Hy3

license: apache-2.0 library_name: transformers pipeline_tag: text-generation tags:

• hunyuan
• hy3
• moe
• text-generation

中文 ｜ English

🖥️ Official Website  |   💬 GitHub

Table of Contents

• Model Introduction
• Stronger Agent Capabilities
• More Reliable Product Experiences
• Benchmark Appendix
• News
• Model Links
• Quickstart
• Deployment
  • vLLM
  • SGLang
• Finetuning
• Quantization
• License
• Contact Us

Model Introduction

Hy3 is a 295B-parameter Mixture-of-Experts (MoE) model with 21B active parameters and 3.8B MTP layer parameters, developed by the Tencent Hy Team. Following the Hy3 Preview launch in late April, we gathered feedback from 50+ products and scaled up post-training with higher quality data. Today, we introduce Hy3, which outperforms similar-size models and rivals flagship open-source models with 2-5x parameters. It also shows significant gains in utility across various products and productivity tasks.

Stronger Agent Capabilities

Building on Hy3 Preview, we further improved the quality and diversity of post-training data while scaling up RL training. Hy3 shows solid gains across reasoning, agentic, and long-context tasks, competitive with much larger flagship models.

In productivity scenarios such as coding, office work, financial modeling, frontend design, and game development, Hy3 has made remarkable progress and can now serve as a reliable, cost-effective model option.

We don't think public benchmark scores tell the full story. So we ran a blind evaluation with 270 experts using tasks from their work, and Hy3 scored 2.67/4, outperforming GLM-5.1 at 2.51/4. The advantage was most substantial in frontend development, data & storage, and CI/CD tasks.

More Reliable Product Experiences

Model usefulness is not fully captured by benchmarks. Based on extensive product feedback, we identified and fixed the following issues, receiving consistently positive feedback from product teams.

Stability of tool calls and output formats: We fixed multiple baseline reliability issues, bringing the model to production-grade standards across tool configurations and output constraints. Tool-call error recovery and overall efficiency improved. Hy3 also generalizes across different agent scaffoldings. On SWE-Bench Verified, accuracy variance across scaffoldings like CodeBuddy, Cline, and KiloCode remains within 4%.

Knowledge and anti-hallucination: Guided by the ideal of "answer when grounded, state when evidence is missing, do not conflate sources or fabricate data," we implemented fine-grained data cleaning and training constraints. In internal evaluations based on real-world scenarios, Hy3's hallucination rate dropped from 12.5% to 5.4%, and commonsense error rates fell from 25.4% to 12.7%. These improvements materially reduce fact conflation, fabrication, and logical contradiction.

Complex context retention and multi-turn intent tracking: Through joint optimization of SFT and RL, Hy3 improved on operational pain points like coreference resolution, ellipsis recovery, and multi-turn constraint inheritance. On internal comprehensive multi-turn tests, the issue rate dropped from 17.4% to 7.9%. Hy3 also improved markedly on long-dialogue evals like MRCR. Its outputs are more concise while ensuring complex intents do not decay or drift over long-horizon interactions.

Benchmark Appendix

News

• 🔥 We open-source Hy3 and Hy3-FP8 model weights on Hugging Face, ModelScope, GitCode, and CNB.

Model Links