Search Results for "mac framework" - Page 16
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Showing 1369 open source projects for "mac framework"
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Guardrails
Framework for validating and controlling LLM outputs in AI apps
Guardrails is an open source Python framework designed to help developers build more reliable and controlled applications powered by large language models. It provides mechanisms for validating and constraining both the inputs sent to a model and the outputs generated by it, helping reduce risks such as harmful content, prompt injection, or inaccurate responses. Guardrails works by applying configurable guards that intercept and evaluate interactions with the model before results are... -
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hls4ml
Machine learning on FPGAs using HLS
hls4ml is an open-source framework that enables machine learning models to be implemented directly on hardware such as FPGAs and ASICs using high-level synthesis techniques. The system converts trained neural network models from common machine learning frameworks into hardware description code suitable for ultra-low-latency inference. This approach allows machine learning algorithms to run directly on specialized hardware, making them suitable for applications that require extremely fast... -
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FISSURE
The RF and reverse engineering framework for everyone
FISSURE is an open-source radio frequency analysis and signal intelligence framework built to support software-defined radio research, wireless security experimentation, and protocol reverse engineering. The project brings together tools for capturing, inspecting, decoding, replaying, and analyzing RF signals across a wide range of wireless technologies. It is designed as a practical environment for researchers and operators who need to move from raw spectrum observation to structured... -
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Rogue
AI Agent Evaluator & Red Team Platform
Rogue is an open-source evaluation and red-team framework designed to test the reliability, safety, and policy compliance of AI agents. The platform automatically interacts with an AI agent by generating dynamic scenarios and multi-turn conversations that simulate real-world interactions. Instead of relying solely on static test scripts, Rogue uses an agent-as-a-judge architecture where one agent probes another agent to detect failures or unexpected behaviors. The system allows developers to... -
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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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RLHF-Reward-Modeling
Recipes to train reward model for RLHF
RLHF-Reward-Modeling is an open-source research framework focused on training reward models used in reinforcement learning from human feedback for large language models. In RLHF pipelines, reward models are responsible for evaluating generated responses and assigning scores that guide the model toward outputs that better match human preferences. The repository provides training recipes and implementations for building reward and preference models using modern machine learning frameworks. It... -
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ModernBERT
Bringing BERT into modernity via both architecture changes and scaling
ModernBERT is an open-source research project that modernizes the classic BERT encoder architecture by incorporating recent advances in transformer design, training techniques, and efficiency improvements. 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... -
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Agentless
An agentless approach to automatically solve software development
Agentless is an open-source framework that applies large language models to automatically resolve software development issues without relying on complex autonomous agent systems. The project proposes an alternative approach to AI-driven code repair that avoids the overhead of multi-agent orchestration by using a structured pipeline for identifying and fixing bugs. When solving a problem, the system first performs localization to determine which files, functions, or code segments are most... -
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MaxText
A simple, performant and scalable Jax LLM
MaxText is a high-performance, highly scalable open-source framework designed to train and fine-tune large language models using the JAX ecosystem. The project acts as both a reference implementation and a practical training library that demonstrates best practices for building and scaling transformer-based language models on modern accelerator hardware. It is optimized to run efficiently on Google Cloud TPUs and GPUs, enabling researchers and engineers to train models ranging from small... -
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InternLM-XComposer-2.5
InternLM-XComposer2.5-OmniLive: A Comprehensive Multimodal System
InternLM-XComposer is an open-source multimodal AI system designed to generate long-form content that combines text with visual elements such as images and diagrams. The model is built on top of the InternLM language model architecture and extends its capabilities to handle multimodal inputs and outputs. Instead of producing only textual responses, the system can generate visually enriched documents such as illustrated articles, presentations, and educational materials. It incorporates... -
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AgentBench
A Comprehensive Benchmark to Evaluate LLMs as Agents (ICLR'24)
AgentBench is an open-source benchmark designed to evaluate the capabilities of large language models when used as autonomous agents. Unlike traditional language model benchmarks that focus on static text tasks, AgentBench measures how models perform in interactive environments that require planning, reasoning, and decision-making. The benchmark includes multiple environments that simulate realistic scenarios such as web interaction, database querying, and problem solving tasks. These... -
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Chitu
High-performance inference framework for large language models
Chitu is a high-performance inference engine designed to deploy and run large language models efficiently in production environments. The framework focuses on improving efficiency, flexibility, and scalability for organizations that need to run LLM inference workloads across different hardware platforms. It supports heterogeneous computing environments, including CPUs, GPUs, and various specialized AI accelerators, allowing models to run across a wide range of infrastructure configurations.... -
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NExT-GPT
Code and models for ICML 2024 paper, NExT-GPT
NExT-GPT is an open-source research framework that implements an advanced multimodal large language model capable of understanding and generating content across multiple modalities. Unlike traditional models that primarily handle text, NExT-GPT supports input and output combinations involving text, images, video, and audio in a unified architecture. The system connects a large language model with multimodal encoders and diffusion-based decoders so it can interpret information from different... -
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PyTorch-Tutorial-2nd
CV, NLP, LLM project applications, and advanced engineering deployment
PyTorch-Tutorial-2nd is an open-source educational repository that provides structured tutorials for learning deep learning with the PyTorch framework. The project serves as a practical companion to a second edition of a PyTorch learning guide and is designed to help learners understand neural network concepts through hands-on coding examples. The repository covers a wide range of topics including tensor operations, neural network construction, model training workflows, and optimization... -
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AppAgent
Multimodal Agents as Smartphone Users, an LLM-based multimodal agent
AppAgent is an open-source multimodal agent framework designed to enable large language models to operate smartphone applications through natural interactions with graphical user interfaces. The system allows an AI agent to interpret visual information from the screen and translate natural language instructions into actions such as tapping, swiping, and navigating between application screens. Instead of requiring backend access to application APIs, the framework interacts with apps the same... -
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Nano-vLLM
A lightweight vLLM implementation built from scratch
Nano-vLLM is a lightweight implementation of the vLLM inference engine designed to run large language models efficiently while maintaining a minimal and readable codebase. The project recreates the core functionality of vLLM in a simplified architecture written in approximately a thousand lines of Python, making it easier for developers and researchers to understand how modern LLM inference systems work. Despite its compact design, nano-vllm incorporates advanced optimization techniques such... -
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NagaAgent
A simple yet powerful agent framework for personal assistants
NagaAgent is an experimental framework for building interactive virtual agents capable of autonomous reasoning, dialog, and task execution using components that mirror human cognitive patterns. It provides abstractions for representing goals, context, and state so that agents can plan sequences of actions, evaluate outcomes, and adjust behavior over time. The project includes mechanisms for semantic memory, reasoning pipelines, and integration points with external data sources and language... -
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Agent SOP
Natural language workflows for AI agents
Agent SOP is a framework that implements structured operational procedures (SOPs) for autonomous agents so that they can carry out complex multi-step tasks reliably and in a defined order. Instead of relying solely on broad language model reasoning, this project enforces explicit step sequences with checkpoints, conditional transitions, and rollback logic, making agent workflows more predictable and auditable. It defines reusable SOP templates that agents can instantiate with... -
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Agent Reinforcement Trainer
Train multi-step agents for real-world tasks using GRPO
Agent Reinforcement Trainer, or ART is an open-source reinforcement learning framework tailored to training large language model agents through experience, making them more reliable and performant on multi-turn, multi-step tasks. Instead of just manually crafting prompts or relying on supervised fine-tuning, ART uses techniques like Group Relative Policy Optimization (GRPO) to let agents learn from environmental feedback and reward signals. The framework is designed to integrate easily with... -
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FinRobot
An Open-Source AI Agent Platform for Financial Analysis using LLMs
FinRobot is an open-source AI framework focused on automating financial data workflows by combining data ingestion, feature engineering, model training, and automated decision-making pipelines tailored for quantitative finance applications. It provides developers and quants with structured modules to fetch market data, process time series, generate technical indicators, and construct features appropriate for machine learning models, while also supporting backtesting and evaluation metrics to... -
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Dolphin
Document Image Parsing via Heterogeneous Anchor Prompting”
Dolphin — maintained by ByteDance — is a project aimed at providing a high-performance, robust, and extensible media or multimedia framework / player infrastructure (or possibly a streaming media solution), intended to meet modern demands for efficiency, flexibility, and integration in media-heavy applications. 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... -
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Agently 4
Build GenAI application quick and easy
Agently is a Python framework for building generative-AI (“GenAI”) applications; it focuses on enabling developers to orchestrate AI agents, workflows, and event-driven logic in a robust, reusable way. With Agently, one can define agents that call different models, chain tasks, trigger workflows based on events, and switch models with minimal code changes. It abstracts away boilerplate around model API calls, tool usage, prompt management, and workflow state. The project aims at... -
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Smallpond
A lightweight data processing framework built on DuckDB and 3FS
smallpond is a lightweight distributed data processing framework built by DeepSeek, designed to scale DuckDB workloads over clusters using their 3FS (Fire-Flyer File System) backend. The idea is to preserve DuckDB’s fast analytics engine but lift it from single-node to multi-node settings, giving you the ability to operate on large datasets (e.g. petabyte scale) without moving to a heavyweight system like Spark. Users write Python-like code (via DataFrame APIs or SQL strings) to express... -
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Jupyter Enterprise Gateway
Enables Jupyter Notebooks to share resources across clusters
Jupyter Enterprise Gateway is a headless web server with a pluggable framework for anyone supporting multiple notebook users in a managed-cluster environment. Some of the core functionality it provides is better optimization of compute resources, improved multi-user support, and more granular security for your Jupyter notebook environment - making it suitable for enterprise, scientific, and academic implementations. From a technical perspective, Jupyter Enterprise Gateway is a web server... -
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Mangum
AWS Lambda support for ASGI applications
Mangum is an adapter for running ASGI applications in AWS Lambda to handle Function URL, API Gateway, ALB, and Lambda@Edge events. Event handlers for API Gateway HTTP and REST APIs, Application Load Balancer, Function URLs, and CloudFront Lambda@Edge. Compatibility with ASGI application frameworks, such as Starlette, FastAPI, Quart and Django. Support for binary media types and payload compression in API Gateway using GZip or Brotli. Works with existing deployment and configuration tools,...
