OrganizationTencent-Hunyuan
@tencent-hunyuan • Open source projects from tencent-hunyuan. Use this route to separate flagship concentration from portfolio breadth before you treat a publisher as broadly strong.
Portfolio concentration
41%
Top three share
Shows whether the organization is driven by one breakout repo or several visible projects.
Breadth
30 repos
Visible snapshot
24 repositories updated in the last 90 days.
Leading language
Python
Portfolio mix
Python (25), Unknown (3), C (1)
Average size
408
Stars per repository
Useful for distinguishing one flagship-heavy publisher from a repeatable portfolio.
Updated: 2026-10-28(6d ago)GitHub API fallback30 repositories
41%
of the visible star count comes from this organization's top three repositories.
408
stars per repository in this same snapshot.
Python
is the most common language here, with 24 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 41%
Organization pages work best when you separate portfolio breadth from flagship concentration. In Tencent-Hunyuan's case, the visible top three repositories account for about 41% 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 (25), Unknown (3), C (1). 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 | tencent-hunyuan/Hy-Memory | Python | 1.9K |
| 2 | tencent-hunyuan/AuK AuK: An Open-Source Foundational Model for Speech Generation and Editing | Python | 1.6K |
| 3 | tencent-hunyuan/Hy-MT2 | Python | 1.5K |
| 4 | tencent-hunyuan/Prism We propose Prism, a dynamic sparse attention framework for natively training joint video-audio generation models at 2K. | Python | 1.5K |
| 5 | tencent-hunyuan/Hunyuan3D-WorldClaw WorldClaw: Agentic 3D Open-world Generation at Scale | 1.4K | |
| 6 | tencent-hunyuan/UniRL UniRL is a Framework for Unified Multimodal Model Reinforcement Learning | Python | 1K |
| 7 | tencent-hunyuan/Hy3 Hy3 (295B A21B), a leading reasoning and agent model in its size, with great cost efficiency. | Python | 677 |
| 8 | tencent-hunyuan/Hy-Nomas Hy-Nomas: Towards Free-form Interactive World Generation | Python | 474 |
| 9 | tencent-hunyuan/Hy4-preview | Python | 406 |
| 10 | tencent-hunyuan/Hy-Embodied-0.5-VLA From Vision-Language-Action Models to a Real-World Robot Learning Stack | Python | 314 |
| 11 | tencent-hunyuan/Hunyuan3D-Buffalo1.0 [Tech Report] A Unified Multimodal Model for Scalable 3D Generation, Understanding, and Editing | 261 | |
| 12 | tencent-hunyuan/Hy-SimulMT | Python | 242 |
| 13 | tencent-hunyuan/Hyra-results Research artifacts from Hyra (/ˈhaɪ.rɑː/) | Python | 200 |
| 14 | tencent-hunyuan/HiLS-Attention Official code for HiLS-Attention | Python | 153 |
| 15 | tencent-hunyuan/Hy-Embodied-RxBrain-1.0 RxBrain: Embodied Cognition Foundation Model with Joint Language-Visual Reasoning and Imagination | Python | 131 |
| 16 | tencent-hunyuan/Simple-Attention-Sparsification [NeurIPS 2026] Code for our resarch paper "SAS: Simple Attention Sparsification via End-to-End Optimization of Context Ranking" | Python | 68 |
| 17 | tencent-hunyuan/GEAR | Python | 68 |
| 18 | tencent-hunyuan/Twins [ICML 2026] Twins: Learn to Predict Unified Representations with Focal Loss | Python | 39 |
| 19 | tencent-hunyuan/Hy-Embodied-RoboFusion | C | 35 |
| 20 | tencent-hunyuan/DiagSafe DiagSafe: diagnostic LLM safety benchmark with prompt-level rubrics (3,489 bilingual prompts, 84-label taxonomy) | Python | 33 |
| 21 | tencent-hunyuan/PlanningBench | 28 | |
| 22 | tencent-hunyuan/ExplorationBench | Python | 27 |
| 23 | tencent-hunyuan/Precise [NeurIPS 2026] Precise: SDE-Consistent Stochastic Sampling for RL Post-Training of Flow-Matching Models | Python | 19 |
| 24 | tencent-hunyuan/RepoProbe Benchmark for repository-level code understanding: 500 open-ended questions from real GitHub Discussions across 50 repositories, graded by a checklist-based verification protocol (ASE 2026) | Python | 14 |
| 25 | tencent-hunyuan/OmniCapBench [NeurIPS 2026] OmniCapBench: A Deep-Structured Evaluation Framework for Fine-Grained Audio-Visual Captioning | Python | 9 |
| 26 | tencent-hunyuan/HYDRA [NeurIPS 2026] HYDRA: Representation Harmonized Tokenization for Multimodal Generation and Understanding | Python | 9 |
| 27 | tencent-hunyuan/Rosetta-inference Open-source native multimodal pretraining — without catastrophic forgetting. | Python | 9 |
| 28 | tencent-hunyuan/evolve-scaler Github page for paper "EvolveScaler: Synthesizing Information-Evolution Contexts via Executable State Machines and Natural-Language Rendering" | HTML | 3 |
| 29 | tencent-hunyuan/HyperLogic HyperLogic: a hard, forward-authored Chinese logical reasoning benchmark with execution-derived answers (Seed / Base / Hard) | Python | 1 |
| 30 | tencent-hunyuan/VisualNeedle A benchmark for active visual search in high-information-density scenes | Python | 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.