Search Results for "requirements" - Page 3
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Showing 181 open source projects for "requirements"
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PipesHub
Workplace AI platform for enterprise search and workflow automation
...PipesHub also enables the creation of custom AI agents and applications through a no-code interface, allowing teams to automate workflows and build intelligent tools without deep technical expertise. It supports flexible deployment options, including on-premise and cloud environments, ensuring compatibility with different security and infrastructure requirements. -
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ComfyUI-Copilot
AI assistant for ComfyUI workflow generation, debugging, and tuning
...It supports the entire workflow lifecycle, including generation, debugging, rewriting, and parameter optimization, helping users iterate more efficiently. ComfyUI-Copilot leverages large language model capabilities to analyze user intent, recommend nodes, and suggest models that match specific requirements. It also provides automated error detection and repair suggestions, improving reliability during development. -
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BoxMOT
Pluggable SOTA multi-object tracking modules for segmentation
...It also includes evaluation tools and benchmarking pipelines that allow researchers to test tracking performance on standard datasets such as MOT17 and MOT20. The system offers different performance modes that balance computational efficiency with tracking accuracy depending on the application requirements. -
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OneFileLLM
Specify a github or local repo, github pull request
OneFileLLM is an open-source project designed to simplify the distribution and execution of large language model applications by packaging them into a single portable file. The concept behind the project is to eliminate the complexity normally associated with deploying AI systems, which often require multiple dependencies, frameworks, and configuration steps. Instead, the entire runtime environment, model interface, and application logic are bundled together into a single executable... -
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Optuna
A hyperparameter optimization framework
Optuna is an automatic hyperparameter optimization software framework, particularly designed for machine learning. It features an imperative, define-by-run style user API. Thanks to our define-by-run API, the code written with Optuna enjoys high modularity, and the user of Optuna can dynamically construct the search spaces for the hyperparameters. Optuna Dashboard is a real-time web dashboard for Optuna. You can check the optimization history, hyperparameter importances, etc. in graphs and... -
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loonflow
A workflow engine base on django python
a workflow engine base on django The django-based workflow engine system (called through the http interface, can be used as a unified workflow engine within the enterprise, providing all workflows such as permission application, resource application, release application, leave, reimbursement, it service, etc. Scenario services), if there is a certain development capability, it is recommended to use only the back-end engine function, and the front-end customized development according to the... -
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AI-Codereview-Gitlab
GitLab automatic code review tool based on large models
...By leveraging multiple large language model providers—including OpenAI, DeepSeek, ZhipuAI, or local models through Ollama—the platform allows teams to choose the AI engine that best fits their infrastructure and privacy requirements. When code changes occur, the system can automatically generate review comments and feedback that are posted directly into GitLab merge requests, allowing developers to see suggestions alongside human reviewer comments. In addition to code analysis, the tool can produce daily development summaries and notifications that help teams track progress and review activity across projects. -
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Pika Skills
A collection of open-source skills for AI coding agents
Pika Skills is an open-source framework designed to extend the capabilities of AI coding agents by introducing modular, reusable “skills” that can be dynamically invoked during development workflows. Each skill acts as a self-contained unit composed of structured instructions, executable scripts, and dependency definitions, enabling agents to autonomously perform complex tasks without requiring manual configuration or orchestration. The system is tightly integrated with the Pika Developer... -
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LiveKit Agents
Framework for building realtime multimodal voice AI agents apps
...LiveKit Agents provides libraries and tooling that allow developers to combine speech-to-text, large language models, and text-to-speech services to build interactive AI experiences. It is designed to run server-side and can integrate with various AI model providers and realtime APIs to support different application requirements. LiveKit Agents also includes tools for scheduling and managing agent tasks, making it easier to connect users to automated assistants in live communication scenarios. -
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face.evoLVe
High-Performance Face Recognition Library on PaddlePaddle & PyTorch
...The repository supports multiple neural network backbones such as ResNet, DenseNet, MobileNet, and ShuffleNet, enabling experimentation with different architectures depending on performance requirements. It also implements a wide range of loss functions commonly used in face recognition research, including ArcFace, CosFace, Triplet loss, and Softmax variants. To improve scalability, the library introduces distributed training techniques that allow large models to be trained efficiently across multiple GPUs. -
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seq2seq-couplet
Play couplet with seq2seq model
...In addition to local execution, the project includes Docker files, which make it easier to package and deploy the application in a more reproducible way. The repository also points users to an external dataset source and documents vocabulary formatting requirements for custom datasets, showing that it is meant for both experimentation and extension. -
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MatMul-Free LM
Implementation for MatMul-free LM
...Since matrix multiplication is one of the most computationally expensive components of modern language models, the project explores alternative computational strategies that reduce hardware requirements while maintaining comparable performance. The architecture relies on quantization-aware training and lightweight operations to replace conventional dense matrix multiplications with more efficient alternatives. These optimizations can significantly reduce memory consumption and potentially improve computational efficiency during both training and inference. ... -
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The Alignment Handbook
Robust recipes to align language models with human and AI preferences
The Alignment Handbook is an open-source resource created to provide practical guidance for aligning large language models with human preferences and safety requirements. The project focuses on the post-training stage of model development, where models are refined after pre-training to behave more helpfully, safely, and reliably in real-world applications. It provides detailed training recipes that explain how to perform tasks such as supervised fine-tuning, preference modeling, and reinforcement learning from human feedback. ... -
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DeepSearcher
Open Source Deep Research Alternative to Reason and Search
...The project integrates with vector databases (including Milvus and related options) so organizations can index internal documents and query them with semantic retrieval. It also supports flexible embeddings, making it easier to choose different embedding models depending on domain requirements, latency targets, or accuracy goals. The overall workflow aims to minimize hallucinations by grounding outputs in retrieved material and then applying structured reasoning over that evidence before generating a final report. -
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bitsandbytes
Accessible large language models via k-bit quantization for PyTorch
...The project includes specialized optimizers and quantized matrix operations that significantly reduce the memory footprint of training and inference workloads. By lowering the hardware requirements needed to work with large models, bitsandbytes helps make modern AI development more accessible to researchers and engineers. The library has become widely used in machine learning pipelines that rely on parameter-efficient training techniques and low-precision inference. -
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Dolphin
Document Image Parsing via Heterogeneous Anchor Prompting”
...It seeks to combine performant media playback or handling (audio/video decoding, streaming, buffering) with a modular, developer-friendly API that allows easy embedding into larger applications or services. Because multimedia delivery requirements vary widely (adaptive streaming, live feeds, cross-platform compatibility, custom UI, performance constraints), Dolphin aims to offer a foundation that developers can build upon or adapt to their needs. It is designed to integrate with other tools and libraries and provide stable playback or media-processing pipelines, while remaining open-source so that users can inspect, extend, and adapt it. -
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HunyuanVideo-Avatar
Tencent Hunyuan Multimodal diffusion transformer (MM-DiT) model
HunyuanVideo-Avatar is a multimodal diffusion transformer (MM-DiT) model by Tencent Hunyuan for animating static avatar images into dynamic, emotion-controllable, and multi-character dialogue videos, conditioned on audio. It addresses challenges of motion realism, identity consistency, and emotional alignment. Innovations include a character image injection module, an Audio Emotion Module for transferring emotion cues, and a Face-Aware Audio Adapter to isolate audio effects on faces,... -
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ZenML
Build portable, production-ready MLOps pipelines
...Set up ZenML in a matter of minutes, and start with all the tools you already use. Gradually scale up your MLOps stack by switching out components whenever your training or deployment requirements change. Keep up with the latest changes in the MLOps world and easily integrate any new developments. Define simple and clear ML workflows without wasting time on boilerplate tooling or infrastructure code. Write portable ML code and switch from experimentation to production in seconds. Manage all your favorite MLOps tools in one place with ZenML's plug-and-play integrations. ... -
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AutoAgent
AutoAgent: Fully-Automated and Zero-Code LLM Agent Framework
...It is structured around modes that cover both “use” and “build” scenarios: a user mode for running a ready-made multi-agent research assistant, plus editors for creating individual agents or multi-agent workflows from conversational requirements. The framework emphasizes self-managing workflow generation, where it can infer steps, refine them, and adapt plans even when users cannot fully specify implementation details up front. It also describes resource orchestration and iterative self-improvement behaviors, including controlled code generation for building tools and agent capabilities when needed. ... -
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OpenAGI
When LLM Meets Domain Experts
...It provides a structured Python framework, pyopenagi, for defining agents as modular units that encapsulate execution logic, configuration, and dependency metadata. Agents are organized in a well-defined folder structure that includes code (agent.py), configuration (config.json), and extra requirements (meta_requirements.txt), which makes them easy to package, share, and reuse. The project includes tooling for registering agents with AIOS by uploading them via a command-line interface, enforcing a consistent naming scheme that matches the local folder layout. A companion tooling layer lets agents call external tools described in the tools.md documentation, enabling them to orchestrate APIs, retrieval pipelines, and other utilities in response to LLM decisions. -
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Archivematica
Free and open-source digital preservation system
Archivematica is a web- and standards-based, open-source application which allows your institution to preserve long-term access to trustworthy, authentic, and reliable digital content. Our target users are archivists, librarians, and anyone working to preserve digital objects. You are free to copy, modify, and distribute Archivematica with attribution under the terms of the AGPLv3 license. Archivematica is an open-source application based on recognized standards that makes it possible to... -
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BitNet
BitNet: Scaling 1-bit Transformers for Large Language Models
...By limiting weight precision while maintaining efficient scaling and normalization strategies, the architecture aims to retain competitive performance while significantly reducing hardware requirements. -
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AutoGroq
Revolutionizes the way users interact with Autogen
AutoGroq is a groundbreaking tool that revolutionizes the way users interact with Autogen™ and other AI assistants. By dynamically generating tailored teams of AI agents based on your project requirements, AutoGroq eliminates the need for manual configuration and allows you to tackle any question, problem, or project with ease and efficiency. AutoGroq was born out of the realization that the traditional approach to building AI agents was backwards. Instead of creating agents in anticipation of problems, AutoGroq uses the syntax of the users' needs as the basis for constructing the perfect AI team. ... -
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PKU Beaver
Constrained Value Alignment via Safe Reinforcement Learning
...These annotations include categories such as harmful language, unethical behavior, privacy violations, and other sensitive topics. By incorporating constraint-based optimization methods, Safe-RLHF trains models that balance reward objectives with safety requirements, ensuring that harmful outputs are penalized during training. -
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gpt-engineer
Full stack AI software engineer
gpt-engineer is an open-source platform designed to help developers automate the software development process using natural language. The platform allows users to specify software requirements in plain language, and the AI generates and executes the corresponding code. It can also handle improvements and iterative development, giving users more control over the software they’re building. Built with a terminal-based interface, gpt-engineer is customizable, enabling developers to experiment with AI-assisted programming and refine their development process. ...
