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hermes-agent
Hermes Agent is an open-source, self-improving AI agent with a built-in learning loop that creates skills from experience, runs across platforms, and supports any LLM provider.
lingbot-video
LingBot-Video is the first open-source large-scale Mixture-of-Experts (MoE) video generation model designed for embodied intelligence, offering efficient inference and state-of-the-art performance on robotics-related benchmarks.
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License
Apache-2.0
Last updated
2026-07-10
Why it is worth attention
It is the first open-source MoE video generation model specifically targeting embodied intelligence, achieving top rankings on the RBench leaderboard while being fully open-sourced under Apache 2.0.
Who it is for
- Researchers in embodied AI and robotics
- Video generation model developers
- Practitioners in robot simulation and training
- Open-source AI enthusiasts
Use cases
- Generating physically plausible videos for robot task planning and simulation
- Creating text-to-video and text-to-image-to-video content with high motion and physical rationality
- Evaluating video synthesis models on embodied benchmarks (manipulation, navigation, etc.)
- Developing custom prompt rewriting and auto-negative pipelines for video generation
Strengths
- Efficient MoE architecture with ~3x faster inference compared to dense models of equivalent capacity
- Trained on 70,000+ hours of embodied data alongside massive web videos, enabling strong physical understanding
- Top performance on RBench leaderboard across multiple embodied categories (avg. 0.620) among open-source models
- Comprehensive inference workflow with prompt rewriting, auto-negative, and multi-GPU support (FSDP, CP8, SGLang)
Considerations
- Requires specific runtime versions (Python ≥3.10, custom torch builds) and careful environment setup
- The 30B-A3B MoE model demands significant GPU memory and system RAM for loading, even with FSDP sharding
- Inference expects structured JSON captions rather than natural language prompts, necessitating the rewriter pipeline
README quick start
LingBot-Video
🌐 Project Page | 🤗 Hugging Face | 🤖 ModelScope | 📄 Paper | ⚖️ License| 💬 WeChat 微信 Group
📘 English Usage: English Documentation
📕 中文使用文档: 中文文档
We are excited to introduce LingBot-Video, the first open-source large-scale MoE (Mixture-of-Experts) video generation model dedicated to embodied intelligence. As a top-tier video model, LingBot-Video is designed to bridge the gap between video synthesis and physical world understanding.
🔥 Key Highlights
- 🚀 Efficient MoE Architecture: Scaled from scratch; balanced between capacity and cost with ~3x faster inference.
- 📦 Data Engine: Trained on massive web videos integrated with 70,000+ hours of embodied data.
- ⚖️ Multi Reward System: Rewarded for high aesthetics, physical rationality, and task completion.
🎬 Video Demos
🔥 Latest News
- July 9, 2026: 🎉 We release the technical report, code, models, rewriters for LingBot-Video.
📦 Model Download
| Model Name | Components | Tasks | Download |
|---|---|---|---|
| ⚡ LingBot-Video-Dense | Dense (1.3B) | T2I, T2V, TI2V | 🤗 Huggingface 🤖 ModelScope |
| 💪 LingBot-Video-MoE | MoE (30B-A3B) + Refiner | T2I, T2V, TI2V, Refinement | 🤗 Huggingface 🤖 ModelScope |
| 📝 LingBot-Video-Rewriter-Base | Qwen3.6-27B official | Prompt rewriter (Expand) | 🤗 Huggingface 🤖 ModelScope |
| 📝 LingBot-Video-Rewriter-Adapter | Qwen3.6-27B LoRA | Prompt rewriter (Json) | 🤗 Huggingface 🤖 ModelScope |
🚀 Quick Start
🛠️ Installation
The root `requireme
Description
Scaling Mixture-of-Experts Video Pretraining for Embodied Intelligence
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