Search Results for "frameworks" - Page 11
Sort By:
Showing 577 open source projects for "frameworks"
View related business solutions-
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.
-
$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.
-
1
DataDreamer
DataDreamer: Prompt. Generate Synthetic Data. Train & Align Models
DataDreamer is a tool designed to assist in the generation and manipulation of synthetic data for various applications, including testing and machine learning. -
2
Moondream
Tiny vision language model
...The project typically showcases procedural visualizations, algorithmic designs, and artistic experiments that push the boundaries of what can be expressed with programming languages and rendering frameworks. While the exact nature can vary by commit or branch, Moondream’s work often blends geometry, color theory, and motion to create immersive visuals that can be interactive, animated, or reactive to input. It serves as both a playground for the author’s artistic curiosity and a resource for other creative coders interested in generative art techniques. ... -
3
Headroom
Compress tool outputs, logs, files, and RAG chunks
...It can compress tool outputs, logs, RAG chunks, files, and conversation history. Headroom can be used as a transparent proxy, a Python function, a TypeScript SDK, or through integrations with frameworks such as LangChain and LiteLLM. It is useful for teams building AI agents, research tools, or LLM products where context size, cost, and latency matter. -
4
mlforecast
Scalable machine learning for time series forecasting
...It supports multi-series forecasting, meaning you can train one model that forecasts many time series at once (common in retail, demand forecasting, etc.), rather than one model per series. The library is built to scale: behind the scenes, it can leverage distributed computing frameworks (Spark, Dask, Ray) when datasets or the number of series grow large. -
Error to trace to log to deploy. One click. No SSH.AppSignal links every error to the trace, the trace to the log, the log to the deploy that shipped it.
-
5
LLMs-from-scratch
Implement a ChatGPT-like LLM in PyTorch from scratch, step by step
...It emphasizes building blocks—tokenization, embeddings, attention, feed-forward layers, normalization, and training loops—so learners understand not just how to use a model but how it works internally. The repository favors clear Python and NumPy or PyTorch implementations that can be run and modified without heavyweight frameworks obscuring the logic. Chapters and notebooks progress from tiny toy models to more capable transformer stacks, including sampling strategies and evaluation hooks. The focus is on readability, correctness, and experimentation, making it ideal for students and practitioners transitioning from theory to working systems. By the end, you have a grounded sense of how data pipelines, optimization, and inference interact to produce fluent text. -
6
Transformer Engine
A library for accelerating Transformer models on NVIDIA GPUs
...As the number of parameters in Transformer models continues to grow, training and inference for architectures such as BERT, GPT, and T5 become very memory and compute-intensive. Most deep learning frameworks train with FP32 by default. This is not essential, however, to achieve full accuracy for many deep learning models. -
7
Milvus Bootcamp
Dealing with all unstructured data, such as reverse image search
Milvus Bootcamp is a collection of tutorials, examples, and best practices for using Milvus, an open-source vector database designed for AI-powered similarity search and retrieval applications. -
8
AI Engineering from Scratch
Learn it. Build it. Ship it for others
AI Engineering from Scratch is a comprehensive open-source curriculum designed to teach artificial intelligence by building every component from first principles rather than relying on prebuilt frameworks. The project is structured into more than 20 phases and hundreds of lessons, covering topics that range from foundational mathematics to advanced systems such as large language models, retrieval pipelines, and multi-agent architectures. Each lesson emphasizes hands-on implementation, requiring learners to write core components such as backpropagation, tokenizers, and attention mechanisms themselves before using higher-level tools. ... -
9
mini SWE-agent
The 100 line AI agent that solves GitHub issues
mini SWE-agent is a lightweight, minimalist AI-powered software engineering agent designed to autonomously solve GitHub issues and assist developers directly from the command line using large language models. Unlike more complex frameworks, it emphasizes simplicity and efficiency, consisting of roughly 100 lines of code while still achieving strong performance on benchmarks such as SWE-bench Verified, where it demonstrates competitive problem-solving capabilities. The agent operates by interpreting software issues, analyzing repository context, and executing actions such as editing code, running commands, and validating fixes through iterative reasoning loops. ... -
Build Securely on AWS with Proven FrameworksMoving to the cloud brings new challenges. How can you manage a larger attack surface while ensuring great network performance? Turn to Fortinet’s Tested Reference Architectures, blueprints for designing and securing cloud environments built by cybersecurity experts. Learn more and explore use cases in this white paper.
-
10
A.I.G
Full-stack AI Red Teaming platform
...It brings together AI infrastructure vulnerability scanning, MCP server risk analysis, and jailbreak evaluation into a unified workflow so that enterprises and individuals can identify critical security issues without relying on external services. Users can deploy it via Docker or scripts to get a modern web UI that guides them through tasks like scanning third-party frameworks for known CVEs and experimenting with prompt security against attack vectors. The tool provides both a visual interface and a comprehensive API, making integration with internal security systems or CI/CD pipelines practical for ongoing risk management. -
11
Context Engineering
A frontier, first-principles handbook
Context Engineering is a comprehensive, open-source project serving as a first-principles handbook for the emerging discipline of context design and optimization in AI. Moving beyond traditional prompt engineering, this repository defines and explores how to craft and provide complete context payloads — not just single prompts — to large language models so they can perform tasks more reliably and intelligently. It takes inspiration from thought leaders like Andrej Karpathy and bridges theory... -
12
AReal
Lightning-Fast RL for LLM Reasoning and Agents. Made Simple & Flexible
AReaL is an open source, fully asynchronous reinforcement learning training system. AReal is designed for large reasoning and agentic models. It works with models that perform reasoning over multiple steps, agents interacting with environments. It is developed by the AReaL Team at Ant Group (inclusionAI) and builds upon the ReaLHF project. Release of training details, datasets, and models for reproducibility. It is intended to facilitate reproducible RL training on reasoning / agentic tasks,... -
13
Agent Payments Protocol (AP2)
Building a Secure and Interoperable Future for AI-Driven Payments
AP2 is a project released by Google’s “Agentic Commerce” initiative, focusing on a protocol and reference implementation for agent-driven or AI-mediated payments. In effect, AP2 aims to define a secure, interoperable protocol that allows software agents to act on behalf of users—making payments or shopping decisions autonomously—while preserving necessary security, auditability, and trust. The repository contains sample scenarios (in Python, Android, etc.) that illustrate how agents,... -
14
Agentic RAG for Dummies
A modular Agentic RAG built with LangGraph
...Instead of relying on static retrieval pipelines, the system shows how agents can orchestrate retrieval, reasoning, and tool usage in a more flexible decision loop. The repository provides practical examples and tutorials that guide developers through building agentic RAG systems using modern AI frameworks. These examples illustrate how agents can access knowledge bases, retrieve documents, analyze them, and refine their queries during multi-step reasoning processes. The repository focuses on simplifying complex architectural concepts so that beginners can understand how agentic retrieval systems are constructed. -
15
TensorFlow Datasets
TFDS is a collection of datasets ready to use with TensorFlow,
TensorFlow Datasets is a collection of datasets ready to use, with TensorFlow or other Python ML frameworks, such as Jax. All datasets are exposed as tf.data. Datasets , enabling easy-to-use and high-performance input pipelines. To get started see the guide and our list of datasets. -
16
Finance
150+ quantitative finance Python programs
...The project focuses on explaining key principles used in finance and investment analysis, including topics such as financial statements, valuation models, portfolio theory, and financial markets. The repository is designed as a study reference for students and professionals who want to understand financial systems and the analytical frameworks used in financial decision-making. It organizes concepts into structured documents that explain both theoretical principles and practical calculations used in finance. The materials often include definitions, formulas, conceptual explanations, and examples to help readers understand how financial models and instruments function in real markets. -
17
Artificial Intelligence for Beginners
12 Weeks, 24 Lessons, AI for All
...It covers a broad range of topics including neural networks, computer vision, natural language processing, and AI ethics. The curriculum is intentionally beginner-friendly while still exposing learners to widely used frameworks such as TensorFlow and PyTorch. It also supports many languages, making the material accessible to a global audience. Overall, the project functions as a complete self-paced learning pathway for students, educators, and developers who want a practical introduction to modern AI concepts. -
18
Medeo Video Generator
AI-powered video generation skill for OpenClaw
Medeo Video Generator is an AI-driven project designed to enable advanced video processing and generation capabilities within agent-based or automation systems. It provides a “skill” module that can be integrated into AI agents, allowing them to create, edit, and manipulate video content programmatically. The project focuses on bridging the gap between language-based AI systems and multimedia outputs by enabling models to produce structured video content as part of their workflows. It... -
19
Tencent-Hunyuan-Large
Open-source large language model family from Tencent Hunyuan
Tencent-Hunyuan-Large is the flagship open-source large language model family from Tencent Hunyuan, offering both pre-trained and instruct (fine-tuned) variants. It is designed with long-context capabilities, quantization support, and high performance on benchmarks across general reasoning, mathematics, language understanding, and Chinese / multilingual tasks. It aims to provide competitive capability with efficient deployment and inference. FP8 quantization support to reduce memory usage... -
20
AgentOps
Python SDK for agent monitoring, LLM cost tracking, benchmarking, etc.
...Rewind and replay agent runs with point-in-time precision. Keep a full data trail of logs, errors, and prompt injection attacks from prototype to production. Native integrations with the top agent frameworks. -
21
Hermes Web UI
The best way to use Hermes Agent from the web or from your phone
...The interface allows users to manage agent sessions, configure models, and interact with persistent memory systems directly from a web environment. It is built using simple technologies like Python and vanilla JavaScript, avoiding complex frontend frameworks. The UI supports real-time interaction, context tracking, and visualization of token usage. It connects to a self-hosted agent that continuously learns and evolves over time. The project emphasizes usability, accessibility, and seamless integration with existing workflows. -
22
autoMate
AI tool for automating desktop tasks via natural language input
autoMate is an AI-powered local automation tool designed to enable users to control and automate their computers using natural language instructions instead of traditional scripting or rule-based systems. It combines large language models with computer vision techniques to interpret user intent and understand on-screen content, allowing it to interact with graphical interfaces similarly to a human user. autoMate follows an observe-decide-act workflow, where it analyzes the screen, plans... -
23
Happy-LLM
Large Language Model Principles and Practice Tutorial from Scratch
Happy-LLM is an open-source educational project created by the Datawhale AI community that provides a structured and comprehensive tutorial for understanding and building large language models from scratch. The project guides learners through the entire conceptual and practical pipeline of modern LLM development, starting with foundational natural language processing concepts and gradually progressing to advanced architectures and training techniques. It explains the Transformer... -
24
Minigrid
Simple and easily configurable grid world environments
...The design emphasizes speed (agents can run thousands of steps per second), low dependency overhead, and high customizability — making it easy to define new maps, new tasks, or wrappers. It supports the Gymnasium-style environment API so that RL researchers can plug it into their existing frameworks and algorithms with minimal adaptation. Because of its simplicity, it is often used for rapid prototyping, analytic experiments, curriculum learning, or pedagogical tutorials. While it is not a full 3D simulation environment, its strength lies in enabling many environment resets and steps cheaply, which is valuable for algorithmic RL research rather than high-fidelity rendering. -
25
nesa
Run AI models end-to-end encrypted
...It integrates mechanisms for verifiable computation, enabling users to confirm that AI outputs were generated correctly without exposing underlying data or models. The platform is designed to be modular and extensible, supporting integration with various machine learning frameworks and deployment environments.
