents with explicit planning, subagents, and skills. It emphasizes a hands-on learning path where each version (from v0 to v4) adds conceptual building blocks like the core agent loop, todo planning, task decomposition, and domain knowledge skills, illuminating the patterns behind what makes a true AI agent tick. The goal is to demystify agent architectures like Claude Code by having learners build simplified versions themselves and observe how tools, memory management, planning constraints, and context isolation contribute to reliable agent behavior. ... -
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BigMac
An open-source toolkit for BigMac-style pipeline-parallel training
...This design reduces activation memory without bringing back cross-module pipeline bubbles. Its scheduler creates global operator plans, while its executor runs those plans through a shared schedule abstraction. A Megatron-Core reference backend and Qwen3 and Qwen3-VL tutorials help developers connect the approach to real training workflows. The simulator lets researchers visualize schedules, compare pipeline strategies, and model timing imbalances caused by compute cost, input size, or uneven stage partitioning. Profiling tools also expose per-operator traces for diagnosing pipeline performance before or during experiments. -
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machine-learning-refined
Master the fundamentals of machine learning, deep learning
...Instead of presenting algorithms purely through mathematical derivations, the repository emphasizes geometric intuition, visualization, and step-by-step experimentation. It includes Jupyter notebooks and scripts that illustrate core machine learning topics such as regression, classification, optimization methods, and neural networks. These materials allow learners to see how algorithms behave during training and how different parameters affect model performance. -
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TRELLIS.2
Native and Compact Structured Latents for 3D Generation
TRELLIS.2 is a cutting-edge open-source model and codebase for high-fidelity 3D asset generation from 2D images, developed to push forward the state of the art in image-to-3D generation. At its core is a novel sparse voxel structure called O-Voxel that jointly encodes both geometry and surface appearance, enabling reconstruction and generation of complex 3D shapes with arbitrary topology, open surfaces, and physically based rendering (PBR) textures. The system leverages a large 4-billion-parameter architecture combining sparse 3D variational autoencoders with flow-matching transformers to produce fully textured 3D models at resolutions up to 1536³ voxels. ... -
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caveman
Why use many token when few token do trick
...It may include utilities for routing, state handling, or simple server logic, depending on its implementation scope. Caveman embraces a philosophy of “less is more,” encouraging developers to focus on core functionality rather than framework overhead. Its design makes it particularly useful for experimentation, small tools, or proof-of-concept applications. -
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CyberPPT
A Codex Skill for generating high-density, editable PowerPoints
...Users select from eight fixed visual systems, after which the skill plans each slide’s hierarchy, grid, charts, palette, and information density. Image-generation blueprints guide reconstruction while native text, shapes, tables, charts, and vectors preserve core editability. Multiple quality gates inspect structure, visuals, overflow, spatial alignment, curves, and editable elements. Delivery includes the PPTX, rendered previews, manifests, and QA results, with failed hard gates blocking completion. -
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nimbletools-core
Open Source MCP Platform - Build, scale, and deploy MCP servers on k8s
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OpenManus
Open-source AI agent framework
OpenManus is an open-source AI agent framework designed to autonomously execute complex, multi-step tasks by combining reasoning, planning, and tool use. It enables developers to build agents that can think, act, and iterate toward goals rather than simply responding to prompts. The platform emphasizes task decomposition, allowing agents to break down objectives into smaller steps and execute them sequentially or recursively. OpenManus supports integration with external tools, APIs, and... -
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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 understand, customize, and extend for experimentation or production use. With simple one-click deployment and a straightforward CLI, users can get a working AI assistant running in minutes. ...