Search Results for "human" - Page 2
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Showing 346 open source projects for "human"
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The Alignment Handbook
Robust recipes to align language models with human and AI preferences
The Alignment Handbook is an open-source resource created to provide practical guidance for aligning large language models with human preferences and safety requirements. The project focuses on the post-training stage of model development, where models are refined after pre-training to behave more helpfully, safely, and reliably in real-world applications. It provides detailed training recipes that explain how to perform tasks such as supervised fine-tuning, preference modeling, and reinforcement learning from human feedback. ... -
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CloakBrowser
Stealth Chromium for developers and browser automation.
...Run CloakBrowser locally or self-hosted on Windows, macOS, Linux, Docker, and VPS infrastructure. Features include persistent profiles, reproducible browser identities, proxy-aware configuration, human-like interaction timing, and CDP server mode. This repository contains the MIT-licensed Python, JavaScript, and .NET wrappers. The custom Chromium binary is downloaded separately and distributed under a proprietary license. Proxies and CAPTCHA-solving services are not included. Official website and pricing: https://cloakbrowser.dev/ Official SourceForge product profile: https://sourceforge.net/software/product/CloakBrowser/ -
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HumanEval
Code for the paper "Evaluating Large Language Models Trained on Code"
...Researchers can use the dataset to run reproducible comparisons across models and track improvements in functional code synthesis. By focusing on correctness through execution, human-eval provides a rigorous and practical way to evaluate programming capabilities in AI systems. -
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KaTrain
Improve your Baduk skills by training with KataGo
...It is designed to help players of all skill levels improve by identifying mistakes, analyzing move efficiency, and offering alternative strategies based on AI evaluation. The application allows users to play against AI opponents with adjustable difficulty, including intentionally weakened versions of the engine that simulate human-like play styles. One of its key strengths is its ability to generate detailed post-game analyses, highlighting the moves that resulted in the greatest loss of points and suggesting improvements. KaTrain also includes interactive learning features such as retrying moves, exploring variations, and visualizing territory control probabilities. -
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deepface
A Lightweight Face Recognition and Facial Attribute Analysis
...It is a hybrid face recognition framework wrapping state-of-the-art models: VGG-Face, FaceNet, OpenFace, DeepFace, DeepID, ArcFace, Dlib, SFace and GhostFaceNet. Experiments show that human beings have 97.53% accuracy on facial recognition tasks whereas those models already reached and passed that accuracy level. -
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Concordia
Crowdsourcing platform for full text transcription and tagging
Concordia is a platform for crowdsourcing transcription and tagging of text in digitized images. It was developed by the Library of Congress so that volunteers of all backgrounds could transcribe and tag digitized images of manuscripts and typed materials from the Library’s collections that could not otherwise be done by optical character recognition. -
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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. ... -
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autoresearch
AI agents autonomously run and improve ML experiments overnight
autoresearch is an experimental framework that enables AI agents to autonomously conduct machine learning research by iteratively modifying and training models. Created by Andrej Karpathy, the project allows an agent to edit the model training code, run short experiments, evaluate results, and repeat the process without human intervention. Each experiment runs for a fixed five-minute training window, enabling rapid iteration and consistent comparison across architectural or hyperparameter changes. The system centers on a simple workflow where the agent modifies a single training file while human researchers guide the process through a program.md instruction file. ... -
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Timbal
Simple, performant, battle-tested framework for building reliable AI
...Developers define behavior with familiar async functions, Pydantic validation, and composable tools instead of relying on hidden orchestration. The framework supports multiple model providers and can automatically fail over between configured models. Human approval gates can pause runs, preserve their state, and resume them after a process restart. Built-in capabilities cover persistent memory, context compaction, structured outputs, reusable skills, MCP servers, tracing, and evaluations. Applications can run locally, be self-hosted, or be deployed through the Timbal platform with an accompanying API and user interface. -
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AgentScope
Build and run agents you can see, understand and trust
...It provides essential abstractions that evolve with advancing LLM capabilities, emphasizing reasoning, tool use, and flexible orchestration rather than rigid prompt constraints. With built-in support for ReAct agents, memory, planning, human-in-the-loop control, and real-time voice interaction, developers can create powerful agents in minutes. AgentScope integrates seamlessly with tools, long-term memory systems, MCP, A2A (Agent-to-Agent) protocols, and observability frameworks. It also supports reinforcement learning workflows for tuning agents and improving performance across complex tasks. ... -
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AG-UI
The Agent-User Interaction Protocol
...Instead of treating an AI agent as a black-box chat endpoint, AG-UI defines structured events for messages, tool calls, state changes, lifecycle updates, and user interactions. This makes it easier for developers to build agent-powered apps that stream progress, request human input, update UI state, and coordinate complex workflows. The project is especially useful for teams building copilots, workflow assistants, multi-agent products, or custom AI interfaces. Overall, AG-UI provides a shared communication layer between autonomous systems and the interfaces where people actually use them. -
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DeepVariant
DeepVariant is an analysis pipeline that uses a deep neural networks
...See this page for more details and instructions on how to run DeepTrio. Out-of-the-box use for PCR-positive samples and low quality sequencing runs, and easy adjustments for different sequencing technologies and non-human species. -
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SleepFM-Clinical
Improve human sleep through scientifically
SleepFM-Clinical is a specialized version of SleepFM designed for clinical and research environments, offering an adaptive audio modulation system aimed at improving human sleep through scientifically guided soundscapes. Rather than simply playing static white noise or ambient tracks, it uses a closed-loop, frequency-modulated framework that responds to user-specific sleep patterns and physiological signals to tailor sound in ways that can enhance sleep onset and depth. The clinical release includes additional features for controlled experimentation, such as logging capabilities, adjustable parameter sets, and protocols suitable for sleep studies and therapeutic settings. ... -
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Sapiens
High-resolution models for human tasks
Sapiens is a research framework from Meta AI focused on embodied intelligence and human-like multimodal learning, aiming to train agents that can perceive, reason, and act in complex environments. It integrates sensory inputs such as vision, audio, and proprioception into a unified learning architecture that allows agents to understand and adapt to their surroundings dynamically. The project emphasizes long-horizon reasoning and cross-modal grounding—connecting language, perception, and action into a single agentic model capable of following abstract goals. ... -
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jrnl
Collect your thoughts and notes without leaving the command line
...Maybe even you? Effortlessly access several journals for all parts of your life. You can use it to easily create, search, and view journal entries. Journals are stored as human-readable plain text, and can also be encrypted using AES encryption. -
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VideoChat
Real-time voice interactive digital human
VideoChat is a real-time voice-interactive “digital human” system that combines automatic speech recognition, large language models, text-to-speech, and talking-head generation into a single conversational pipeline. It supports both pure end-to-end voice solutions based on multimodal large language models (GLM-4-Voice feeding directly into talking-head generation) and a more traditional cascaded pipeline using ASR → LLM → TTS → talking head. -
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Orpheus TTS
Towards Human-Sounding Speech
Orpheus TTS is a state-of-the-art open-source text-to-speech system built on a Llama-3B backbone, treating speech synthesis as a large language model problem instead of a traditional TTS pipeline. It is designed to produce human-like speech with natural intonation, emotion, and rhythm, targeting quality comparable to or better than many closed-source systems. The project ships both pretrained and finetuned English models, as well as a family of multilingual models released as a research preview, and includes data-processing scripts so users can train or finetune their own variants. ... -
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colleague-skill
Transform a cold separation into a warm Skill
colleague-skill is a specialized agent skill designed to simulate a collaborative teammate within AI-driven workflows, enabling agents to behave more like human colleagues in problem-solving scenarios. The project focuses on enhancing interaction quality by introducing role-based behavior, contextual awareness, and cooperative task execution. It allows agents to provide suggestions, feedback, and alternative approaches, mimicking real-world collaboration dynamics. The system likely integrates with broader agent frameworks, enabling seamless inclusion in multi-agent environments. ... -
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GPT PILOT
The first real AI developer
...It powers the Pythagora VS Code extension and relies on coordinated AI agents that mimic roles in a real development workflow. GPT Pilot is intended to automate the majority of routine coding work while leaving strategic decisions and final review to the human developer. Overall, the project represents an ambitious attempt to move from AI coding assistance toward semi-autonomous software development. -
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Flask-Migrate
SQLAlchemy database migrations for Flask applications using Alembic
...The extension handles versioning of the database schema so teams can track and share structural changes alongside application code, reducing manual migration errors and enabling collaborative development. Flask-Migrate also supports branching and merging workflows by generating human-readable migration scripts that developers can review, edit, and commit to version control. -
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verl
Volcano Engine Reinforcement Learning for LLMs
...It ships with reference implementations of popular alignment algorithms and clear examples that make it straightforward to reproduce baselines before customizing. Data pipelines treat human feedback, simulated environments, and synthetic preferences as interchangeable sources, which helps with rapid experimentation. VERL is meant for both research and production hardening: logging, checkpointing, and evaluation suites are built in so you can track learning dynamics and regressions over time. -
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Habitat-Lab
A modular high-level library to train embodied AI agents
...It is designed to train agents to perform a wide variety of embodied AI tasks in indoor environments, as well as develop agents that can interact with humans in performing these tasks. Allowing users to train agents in a wide variety of single and multi-agent tasks (e.g. navigation, rearrangement, instruction following, question answering, human following), as well as define novel tasks. Configuring and instantiating a diverse set of embodied agents, including commercial robots and humanoids, specifying their sensors and capabilities. Providing algorithms for single and multi-agent training (via imitation or reinforcement learning, or no learning at all as in SensePlanAct pipelines), as well as tools to benchmark their performance on the defined tasks using standard metrics. -
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Aden Hive
Outcome driven agent development framework that evolves
...Once deployed, agents can capture failure data, evolve automatically to meet their success criteria, and redeploy without constant manual intervention, delivering continual improvement over time. The framework also includes human-in-the-loop nodes, credential management, cost and budget controls, and real-time observability so teams can monitor execution and intervene as needed. Hive is designed for production environments and supports a wide range of large language models, local models, and business system connectivity. -
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FastAPI
FastAPI framework, high performance, easy to learn, fast to code
FastAPI is a modern, fast (high-performance), web framework for building APIs with Python 3.6+ based on standard Python type hints. Great editor support. Completion everywhere. Less time debugging. Designed to be easy to use and learn. Less time reading docs. Minimize code duplication. Multiple features from each parameter declaration. Fewer bugs. Get production-ready code. With automatic interactive documentation. Based on (and fully compatible with) the open standards for APIs: OpenAPI... -
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video2robot
End-to-end pipeline converting generative videos
video2robot is an end-to-end open-source pipeline that converts generative video or prompt-driven motion content into executable humanoid robot motion sequences, enabling researchers and developers to go from high-level action descriptions or videos to robot-ready motion data. The pipeline supports both prompt-to-video generation using models like Veo/Sora and video upload processing, followed by human pose extraction through a 3D pose model and retargeting of that motion to robot joints using a general motion retargeting system. This workflow allows users to generate robot motion files that specify joint angles, root positions, and orientations that can be deployed on supported robot platforms (e.g., Unitree models). Video2robot includes scripts for each stage of the pipeline (generation, extraction, conversion, visualization) and can run as a CLI or through a basic web UI.