Search Results for "stage" - Page 2
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Showing 72 open source projects for "stage"
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Auto-Deep-Research
Your Fully-Automated Personal AI Assistant
Auto-Deep-Research is a system designed to fully automate deep research workflows using language models, retrieval, planning, and multi-stage reasoning to produce structured research artifacts such as surveys, benchmarks, reports, and even prototypes without heavy human intervention. Users provide a research topic or multifaceted goal, and the system autonomously breaks the objective down into subtasks like literature collection, critical summarization, cross-comparison, citation extraction, metric evaluation, and structured writing. ... -
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HY-Motion 1.0
HY-Motion model for 3D character animation generation
HY-Motion 1.0 is an open-source, large-scale AI model suite developed by Tencent’s Hunyuan team that generates high-quality 3D human motion from simple text prompts, enabling the automatic production of fluid, diverse, and semantically accurate animations without manual keyframing or rigging. Built on advanced deep learning architectures that combine Diffusion Transformer (DiT) and flow matching techniques, HY-Motion scales these approaches to the billion-parameter level, resulting in strong... -
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EverMemOS
Long-term memory OS for AI with structured recall and context awarenes
...Its architecture combines memory storage, indexing, and retrieval with agent-level reasoning, allowing AI systems to make informed decisions based on prior interactions. EverMemOS goes beyond simple retrieval by actively applying stored knowledge to current tasks, improving personalization and consistency. EverMemOS uses a multi-stage memory lifecycle to convert raw dialogue into structured semantic data, supporting long-horizon reasoning and adaptive behavior across sessions. -
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Real-Time Voice Cloning
Clone a voice in 5 seconds to generate arbitrary speech in real-time
...It implements the SV2TTS pipeline (“Transfer Learning from Speaker Verification to Multispeaker Text-To-Speech Synthesis”) in three stages: a speaker encoder, a synthesizer, and a vocoder. In the first stage, short audio clips are converted into a fixed-dimensional speaker embedding that captures voice characteristics; this embedding is then used by a Tacotron-style synthesizer to generate spectrograms from text, which a WaveRNN-based vocoder finally turns into audio. The repo includes both a command-line demo and a graphical “toolbox” application where you can load reference voices, type text, and hear the synthesized results interactively. ... -
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VOID
Video Object and Interaction Deletion
VOID is an advanced AI video processing system developed by Netflix that focuses on removing objects from videos while preserving the physical and visual realism of the surrounding environment. Unlike traditional inpainting methods that only erase pixels or simple artifacts, VOID models the full interaction dynamics between objects and their environment, including shadows, reflections, and even physical consequences such as movement or balance changes. Built on top of transformer-based... -
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AnyTool
AnyTool: Universal Tool-Use Layer for AI Agents
AnyTool is an open-source universal tool-use layer for AI agents that addresses the critical problem of how autonomous agents reliably interact with external tools and environments. Rather than having each agent handle tool invocation logic on its own, AnyTool provides a standardized interface and orchestrator that intelligently selects and manages tools, reduces context overhead, and improves execution reliability across diverse capabilities like web APIs, local commands, and GUI... -
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TorchDistill
A coding-free framework built on PyTorch
torchdistill (formerly kdkit) offers various state-of-the-art knowledge distillation methods and enables you to design (new) experiments simply by editing a declarative yaml config file instead of Python code. Even when you need to extract intermediate representations in teacher/student models, you will NOT need to reimplement the models, which often change the interface of the forward, but instead specify the module path(s) in the yaml file. In addition to knowledge distillation, this... -
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ComfyUI-WanVideoWrapper
ComfyUI wrapper nodes for WanVideo and related models
...It also enables extended video generation by linking outputs between iterations, allowing for longer and more coherent animations. Additionally, the wrapper often includes optimizations for performance, such as low VRAM configurations and multi-stage sampling strategies. -
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Claw Compactor
14-stage Fusion Pipeline for LLM token compression
Claw Compactor is a utility designed to optimize and manage the context limitations inherent in AI agent systems, particularly those built on OpenClaw-like architectures. It addresses the challenge of finite context windows in language models by compressing or summarizing historical interactions while preserving essential information. The system works by transforming older conversation data into condensed representations that maintain continuity without exceeding token limits. This approach... -
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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. Internally, it leverages pretrained language models and speech encoders, with a multimodal adapter that integrates both modalities for inference and training. ... -
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nanochat
The best ChatGPT that $100 can buy
nanochat is a from-scratch, end-to-end “mini ChatGPT” that shows the entire path from raw text to a chatty web app in one small, dependency-lean codebase. The repository stitches together every stage of the lifecycle: tokenizer training, pretraining a Transformer on a large web corpus, mid-training on dialogue and multiple-choice tasks, supervised fine-tuning, optional reinforcement learning for alignment, and finally efficient inference with caching. Its north star is approachability and speed: you can boot a fresh GPU box and drive the whole pipeline via a single script, producing a usable chat model in hours and a clear markdown report of what happened. ... -
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J-Space Cognition Suite V3.6
AI cognitive-enhancement Skills based on Anthropic's J-space
...The suite manages an agent's accessible working representations through selective loading instead of applying every mechanism to every task. Its fast, full, and loop modes scale from simple checks to multi-stage work requiring persistent state. Core mechanisms include shared workspace anchors, compact reasoning tracks, metacognitive control, explicit intermediate reasoning, and empirical verification. An optional Python controller records goals, checkpoints, open questions, recovery state, and task continuity. -
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BigMac
An open-source toolkit for BigMac-style pipeline-parallel training
...A Megatron-Core reference backend and Qwen3 and Qwen3-VL tutorials help developers connect the approach to real training workflows. The simulator lets researchers visualize schedules, compare pipeline strategies, and model timing imbalances caused by compute cost, input size, or uneven stage partitioning. Profiling tools also expose per-operator traces for diagnosing pipeline performance before or during experiments. -
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Cognita
Open source RAG framework for building scalable modular AI apps
...It addresses the gap between quick experimentation in notebooks and the complexity of deploying scalable AI systems by introducing a modular and API-driven architecture. Cognita provides reusable components such as parsers, data loaders, embedders, retrievers, and query controllers, allowing teams to customize each stage of the RAG pipeline independently. It includes both a backend service and a frontend interface, enabling users to upload documents, experiment with configurations, and perform question-answering tasks interactively. Cognita supports incremental indexing, meaning it processes only new or updated data to reduce computational overhead and improve efficiency. -
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Parallax
Parallax is a distributed model serving framework
...Instead of relying on centralized GPU clusters in data centers, the system allows multiple heterogeneous machines to collaborate in serving AI inference workloads. Parallax divides model layers across different nodes and dynamically coordinates them to form a complete inference pipeline. A two-stage scheduling architecture determines how model layers are allocated to available hardware and how requests are routed across nodes during execution. This scheduling system optimizes latency, throughput, and hardware utilization even when nodes have different computational capabilities. The platform also supports model sharding and pipeline parallelism, allowing very large models to run across distributed resources. -
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DATAGEN
AI-driven multi-agent research assistant automating hypothesis
...The system coordinates multiple specialized AI agents that collaborate to perform tasks such as hypothesis generation, data collection, analysis, visualization, and report creation. Instead of requiring users to manually orchestrate each stage of a research process, the platform allows these agents to coordinate automatically and handle the workflow end-to-end. The project integrates several modern AI frameworks including LangChain, LangGraph, and large language models to manage reasoning and data processing tasks. Through this architecture, the system can combine structured data analysis with natural language reasoning to generate insights and research outputs. ... -
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video2robot
End-to-end pipeline converting generative videos
...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. -
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Coconut
Training Large Language Model to Reason in a Continuous Latent Space
Coconut is the official PyTorch implementation of the research paper “Training Large Language Models to Reason in a Continuous Latent Space.” The framework introduces a novel method for enhancing large language models (LLMs) with continuous latent reasoning steps, enabling them to generate and refine reasoning chains within a learned latent space rather than relying solely on discrete symbolic reasoning. It supports training across multiple reasoning paradigms—including standard... -
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IMS Toucan
Controllable and fast Text-to-Speech for over 7000 languages
IMS-Toucan is a toolkit for training, using, and teaching state-of-the-art text-to-speech systems, built at the Institute for Natural Language Processing (IMS), University of Stuttgart. It is the official home of ToucanTTS, a massively multilingual TTS system designed to support over 7,000 languages with a single unified framework. The toolkit focuses on being fast and controllable while not requiring huge amounts of compute, making it practical for research labs and smaller teams. It... -
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Ling-V2
Ling-V2 is a MoE LLM provided and open-sourced by InclusionAI
...It introduces highly sparse architectures where only a fraction of the model’s parameters are activated per input token, enabling models like Ling-mini-2.0 to achieve reasoning and instruction-following capabilities on par with much larger dense models while remaining significantly more computationally efficient. Trained on more than 20 trillion tokens of high-quality data and enhanced through multi-stage supervised fine-tuning and reinforcement learning, Ling-V2’s models demonstrate strong general reasoning, mathematical problem-solving, coding understanding, and knowledge-intensive task performance. -
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Ring
Ring is a reasoning MoE LLM provided and open-sourced by InclusionAI
Ring is a reasoning Mixture-of-Experts (MoE) large language model (LLM) developed by inclusionAI. It is built from or derived from Ling. Its design emphasizes reasoning, efficiency, and modular expert activation. In its “flash” variant (Ring-flash-2.0), it optimizes inference by activating only a subset of experts. It applies reinforcement learning/reasoning optimization techniques. Its architectures and training approaches are tuned to enable efficient and capable reasoning performance.... -
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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. The handbook also includes reproducible workflows for training instruction-following models and evaluating alignment quality across different datasets and benchmarks. ... -
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Build Your Own OpenClaw
A step-by-step guide to build your own AI agent
Build Your Own OpenClaw is a step-by-step educational framework that teaches developers how to construct a fully functional AI agent system from scratch, gradually evolving from a simple chat loop into a multi-agent, production-ready architecture. The project is structured into 18 progressive stages, each introducing a new concept such as tool usage, memory persistence, event-driven design, and multi-agent coordination, with each step including both explanatory documentation and runnable... -
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kg-gen
Knowledge Graph Generation from Any Text
kg-gen is an open-source framework developed by the STAIR Lab that automatically generates knowledge graphs from unstructured text using large language models. The system is designed to transform plain text sources such as documents, articles, or conversation transcripts into structured graphs composed of entities and relationships. Instead of relying on traditional rule-based extraction techniques, KG-Gen uses language models to identify entities and their relationships, producing... -
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QAnything
Question and Answer based on Anything
...Users can upload local files and receive fast, reliable answers based on the indexed content. The system supports formats such as PDF, Word, PowerPoint, Excel, Markdown, email, text, images, CSV, and web links. Its retrieval process uses a two-stage vector and reranking approach to maintain answer quality as the knowledge base grows. It is built to be hardware-friendly, easy to deploy with Docker, and usable across Windows, macOS, and Linux.
