Hy-MT2 keeps gaining momentum. Since its open-source release in May: → 700K+ downloads 🌟 → Hy-MT2-1.8B reached #1# on the Hugging Face trending, with 30B-A3B reaching #4# 🥇 → 70+ verified product and project integrations 💻 → Broader Hy-MT ecosystem support across Apple MLX-LM, Microsoft ONNX Runtime, NVIDIA NeMo, LLaMA-Factory, and more 👯 → Real-world adoption, including real-time multilingual translation of livestream comments on Bilibili 📺 And now, Hy-MT2-30B-A3B is officially available in GGUF format—addressing one of the community’s most-requested deployment needs and making local inference easier. Ready to run Hy-MT2-30B-A3B locally? Try the new GGUF release: Explore Hy-MT2: HuggingFace： Modelscope： Github： #TencentHy# #HyMT2# #OpenSource#

Tencent Hy3 from @TencentHunyuan is free on @OpenRouter through July 21. 295B MoE, 256K context, built for coding, reasoning, agents and reliable tool use. Try it in OpenClaw today: openclaw models set openrouter/tencent/hy3:free

🚀 Open-source upgrade unlocked. Tencent Hy-MT2 is now under Apache License 2.0 — maximum freedom for research, commercial use, fine-tuning, and derivatives. No strings attached.😎😎😎 Proud to push model weights back to the community. Our two variants are currently sitting at #1# and #4# on the Hugging Face trending leaderboard. Clone, fork, break things, ship feedback. The iteration loop is live.🔥 Let’s keep building the frontier together. #Tencent# #Hy# #HyMT2# #Apache2# #HuggingFace# #OpenSourceA#

Planning is where LLMs move from “saying” to “doing.” Tencent Hy, in collaboration with the Gaoling School of Artificial Intelligence at Renmin University of China, is excited to open-source PlanningBench - a scalable, verifiable framework for evaluating and training LLM planning capabilities. With PlanningBench, you get: ✅ 30+ real-world planning tasks ✅ Automated verification ✅ Evaluation and training support See how top-tier LLMs perform on PlanningBench 👇 Resources: arXiv: GitHub: HuggingFace: #PlanningBench# #TencentHunyuan# #OpenSource# 📷

Minimax H3 is the first *open* video model I've seen that outperforms HunyuanVideo 1.5 - which is actually an impressive accomplishment for @TencentHunyuan to have held that throne for so long. For a while now I've been sad that the image/video frontier seemed to be getting much more closed than the text LLM frontier. H3 is a very strong step back in the open direction. This was generated in ~30 min on my AMD laptop:

🌍Introducing Hy ASR 3.0 preview, a speech recognition model from @TencentHunyuan built to understand, not just transcribe What we improved: - Cleaner on real-world audio: dialects, code-switching, and long-form content with less error accumulation over time - Context-aware correction: homophones and ambiguous phrases get resolved from context, not guessed in isolation - Hotword injection: drop in brand names, people, domain terms without retraining. Lowers integration cost for niche use cases - Built for noisy rooms: whisper, background noise, tricky acoustic conditions stay stable

🙏 Thank you all for the incredible love and support! Our latest Tencent Hunyuan translation models are on fire on Hugging Face: 🥰Hy-MT2-1.8B ranks #1# 🥰Hy-MT2-30B-A3B ranks #4# on the open-source model trending leaderboard, with over 7K downloads already! To make it even easier for everyone, we’ve launched the Tencent Hy Translation WeChat mini-program, built on Hy-MT2. It supports voice input and offline translation, plus powerful customization of translation styles and instructions — delivering results that better match your expectations and feel far more practical. Try it out and share your feedback with us — we’d love to hear from you! Models on HF: GitHub: #HyMT2# #TencentHunyuan# #OpenSource#

12 Chinese AI models made their World Cup predictions. ⚽ The World Cup Round of 32 lineup is out.🔥 The first leaderboard is in.👇 Tencent Hy went 29/32, ranking #1# among the 12 models after the Round of 32 was set. Now comes the harder test: whose predictions survive the knockout stage? #TencentHunyuan# #AI# #WorldCup#

Can AI truly edit audio, not just generate it? 🎧 Tencent Hy, in collaboration with SJTU, SII, NTU, TJU, ZODA, PKU, FDU, and other collaborators, introduces MMAE. MMAE--A Massive Multitask Audio Editing Benchmark, is the first comprehensive evaluation benchmark for speech and audio "Banana🍌" Instead of simply requiring the AI to "generate" audio, it demands that the AI understand an existing audio clip and precisely modify it according to natural language instructions—altering what needs to be changed while leaving the rest untouched. Current models show an Exact Match Rate (EMR) below 5%, revealing a major gap in reliable audio editing. MMAE includes: ✅ 2,000 high-fidelity samples from real-world scenarios ✅ 17,741 fine-grained rubric evaluation items ✅ 7 modality settings across sound, music, speech and their mixtures ✅ 6 task complexity from basic modifications to multi-hop reasoning and multi-round editing ✅ 8 operation types across local and global granularities How to use: arXiv: GitHub: HuggingFace: Demo: