OrganizationOpenMOSS (SII)
@openmoss • OpenMOSS Team is a research group under the Shanghai Innovation Institute (SII), working in close collaboration with Fudan University and MOSI Intelligence. . Use this route to separate flagship concentration from portfolio breadth before you treat a publisher as broadly strong.
Portfolio concentration
66%
Top three share
Shows whether the organization is driven by one breakout repo or several visible projects.
Breadth
30 repos
Visible snapshot
23 repositories updated in the last 90 days.
Leading language
Python
Portfolio mix
Python (19), HTML (3), TypeScript (3)
Average size
421
Stars per repository
Useful for distinguishing one flagship-heavy publisher from a repeatable portfolio.
Updated: 2026-10-20(2d ago)GitHub API fallback30 repositories
66%
of the visible star count comes from this organization's top three repositories.
421
stars per repository in this same snapshot.
Python
is the most common language here, with 23 repositories updated in the last 90 days.
Why this rank
This organization stands out because its public portfolio is relatively balanced across 30 repositories.
Balanced portfolio across 30 reposTop 3 share 66%
Organization pages work best when you separate portfolio breadth from flagship concentration. In OpenMOSS (SII)'s case, the visible top three repositories account for about 66% of total stars in this snapshot, which helps explain whether the organization is known for one breakout project or for a broader repeatable portfolio.
The dominant language mix here is Python (19), HTML (3), TypeScript (3). That makes this page useful not just for popularity checks, but also for seeing what technical shape an organization's public ecosystem actually has.
Top Repositories
| # | Repository | Language | Stars |
|---|---|---|---|
| 1 | openmoss/MOSS-TTS-Nano A 100M-parameter multilingual TTS model for real-time CPU inference, voice cloning, and 48 kHz stereo generation | Python | 4.5K |
| 2 | openmoss/MOSS-Transcribe-Diarize A 0.9B model for long-form transcription in 50+ languages with speaker diarization, timestamps, and acoustic event awareness | Python | 2.2K |
| 3 | openmoss/MOSS-Image Official repository for MOSS-Image, a 5B-parameter base DiT model with high-quality text rendering, layout generation, and efficient fine-tuning. | Python | 1.5K |
| 4 | openmoss/Awesome-WAM A curated collection of papers, explainers, and resources on World Action Models for embodied AI | HTML | 1.5K |
| 5 | openmoss/MOSS-VL An open-weight 11B model series for long-form and real-time video understanding | C++ | 781 |
| 6 | openmoss/MOSS-Audio An open-source model for understanding speech, environmental sounds, and music through captioning, question answering, and reasoning | Python | 689 |
| 7 | openmoss/EasyWAM A unified framework for training, fine-tuning, and evaluating World Action Models | Python | 374 |
| 8 | openmoss/OpenETA An embodied task agent that connects perception, action, verification, and learning in a continuous physical-world loop | Python | 208 |
| 9 | openmoss/MOSS-Music An open-source model for music captioning, lyrics transcription, structural analysis, and musical question answering | Python | 194 |
| 10 | openmoss/MOSS-Video-Preview An 11B model for real-time video understanding with gated cross-attention and streaming inference | Python | 173 |
| 11 | openmoss/OmniVAE A unified audio-video variational autoencoder with cross-modal alignment for joint reconstruction and generation | Python | 94 |
| 12 | openmoss/SWE-bench-Science A benchmark for testing whether coding agents can resolve engineering tasks in scientific software | Python | 86 |
| 13 | openmoss/MOSS-TTS-Nano-Reader A local browser reading app powered by MOSS-TTS-Nano with in-browser ONNX inference | JavaScript | 63 |
| 14 | openmoss/claude-codex-handoff An asynchronous file-based handoff protocol for Claude Code and Codex agents sharing one project workspace | Python | 43 |
| 15 | openmoss/OurClaw A multi-user OpenClaw deployment with isolated workspaces and configurable model backends | TypeScript | 25 |
| 16 | openmoss/ScienceEvolve Build, run, and improve your own science research workflow, and evolve it. | TypeScript | 16 |
| 17 | openmoss/MOSS-Audio-Tokenizer-Eval An objective reconstruction evaluation toolkit for audio tokenizers, neural codecs, audio VAEs, and vocoders | Python | 15 |
| 18 | openmoss/ScienceEvolve-server Build, run, and improve your own science research workflow, and evolve it. Web Server. | TypeScript | 12 |
| 19 | openmoss/VehicleArena A persistent 3D urban-driving benchmark for evaluating LLM agents on passenger requests, driving decisions, and multi-agent traffic interactions. | Python | 11 |
| 20 | openmoss/FDU-PRML-2026Fall 复旦大学《模式识别与机器学习》课程仓库 | Python | 11 |
| 21 | openmoss/MOSS-VL-Demo Interactive web demo for MOSS-VL video understanding models | Vue | 10 |
| 22 | openmoss/SummerQuest-2026 Public assignments and submissions for the 2026 OpenMOSS summer training program | Python | 9 |
| 23 | openmoss/GSF-chi Global Stereochemical Fields for Chiral Graph Transformers | Python | 8 |
| 24 | openmoss/Hybrid-Mechanics | 8 | |
| 25 | openmoss/AgentHPOBench A benchmark for evaluating LLM agents as sequential hyperparameter optimizers across 30 executable machine learning tasks | Python | 8 |
| 26 | openmoss/imclaw-skill An OpenClaw skill for real-time messaging between AI agents and users through IMClaw Hub | Python | 7 |
| 27 | openmoss/MOSS-TTS-Nano-Demo Interactive web demo for MOSS-TTS-Nano speech generation | CSS | 4 |
| 28 | openmoss/ContextWeave A benchmark for evaluating coding-agent memory systems through long-horizon personal worklog tasks | Python | 2 |
| 29 | openmoss/moss-tts-realtime | HTML | 0 |
| 30 | openmoss/OmniVAE.github.io Project website for OmniVAE | HTML | 0 |
Next step after the organization read
Open a flagship repository, compare a couple of portfolio leaders, or return to the organization map when you want a broader concentration read.
Learn and methodology
Keep trust-building context reachable, but behind the first data read instead of ahead of it.
How to read this organization snapshot
Total stars are useful as a discovery signal, but they do not tell you whether a team maintains every repository equally. Pair this page with release cadence, maintainer activity, and the flagship concentration shown above before making adoption decisions.
For broader background on GitStar's ranking logic and editorial guidance, see Methodology & Editorial Standards.