Search Results for "ace-step"
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Showing 283 open source projects for "ace-step"
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ACE-Step 1.5
The most powerful local music generation model
...Beyond straightforward text-to-music synthesis, ACE-Step 1.5 enables flexible creative workflows, including tasks like cover generation, editing existing tracks, transforming vocals to background accompaniment, and stylistic personalization using low-rank adaptation from just a few example songs. -
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Step-Audio
Open-source framework for intelligent speech interaction
...Through its architecture, Step-Audio supports multilingual interaction, dialects, emotional tones (joy, sadness, etc.), and even more creative speech styles (like rap or singing), while allowing dynamic control over speech characteristics. It also provides a “generative data engine,” which can produce synthetic speech data (cloning voices, varying style) to support TTS training. -
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Step 3.5 Flash
Fast, Sharp & Reliable Agentic Intelligence
Step 3.5 Flash is a cutting-edge, open-source large language model developed by StepFun-AI that pushes the frontier of efficient reasoning and “agentic” intelligence in a way that makes powerful AI accessible beyond proprietary black boxes. Unlike dense models that activate all their parameters for every token, Step 3.5 Flash uses a sparse Mixture-of-Experts (MoE) architecture that selectively engages only about 11 billion of its roughly 196 billion total parameters per token, delivering high-quality reasoning and interaction at far lower compute cost and latency than traditional large models. ... -
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Step-Audio-EditX
LLM-based Reinforcement Learning audio edit model
Step-Audio-EditX is an open-source, 3 billion-parameter audio model from StepFun AI designed to make expressive and precise editing of speech and audio as easy as text editing. Rather than treating audio editing as low-level waveform manipulation, this model converts speech into a sequence of discrete “audio tokens” (via a dual-codebook tokenizer) — combining a linguistic token stream and a semantic (prosody/emotion/style) token stream — thereby abstracting audio editing into high-level token operations. ... -
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Step-Video-T2V
State-of-the-art (SoTA) text-to-video pre-trained model
...Its training and generation pipeline includes techniques like flow-matching, full 3D attention for temporal consistency, and fine-tuning approaches (e.g. video-based DPO) to improve fidelity and reduce artifacts. As a result, Step-Video-T2V aims to push the frontier of open-source video generation. -
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Step-Audio 2
Multi-modal large language model designed for audio understanding
Step-Audio2 is an advanced, end-to-end multimodal large language model designed for high-fidelity audio understanding and natural speech conversation: unlike many pipelines that separate speech recognition, processing, and synthesis, Step-Audio2 processes raw audio, reasons about semantic and paralinguistic content (like emotion, speaker characteristics, non-verbal cues), and can generate contextually appropriate responses — including potentially generating or transforming audio output. ... -
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TTS WebUI
A single Gradio + React WebUI with extensions for ACE-Step
TTS-WebUI is a unified Gradio + React web interface that brings together a large ecosystem of text-to-speech, voice conversion, and audio generation models under a single UI. It supports a wide range of models such as Bark, MusicGen, Tortoise, RVC, StyleTTS2, ParlerTTS, CosyVoice, XTTSv2, Stable Audio, SeamlessM4T, and many others, exposing them as interchangeable backends for speech and music synthesis. The project provides an installer that sets up Conda, Python environments, and all... -
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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 code. ... -
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Qwen3.5
Qwen3.5 is the large language model series developed by Qwen team
Qwen3.5 is part of Alibaba’s Qwen family of large language and multimodal foundation models, designed to power advanced AI applications such as chatbots, coding assistants, and autonomous agents. The project represents a significant step toward “agentic AI,” meaning models that can reason through multi-step tasks and interact with external tools or environments rather than only generating text. Qwen3.5 builds on earlier Qwen generations by improving multilingual understanding, reasoning ability, and efficiency, while also introducing native multimodal capabilities that allow the model to work with both language and visual inputs. ... -
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GLM-5
From Vibe Coding to Agentic Engineering
...Building on earlier GLM series models, GLM-5 dramatically scales the parameter count (to roughly 744 billion) and expands pre-training data to significantly improve performance on complex tasks such as multi-step reasoning, software engineering workflows, and agent orchestration compared to its predecessors like GLM-4.5. It incorporates innovations like DeepSeek Sparse Attention (DSA) to preserve massive context windows while reducing deployment costs and supporting long context processing, which is crucial for detailed plans and agent tasks. -
11
self-llm
Tutorial tailored for Chinese babies on rapid fine-tuning
...The repository focuses on helping beginners and developers understand how to run and customize modern LLMs locally rather than relying solely on hosted APIs. It provides step-by-step tutorials covering environment setup, model deployment, inference workflows, and efficient fine-tuning techniques such as LoRA and parameter-efficient training. The project also includes guides for integrating models into real applications, including command-line interfaces, web demos, and frameworks like LangChain. By combining theory, configuration instructions, and runnable examples, self-llm lowers the barrier to entry for students and engineers who want to experiment with open-source models. -
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Kimi K2.5
Moonshot's most powerful AI model
...Based on a 1T-parameter Mixture-of-Experts (MoE) architecture with 32B activated parameters, it integrates advanced language reasoning with strong visual understanding. K2.5 supports both “Thinking” and “Instant” modes, enabling either deep step-by-step reasoning or low-latency responses depending on the task. Designed for agentic workflows, it features an Agent Swarm mechanism that decomposes complex problems into coordinated sub-agents executing in parallel. With a 256K context length and MoonViT vision encoder, the model excels across reasoning, coding, long-context comprehension, image, and video benchmarks. ... -
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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. ... -
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verl-agent
Designed for training LLM/VLM agents via RL
...Developers can configure memory modules that determine how historical information is stored and incorporated into each step of the reasoning process. -
15
pi-autoresearch
Autonomous experiment loop extension for pi
...The system likely integrates with external data sources or APIs to retrieve information and process it into structured insights. Its architecture suggests a focus on autonomy, allowing it to run multi-step research pipelines that mimic human investigative processes. This makes it particularly useful for exploratory analysis, trend discovery, or generating structured knowledge from large information spaces. Overall, pi-autoresearch represents a step toward self-directed research agents capable of producing increasingly refined outputs over time. -
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Langflow
Low-code app builder for RAG and multi-agent AI applications
Langflow is a low-code app builder for RAG and multi-agent AI applications. It’s Python-based and agnostic to any model, API, or database. -
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Claude Code Architecture Study
Research on Coding Agents
...The project focuses on breaking down the architecture of agentic systems, including how models perceive context, make decisions, and execute actions in a coding environment. It likely provides step-by-step examples, conceptual explanations, and practical implementations that guide users through creating their own agents. The framework emphasizes learning by doing, allowing users to experiment with agent behavior, prompt design, and workflow structuring. It also explores how agents interact with tools such as file systems, terminals, and APIs, giving a holistic view of real-world applications. ... -
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xiaohongshu-ops
Turn Openclaw into a Xiaohongshu operations assistant
...It also provides practical frameworks for increasing visibility, improving content performance, and leveraging trends effectively. The content is organized to support both beginners and experienced operators, offering step-by-step strategies as well as advanced growth tactics. -
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thorough-pytorch
PyTorch Getting Started Tutorial, read online
...It emphasizes a learning approach that combines theoretical explanations with hands-on coding exercises so that students can build and experiment with neural networks directly. The project encourages collaborative learning and often organizes materials in a step-by-step progression that gradually increases in complexity. Topics include neural network fundamentals, training procedures, model evaluation, and practical deep learning workflows. By combining structured lessons with programming projects, the repository aims to help learners develop both conceptual understanding and practical implementation skills. -
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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 context-specific parameters, allowing organizations to codify best practices for customer support, data processing, document workflows, or incident response. ... -
21
Vulnhuntr
AI tool for detecting complex vulnerabilities in Python codebases
...It focuses on Python projects and applies static code analysis combined with LLM reasoning to trace how user input flows through an application. Instead of scanning entire repositories at once, it builds call chains step by step, allowing deeper inspection of complex, multi-stage issues that traditional tools may miss. Vulnhuntr can generate detailed findings, including vulnerability explanations and potential exploit paths, helping developers and security teams understand risks faster. It supports multiple LLM providers such as OpenAI, Anthropic, and Ollama, and can be run via CLI, Docker, or pipx. ... -
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Browser Agent
AI Browser Agent is an advanced Browser AI tool
...The tool allows developers to describe tasks in plain English, such as navigating pages, clicking elements, filling forms, and extracting data, and the system executes those actions as if a human were interacting with the browser. It is designed to simplify complex automation workflows by removing the need for manually written selectors or step-by-step scripts. The agent supports multi-step task execution, enabling it to perform sequences of actions across multiple pages while maintaining context. It also provides structured output formats such as JSON, HTML, Markdown, or screenshots, making it easy to integrate results into other systems or pipelines. Because it can interact with dynamic, JavaScript-heavy websites, it is suitable for modern web scraping and automation tasks. -
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OpenAI Quickstart Node
Node.js example app from the OpenAI API quickstart tutorial
...The project is a practical starting point for building AI-powered applications, serving as a foundation for experimentation and integration into larger projects. It simplifies onboarding by offering step-by-step setup instructions and ready-to-use code snippets that can be adapted for custom needs. -
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Generative AI for Beginners .NET
Hands-on .NET course for building real-world generative AI apps
...It walks through core concepts such as text generation, chat-based interactions, and integrating large language models into applications. Each lesson includes short videos, working code samples, and step-by-step instructions, making it easy to follow and apply immediately. Generative AI for Beginners .NET supports tools like GitHub Models, Azure OpenAI Service, and local models, giving flexibility in how projects are built and tested. Developers can run examples locally or in cloud-based environments such as GitHub Codespaces. It focuses on practical implementation rather than theory, helping users move from simple experiments to complete AI-powered solutions while understanding responsible AI usage and modern development workflows. -
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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 Python applications, abstracting much of the RL infrastructure so developers can train agents without deep RL expertise or heavy infrastructure overhead. ...
