Search Results for "model-builder" - Page 27
Sort By:
Showing 6994 open source projects for "model-builder"
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.
-
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.
-
1
OpenClaw
Your own personal AI assistant. Any OS. Any Platform.
OpenClaw (formerly Clawdbot/Moltbot) is an open-source, self-hosted autonomous AI assistant designed to run on user-controlled hardware and bridge conversational natural language with real-world task execution, effectively acting as a proactive digital assistant rather than a reactive chatbot. It lets you send instructions through familiar messaging platforms like WhatsApp, Telegram, Discord, Slack, Signal, iMessage, and more, and then interprets those instructions to carry out actions such... -
2
Perfetto
Production-grade client-side tracing, profiling, and analysis
Perfetto is a production-grade tracing platform for Android, Linux, and Chrome that captures extremely detailed information about what a system is doing over time. It’s designed around a low-overhead producer/consumer model: instrumented components (“producers”) write binary events into shared memory buffers and a collector (“service”) reliably streams them to storage. The data model spans kernel and userspace, so you can stitch together CPU scheduling, app lifecycles, binder/IPC hops, GPU work, power and thermal signals, file I/O, heap samples, and more into a single coherent timeline. ... -
3
AniList MCP
AniList MCP server for accessing anime and manga data
An MCP server that interfaces with the AniList API, allowing AI clients to access and interact with anime, manga, character, staff, and user data from AniList. -
4
ArviZ.jl
Exploratory analysis of Bayesian models with Julia
ArviZ.jl (pronounced "AR-vees") is a Julia package for exploratory analysis of Bayesian models. It includes functions for posterior analysis, model checking, comparison and diagnostics. -
Your monitoring isn't a stack. It's a pile. Fix that.Replace Datadog, New Relic, and Sentry without adding three more dashboards.
-
5
bert4torch
An elegent pytorch implement of transformers
An elegant PyTorch implement of transformers. -
6
The Ocean Land Atmosphere Model (OLAM) is a new global general circulation/climate prediction model that will allow to “nest down” to any desired resolution using a flexible mesh refinement technique that works on either triangular or hexagonal g
-
7
X-AnyLabeling
Effortless data labeling with AI support from Segment Anything
X-AnyLabeling is an open-source data annotation platform designed to streamline the process of labeling datasets for computer vision and multimodal AI applications. The software integrates an AI-powered labeling engine that allows users to generate annotations automatically with the assistance of modern vision models such as Segment Anything and various object detection frameworks. It supports labeling tasks across images and videos and enables developers to prepare training datasets for... -
8
CodeGeeX2
CodeGeeX2: A More Powerful Multilingual Code Generation Model
CodeGeeX2 is the second-generation multilingual code generation model from ZhipuAI, built upon the ChatGLM2-6B architecture and trained on 600B code tokens. Compared to the first generation, it delivers a significant boost in programming ability across multiple languages, outperforming even larger models like StarCoder-15B in some benchmarks despite having only 6B parameters. The model excels at code generation, translation, summarization, debugging, and comment generation, and it supports over 100 programming languages. ... -
9
Mezzanine
CMS framework for Django
...Apart from the features that come with Django such as MVC architecture, ORM, templating and caching, Mezzanine comes with a great many other features. This includes hierarchical page navigation, a simple drag-and-drop HTML5 forms builder with CSV export, scheduled publishing, easy page ordering, social media sharing, and so much more. -
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
Keiyoushi Extensions
Extension repository for Mihon and variants
Keiyoushi Extensions is an open-source repository that provides a large collection of third-party content source extensions designed primarily for manga reading applications such as Tachiyomi-based clients. These extensions function as connectors that allow users to browse, search, and read content from a wide range of external websites within a unified app interface. The project is structured to support modular installation, enabling users to selectively add or remove sources based on their... -
11
LOTUS
AI-Powered Data Processing: Use LOTUS to process all of your datasets
LOTUS is an open-source framework and query engine designed to enable efficient processing of structured and unstructured datasets using large language models. The system provides a declarative programming model that allows developers to express complex AI data operations using high-level commands rather than manually orchestrating model calls. It offers a Python interface with a Pandas-like API, making it familiar for data scientists and engineers already working with data analysis libraries. The core concept of the framework is the use of semantic operators, which extend traditional relational database operations to support reasoning over text and other unstructured data. ... -
12
SAM 2
The repository provides code for running inference with SAM 2
SAM2 is a next-generation version of the Segment Anything Model (SAM), designed to improve performance, generalization, and efficiency in promptable image segmentation tasks. It retains the core promptable interface—accepting points, boxes, or masks—but incorporates architectural and training enhancements to produce higher-fidelity masks, better boundary adherence, and robustness to complex scenes. -
13
Qwen
The official repo of Qwen chat & pretrained large language model
...These models, which range from smaller to larger configurations, are designed for a wide range of natural language processing tasks. They are openly available for research and commercial use, with Qwen's code and model weights shared on GitHub. Qwen's capabilities include text generation, comprehension, and conversation, making it a versatile tool for developers looking to integrate advanced AI functionalities into their applications. -
14
Open Code Review
Open-source & free, battle-tested at Alibaba's scale
...It is designed to go beyond shallow diff feedback by letting the agent inspect full files, search the codebase, and use surrounding context. Developers can configure model endpoints, making it adaptable to different LLM providers and deployment needs. Its main value is bringing large-scale, agent-assisted review workflows into local and CI-based development environments. -
15
VGGT-Ω
[CVPR 2026 Oral] VGGT Omega
...The project is associated with 3D understanding workflows where models infer scene geometry without a traditional multi-stage reconstruction pipeline. It includes pretrained model variants with different resolutions and text-alignment capabilities, though checkpoint access may require approval. The repository also provides a Gradio demo that can visualize predicted cameras and depth-unprojected point clouds as a GLB scene. VGGT-Omega is best suited for researchers and developers working on 3D reconstruction, visual geometry, and image-based scene understanding. -
16
DeepSeek TUI
Coding agent for DeepSeek models that runs in your terminal
...It provides a text-based chat experience directly within the command line, making it ideal for developers who prefer minimal interfaces. The tool supports streaming responses, allowing real-time interaction with the model. It includes features for managing prompts, sessions, and conversation history within the terminal environment. DeepSeek-TUI emphasizes speed and simplicity, avoiding heavy graphical dependencies. It is particularly useful for local or remote environments where graphical interfaces are impractical. Overall, it delivers an efficient CLI-first experience for AI interaction. -
17
Elixir LangChain
Elixir implementation of a LangChain style framework
...The library focuses on enabling composable workflows where different processes, APIs, and services can be connected into structured pipelines driven by LLMs. It mirrors the architecture of traditional LangChain frameworks by providing abstractions for chains, tools, agents, and model integrations while adapting them to Elixir’s concurrency model. This makes it particularly well-suited for building scalable, real-time AI systems that benefit from Elixir’s fault tolerance and lightweight process management. The framework supports multiple providers and includes features such as streaming responses, tool calling, and structured outputs, allowing developers to create complex AI-driven workflows. -
18
TurboQuant+
Implementation of TurboQuant (ICLR 2026)
TurboQuant Plus is an extended and enhanced version of quantization tooling aimed at improving neural network efficiency through advanced compression and optimization strategies. It builds upon the concept of reducing model precision to accelerate inference while attempting to maintain or recover accuracy through refined techniques. The project explores additional enhancements such as improved calibration, adaptive quantization, and potentially hybrid precision approaches that combine multiple levels of compression. It is designed to be used in conjunction with modern machine learning workflows, particularly those involving large models that require optimization for deployment. ... -
19
CUDA-Q
C++ and Python support for the CUDA Quantum programming model
CUDA-Q is an open-source platform for developing hybrid quantum-classical applications using a unified programming model across CPUs, GPUs, and quantum processing units. It provides a full toolchain that includes compilers, runtimes, and libraries for writing quantum programs in both C++ and Python. The platform is designed to be hardware-agnostic, allowing developers to run applications on different quantum backends or simulate them efficiently using GPU acceleration when physical quantum hardware is unavailable. ... -
20
DecryptPrompt
Summarize Prompt & LLM papers, open source data & models
...Many of the resources focus on understanding how prompts influence model behavior and how prompting strategies can improve reasoning or efficiency. -
21
OpenUI
OpenUI let's you describe UI using your imagination
...Users can iteratively refine designs through conversational prompts and convert generated layouts into frameworks like React or Web Components. OpenUI supports multiple model providers through LiteLLM, making it flexible across different AI ecosystems. The system is designed both as a practical prototyping tool and as a research platform for building next-generation AI-powered developer experiences. Its architecture separates frontend rendering from backend model orchestration, enabling extensibility and customization. ... -
22
Plano
Delivery infrastructure for agentic apps
Plano is an AI-native proxy and data plane designed to simplify the infrastructure required to deploy and operate agentic applications in production environments. It removes repetitive plumbing work from application code by centralizing capabilities such as agent routing, orchestration, guardrails, observability, and model selection. Built on modern proxy technology and compatible with any language or AI framework, Plano enables developers to focus on core agent logic instead of infrastructure complexity. The system provides intelligent LLM routing APIs that support model agility, along with filter chains for safety, moderation, and memory hooks. It also exposes rich traces, metrics, and logs to support continuous improvement of agent behavior in production. ... -
23
TurboDiffusion
100–200× Acceleration for Video Diffusion Models
...The project targets large video models and enables developers to run accelerated generation even on single high-end GPUs, making fast video synthesis more practical for research and creative workflows. TurboDiffusion is structured to integrate with existing diffusion model architectures and provides tools for experimenting with and benchmarking speed and quality trade-offs. -
24
Open Responses
Specification for multi-provider, interoperable LLM interfaces
Open Responses is an open-source implementation of an API compatible with the OpenAI Responses API that lets developers self-host a drop-in alternative endpoint for AI interactions while preserving compatibility with existing Agents SDKs and model workflows. It enables you to run a local or private server that speaks the standard Responses API, so tools, applications, and agents built against that API can operate without contacting OpenAI’s cloud and can instead route calls to any large language model provider you choose, such as Claude, Qwen, Ollama, or others. This makes it a powerful option for teams or individuals who want full control over their AI infrastructure, prioritize privacy, or need to standardize inference calls across multiple backends without rewriting their code. -
25
LLMs-from-scratch
Implement a ChatGPT-like LLM in PyTorch from scratch, step by step
LLMs-from-scratch is an educational codebase that walks through implementing modern large-language-model components 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. ...
