Search Results for "model-builder" - Page 34
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Showing 2224 open source projects for "model-builder"
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1
Preswald
Python tool for browser-based interactive data apps in one file
...Preswald emphasizes a code-first workflow where users define applications entirely in Python while using built-in UI components such as tables, charts, and forms. It also includes a reactive execution model that only recomputes necessary parts of the app, improving performance and responsiveness. -
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Ultravox
Fast multimodal LLM for real-time voice interaction and AI apps
Ultravox is an open source multimodal large language model designed specifically for real-time voice-based interactions. It is built to process both text and spoken audio directly, eliminating the need for a separate speech recognition stage and enabling more seamless conversational experiences. Ultravox works by combining text prompts with encoded audio inputs, allowing it to understand spoken language alongside written instructions in a unified pipeline. -
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DeepReasoning
High-performance API combining reasoning and creative AI models
DeepReasoning is a high-performance large language model inference API designed to unify advanced reasoning and creative generation capabilities into a single system. It combines DeepSeek R1’s chain-of-thought reasoning with Claude’s strengths in code generation and conversational output, enabling more capable and balanced responses. DeepReasoning provides both an API and a chat interface, allowing developers and users to interact with the combined models in a streamlined way. ... -
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WhatsApp MCP Server
WhatsApp MCP server enabling AI access to chats and messaging
whatsapp-mcp is an open source Model Context Protocol (MCP) server that enables AI agents to interact directly with a user’s WhatsApp account through a structured interface. It acts as a bridge between WhatsApp and large language models, allowing controlled access to messages, chats, and contacts. whatsapp-mcp is composed of two main components: a Go-based bridge that connects to the WhatsApp Web API and stores data locally, and a Python-based MCP server that exposes tools for AI interaction. ... -
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.
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Everywhere
Context-aware desktop AI assistant that understands screen content
...It can analyze on-screen information in real time and provide contextual responses, making it useful for tasks like troubleshooting errors, summarizing articles, translating text, and refining written content. It integrates with multiple large language model providers and supports various tools, enabling flexible and extensible AI-powered workflows. Everywhere features a modern design with interactive elements such as markdown rendering, keyboard shortcuts, and voice input capabilities. Additionally, the project emphasizes seamless workflow integration by operating alongside existing applications rather than requiring users to switch. -
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ADK Go
Code-first Go toolkit for building, evaluating, and deploying AI agent
...It is designed to be modular and flexible, allowing developers to integrate custom tools, external services, or existing functionality into agent workflows. Although optimized for certain AI ecosystems, ADK-Go is model-agnostic and can work with different models and deployment environments. -
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TypeChat
Library for building type-safe natural language interfaces with LLMs
TypeChat is an open source library developed by Microsoft that simplifies the creation of natural language interfaces by using type definitions to structure interactions with large language models. Traditional natural language interfaces often relied on complex decision trees to interpret user intent and gather required inputs. With the rise of large language models, developers can interpret user requests more easily, but they still face challenges related to output reliability, safety, and... -
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MLE-bench
AI multi-agent framework for automating data-driven R&D workflows
...By iterating through these stages, the framework continuously refines models and strategies using feedback from previous results. RD-Agent focuses heavily on automating complex tasks such as feature engineering, model design, and experimentation, which are traditionally time-consuming in machine learning and quantitative research workflows. RD-Agent can analyze data, generate experimental code, run evaluations, and learn from outcomes to improve future iterations. -
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AiLearning-Theory-Applying
Quickly get started with AI theory and practical applications
AiLearning-Theory-Applying is a comprehensive educational repository designed to help learners quickly understand artificial intelligence theory and apply it in practical machine learning and deep learning projects. The repository provides extensive tutorials covering mathematical foundations, machine learning algorithms, deep learning concepts, and modern large language model architectures. It includes well-commented notebooks, datasets, and implementation examples that allow learners to reproduce experiments and understand the inner workings of various algorithms. The project also introduces important concepts such as probability theory, linear algebra, regression models, clustering methods, and neural network architectures. ... -
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face.evoLVe
High-Performance Face Recognition Library on PaddlePaddle & PyTorch
...The project provides a comprehensive framework for building and training modern face recognition models using deep learning architectures. It includes components for face alignment, landmark localization, data preprocessing, and model training pipelines that allow developers to construct end-to-end facial recognition systems. 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. ... -
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AI Engineer Headquarters
A collection of scientific methods, processes, algorithms
...Rather than focusing only on theoretical knowledge, the repository emphasizes applied learning and encourages engineers to build real systems that incorporate machine learning, large language models, data pipelines, and AI infrastructure. The curriculum includes a progression of topics such as foundational AI engineering skills, machine learning systems design, large language model usage, retrieval-augmented generation systems, model fine-tuning, and autonomous AI agents. It also promotes disciplined learning routines and project-based practice so learners can develop practical experience and build deployable solutions. -
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AutoTrain Advanced
Faster and easier training and deployments
...It supports a wide range of tasks including text classification, sequence-to-sequence modeling, token classification, sentence embedding training, and large language model fine-tuning. The system integrates closely with the Hugging Face ecosystem and allows developers to train models using datasets hosted on the Hugging Face Hub. AutoTrain Advanced can run locally or in cloud environments, making it adaptable to different computational setups. By automating tasks such as model configuration, hyperparameter selection, and training pipelines, the project significantly reduces the technical barrier to building AI systems. -
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Advanced AI explainability for PyTorch
Advanced AI Explainability for computer vision
...It also provides metrics and evaluation tools that help measure the reliability and quality of the generated explanations. By integrating easily with PyTorch models, the library allows developers to diagnose model errors, detect biases in datasets, and improve model transparency. -
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Prometheus-Eval
Evaluate your LLM's response with Prometheus and GPT4
...The project provides tools, datasets, and scripts that allow developers and researchers to measure the quality of LLM responses through automated scoring rather than relying solely on human evaluators. It implements an “LLM-as-a-judge” approach in which a dedicated language model analyzes instruction–response pairs and assigns scores or rankings based on predefined evaluation criteria. The repository includes a Python package that provides a straightforward interface for running evaluations and integrating them into model development pipelines. It also provides training data and utilities for fine-tuning evaluator models so they can assess outputs according to custom scoring rubrics such as helpfulness, accuracy, or style. -
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LLM-Pruner
On the Structural Pruning of Large Language Models
...LLM-Pruner addresses this issue by identifying and removing non-essential components within transformer architectures, such as redundant attention heads or feed-forward structures. The framework relies on gradient-based analysis to determine which parameters contribute least to model performance, enabling targeted structural pruning rather than simple weight removal. After pruning, the framework applies lightweight fine-tuning methods such as LoRA to recover performance using relatively small datasets and short training times. -
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RAG from Scratch
Demystify RAG by building it from scratch
...The project walks through key concepts such as generating embeddings, building vector databases, retrieving relevant documents, and integrating the retrieved context into language model prompts. Each example is written with detailed explanations so that developers can understand the internal mechanics of semantic search and context-aware language generation. The repository emphasizes learning through direct implementation, allowing users to see how each component of the RAG architecture functions independently. -
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Jlama
Jlama is a modern LLM inference engine for Java
...This allows organizations to integrate generative AI features into their systems while maintaining full control over data privacy and infrastructure. The engine supports a wide range of open-source model architectures and formats, including variants of Llama, Mistral, and other transformer-based models. It provides tools for running chat interactions, completing prompts, or exposing an OpenAI-compatible REST API for applications that expect standard LLM endpoints. The project focuses on performance and portability by using native Java optimizations and the Java Vector API to accelerate inference workloads. -
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Agent Development Kit (ADK) for Java
An open-source, code-first Java toolkit
...ADK is designed to be flexible and modular so that developers can build simple automation agents or large distributed agent systems depending on their needs. While it integrates well with Google’s AI ecosystem, the framework is designed to remain model-agnostic and compatible with different machine learning platforms. -
19
ReCall
Learning to Reason with Search for LLMs via Reinforcement Learning
...The project builds on earlier work focused on teaching models how to search for information during reasoning tasks and extends that idea to a broader system where models can call a variety of external tools such as APIs, databases, or computation engines. Instead of relying purely on static knowledge stored inside the model, ReCall allows the language model to dynamically decide when it should retrieve information or invoke external capabilities during the reasoning process. The framework uses reinforcement learning to train models to perform these tool calls effectively while solving multi-step reasoning tasks. -
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TAME LLM
Traditional Mandarin LLMs for Taiwan
...These models are designed to support applications such as conversational AI, knowledge retrieval, and domain-specific reasoning in fields like manufacturing, law, healthcare, and electronics. The training pipeline leverages high-performance computing infrastructure and frameworks such as NVIDIA NeMo and Megatron to enable large-scale model training. Taiwan-LLM aims to improve language understanding and generation for Traditional Mandarin users by incorporating region-specific datasets and evaluation benchmarks. -
21
uzu
A high-performance inference engine for AI models
...Written primarily in Rust and leveraging Apple’s Metal framework, the project focuses on maximizing performance when executing large language models and other AI workloads on devices such as Mac computers with M-series chips. The engine implements a hybrid architecture in which model layers can be executed either as custom GPU kernels or through Apple’s MPSGraph API, allowing it to balance performance and compatibility depending on the workload. By utilizing Apple’s unified memory architecture, uzu reduces memory copying overhead and improves inference throughput for local AI workloads. The system includes a simple high-level API that enables developers to run models, create inference sessions, and generate outputs with minimal configuration. -
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LLM Colosseum
Benchmark LLMs by fighting in Street Fighter 3
LLM-Colosseum is an experimental benchmarking framework designed to evaluate the capabilities of large language models through gameplay interactions rather than traditional text-based benchmarks. The system places language models inside the environment of the classic video game Street Fighter III, where they must interpret the game state and decide which actions to perform during combat. This setup creates a dynamic environment that tests reasoning, situational awareness, and decision-making... -
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WFGY 3.0
A tension reasoning engine over 131 S-class problems
WFGY is an experimental open-source reasoning framework designed to improve the reliability and interpretability of large language model outputs through structured reasoning layers. The project introduces a conceptual reasoning engine that analyzes complex problems by identifying semantic compression errors and residual assumptions within a system’s reasoning process. Its architecture treats reasoning failures as measurable signals that can be detected and analyzed rather than simply observed as incorrect answers. ... -
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ModernBERT
Bringing BERT into modernity via both architecture changes and scaling
...The goal of the project is to bring BERT-style models up to date with the capabilities of modern large language models while preserving the strengths of bidirectional encoder architectures used for tasks such as classification, retrieval, and semantic search. ModernBERT introduces architectural improvements that enhance both training efficiency and inference performance, making the model more suitable for modern large-scale machine learning pipelines. The repository also includes FlexBERT, a modular framework that allows developers to experiment with different encoder building blocks and configurations when constructing new models. -
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DevDocs by CyberAGI
Completely free, private, UI based Tech Documentation MCP server
DevDocs is an open-source documentation server designed to provide developers with a private, structured interface for browsing and interacting with technical documentation using AI tools. The system functions as a Model Context Protocol (MCP) server that allows large language models and developer assistants to access technical documentation in a structured and efficient way. Instead of sending entire documents to a language model, DevDocs organizes documentation into sections so that only the most relevant portions are retrieved during a query. ...
