Search Results for "optimization" - Page 6
Sort By:
Showing 372 open source projects for "optimization"
View related business solutions-
$300 Free Credits for Your Google Cloud ProjectsLaunch your next project with $300 in free Google Cloud credits—no strings attached. Test, build, and deploy without risk. Use your credits across the entire Google Cloud platform to find what works best for your needs. After your credits are used, continue with always-free tier services. Only pay when you're ready to scale. Sign up in minutes and start exploring.
-
Stop Cyber Threats with VM-Series Next-Gen Firewall on AzureGain integrated visibility across all traffic in a single pass. Deploy Palo Alto Networks VM-Series to determine application identity and content while automating security policy updates via rich APIs.
-
1
BCEmbedding
Netease Youdao's open-source embedding and reranker models
BCEmbedding is NetEase Youdao’s open-source embedding and reranker model project for retrieval-augmented generation workflows. It includes an EmbeddingModel for semantic vector generation and a RerankerModel for refining and ordering search results. The project is optimized for bilingual and cross-lingual retrieval, especially across Chinese and English. It is used as a foundation for RAG systems such as QAnything and other Youdao products. The models are designed to work directly without... -
2
Qlib
Qlib is an AI-oriented quantitative investment platform
Qlib is an AI-oriented quantitative investment platform, which aims to realize the potential, empower the research, and create the value of AI technologies in quantitative investment. With Qlib, you can easily try your ideas to create better Quant investment strategies. An increasing number of SOTA Quant research works/papers are released in Qlib. With Qlib, users can easily try their ideas to create better Quant investment strategies. At the module level, Qlib is a platform that consists of... -
3
AutoGen
An Open-Source Programming Framework for Agentic AI
AutoGen is an open-source programming framework for building AI agents and facilitating cooperation among multiple agents to solve tasks. AutoGen aims to provide an easy-to-use and flexible framework for accelerating development and research on agentic AI, like PyTorch for Deep Learning. It offers features such as agents that can converse with other agents, LLM and tool use support, autonomous and human-in-the-loop workflows, and multi-agent conversation patterns. AutoGen provides... -
4
MCP Toolbox for Databases
Open source MCP server that exposes database tools for AI agents
...It provides a central service that exposes database operations as reusable tools that can be consumed by AI agents and developer workflows. It handles common infrastructure concerns such as authentication, connection pooling, and performance optimization so developers do not have to implement them individually in each application. By defining tools and data sources through configuration files, developers can standardize how AI systems access and operate on database resources. GenAI Toolbox is designed to integrate with agent frameworks and development environments so that AI assistants can execute database-related tasks with proper context and security. ... -
Our Free Plans just got better! | Auth0You asked, we delivered! Auth0 is excited to expand our Free and Paid plans to include more options so you can focus on building, deploying, and scaling applications without having to worry about your security. Auth0 now, thank yourself later.
-
5
Google Workspace MCP Server
Control Gmail, Google Calendar, Docs, Sheets, Slides, Chat, Forms
Google Workspace MCP is an open-source server that connects AI assistants to Google Workspace services through the Model Context Protocol (MCP), allowing large language models to interact directly with productivity tools. The project exposes a wide set of Google services including Gmail, Google Drive, Docs, Sheets, Slides, Calendar, Chat, and other Workspace components as structured tools that an AI system can call programmatically. By acting as a bridge between AI clients and the Google... -
6
CogAgent
An open sourced end-to-end VLM-based GUI Agent
CogAgent is a 9B-parameter bilingual vision-language GUI agent model based on GLM-4V-9B, trained with staged data curation, optimization, and strategy upgrades to improve perception, action prediction, and generalization across tasks. It focuses on operating real user interfaces from screenshots plus text, and follows a strict input–output format that returns structured actions, grounded operations, and optional sensitivity annotations. The model is designed for agent-style execution rather than freeform chat, maintaining a continuous execution history across steps while requiring a fresh session for each new task. ... -
7
LLM From Scratch
Build and train a GPT-style language model
LLM From Scratch is a hands-on educational workshop project that teaches developers how to build and train a GPT-style language model entirely from scratch using PyTorch. Instead of relying on high-level abstractions or prebuilt frameworks, the project walks users through implementing every core component manually, including tokenization, transformer architecture, training loops, and autoregressive text generation. The repository is intentionally simplified to focus on conceptual clarity,... -
8
vibecode-cli
The official vibecode.dev CLI built for agents
...It provides an interactive interface powered by modern terminal UI libraries, allowing developers to write, compile, and run code across multiple programming languages without leaving the command line. The tool leverages AI models to assist with code generation, debugging, and optimization, making it particularly useful for rapid prototyping and iterative development. . It supports a wide variety of programming languages and file types, enabling developers to work on diverse projects within a unified interface. Vibecode CLI also handles process execution, logging, and threading, ensuring smooth operation even for more complex tasks. ... -
9
thorough-pytorch
PyTorch Getting Started Tutorial, read online
thorough-pytorch is an educational project designed to teach deep learning using the PyTorch framework through a structured learning series. The repository provides tutorials and practical exercises that guide learners from fundamental PyTorch concepts to more advanced deep learning techniques. It emphasizes a learning approach that combines theoretical explanations with hands-on coding exercises so that students can build and experiment with neural networks directly. The project encourages... -
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.
-
10
DecryptPrompt
Summarize Prompt & LLM papers, open source data & models
...The project collects papers, technical reports, and research materials that explore prompting techniques, model architectures, and reasoning strategies used in modern AI systems. It serves as a structured knowledge base where developers and researchers can quickly find key papers about topics such as chain-of-thought reasoning, prompt optimization, reasoning frameworks, and model training techniques. The repository organizes research into thematic sections that cover different prompting methodologies and reasoning paradigms used in LLM development. Many of the resources focus on understanding how prompts influence model behavior and how prompting strategies can improve reasoning or efficiency. -
11
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... -
12
Bolt NLP
Bolt is a deep learning library with high performance
Bolt is a high-performance deep learning inference framework developed by Huawei Noah's Ark Lab. It is designed to optimize and accelerate the deployment of deep learning models across various hardware platforms. Bolt is a light-weight library for deep learning. Bolt, as a universal deployment tool for all kinds of neural networks, aims to automate the deployment pipeline and achieve extreme acceleration. Bolt has been widely deployed and used in many departments of HUAWEI company, such as... -
13
TruLens
Evaluation and Tracking for LLM Experiments
...Fine-grained, stack-agnostic instrumentation and comprehensive evaluations help identify failure modes and systematically iterate to improve applications. An easy-to-use interface that allows developers to compare different versions of their applications, facilitating informed decision-making and optimization. TruLens supports various use cases, including question-answering, summarization, retrieval-augmented generation, and agent-based applications. -
14
Causal ML
Uplift modeling and causal inference with machine learning algorithms
Causal ML is a Python package that provides a suite of uplift modeling and causal inference methods using machine learning algorithms based on recent research [1]. It provides a standard interface that allows users to estimate the Conditional Average Treatment Effect (CATE) or Individual Treatment Effect (ITE) from experimental or observational data. Essentially, it estimates the causal impact of intervention T on outcome Y for users with observed features X, without strong assumptions on... -
15
Claw Compactor
14-stage Fusion Pipeline for LLM token compression
Claw Compactor is a utility designed to optimize and manage the context limitations inherent in AI agent systems, particularly those built on OpenClaw-like architectures. It addresses the challenge of finite context windows in language models by compressing or summarizing historical interactions while preserving essential information. The system works by transforming older conversation data into condensed representations that maintain continuity without exceeding token limits. This approach... -
16
BitNet
BitNet: Scaling 1-bit Transformers for Large Language Models
BitNet is a machine learning research implementation that explores extremely low-precision neural network architectures designed to dramatically reduce the computational cost of large language models. The project implements the BitNet architecture described in research on scaling transformer models using extremely low-bit quantization techniques. In this approach, neural network weights are quantized to approximately one bit per parameter, allowing models to operate with far lower memory... -
17
node-llama-cpp
Run AI models locally on your machine with node.js bindings for llama
node-llama-cpp is a JavaScript and Node.js binding that allows developers to run large language models locally using the high-performance inference engine provided by llama.cpp. The library enables applications built with Node.js to interact directly with local LLM models without requiring a remote API or external service. By using native bindings and optimized model execution, the framework allows developers to integrate advanced language model capabilities into desktop applications, server... -
18
DocETL
A system for agentic LLM-powered data processing and ETL
DocETL is an open-source system designed to build and execute data processing pipelines powered by large language models, particularly for analyzing complex collections of documents and unstructured datasets. The platform allows developers and researchers to construct structured workflows that extract, transform, and organize information from sources such as reports, transcripts, legal documents, and other text-heavy data. Instead of relying on single prompts or ad-hoc scripts, DocETL... -
19
DINOv2
PyTorch code and models for the DINOv2 self-supervised learning
...It builds on the DINO idea of student–teacher distillation and adapts it to modern Vision Transformer backbones with a carefully tuned recipe for data augmentation, optimization, and multi-crop training. The core promise is that a single pretrained backbone can transfer well to many downstream tasks—from linear probing on classification to retrieval, detection, and segmentation—often requiring little or no fine-tuning. The repository includes code for training, evaluating, and feature extraction, with utilities to run k-NN or linear evaluation baselines to assess representation quality. ... -
20
dlib
Toolkit for making machine learning and data analysis applications
Dlib is a modern C++ toolkit containing machine learning algorithms and tools for creating complex software in C++ to solve real world problems. It is used in both industry and academia in a wide range of domains including robotics, embedded devices, mobile phones, and large high performance computing environments. Dlib's open source licensing allows you to use it in any application, free of charge. Good unit test coverage, the ratio of unit test lines of code to library lines of code is... -
21
Claude-Flow
The leading agent orchestration platform for Claude
...At its core, Claude-Flow integrates Dynamic Agent Architecture (DAA) for self-organizing agent management, neural pattern recognition accelerated by WebAssembly SIMD, and a SQLite-based memory system for context retention and knowledge persistence across tasks. It automates development workflows via pre- and post-operation hooks, providing seamless coordination, code formatting, validation, and performance optimization. -
22
RecursiveMAS
Offical Implementation for "Recursive Multi-Agent Systems"
RecursiveMAS is an advanced multi-agent AI framework that introduces a recursive collaboration mechanism to improve reasoning and problem-solving across multiple agents. Instead of treating agents as independent units exchanging text outputs, it connects them through a shared latent computation loop, allowing internal “thought states” to be passed and refined iteratively. This recursive structure enables agents to build on each other’s intermediate reasoning, leading to deeper and more... -
23
Context Mode
Context window optimization for AI coding agents
Context Mode is a development approach and tooling concept that enhances how AI-assisted coding environments manage and inject context into language model interactions. It focuses on improving the relevance and accuracy of AI-generated outputs by controlling what information is provided to the model at each step. The project explores structured context management, enabling developers to define how files, code snippets, and metadata are included in prompts. It is particularly useful for large... -
24
Desloppify
Agent harness to make your slop code well-engineered and beautiful
Desloppify is a utility-focused project aimed at improving the quality, structure, and clarity of generated or written text by removing redundancy, noise, and unnecessary verbosity. It is designed to “clean up” outputs, particularly those produced by AI systems, making them more concise, readable, and professional. The system likely applies heuristics or transformation rules to identify repetitive patterns, filler content, and stylistic inconsistencies. This makes it especially useful in... -
25
oh-my-claudecode
Teams-first Multi-agent orchestration for Claude Code
oh-my-claudecode is a multi-agent orchestration framework designed to dramatically simplify and enhance the use of Claude Code by providing a zero-configuration, high-level interface for managing complex AI-driven development workflows. The project abstracts away the complexity of interacting with multiple AI agents by introducing a “team-based” execution model, where tasks are automatically decomposed into planning, execution, verification, and iteration stages. It emphasizes natural...
