Tencent’s Hunyuan staff has launched Hunyuan-A13B, a brand new open-source massive language mannequin constructed on a sparse Combination-of-Specialists (MoE) structure. Whereas the mannequin consists of 80 billion whole parameters, solely 13 billion are energetic throughout inference, providing a extremely environment friendly steadiness between efficiency and computational value. It helps Grouped Question Consideration (GQA), 256K context size, and a dual-mode reasoning framework that toggles between quick and sluggish pondering.
Designed for environment friendly deployment and strong reasoning, Hunyuan-A13B achieves top-tier efficiency throughout agentic benchmarks together with BFCL-v3, τ-Bench, C3-Bench, and ComplexFuncBench, typically outperforming bigger fashions in tool-calling and long-context eventualities.
Structure: Sparse MoE with 13B Energetic Parameters
At its core, Hunyuan-A13B follows a fine-grained MoE design comprising 1 shared professional and 64 non-shared consultants, with 8 consultants activated per ahead go. This structure, backed by scaling experiments, ensures efficiency consistency whereas maintaining inference prices low. The mannequin contains 32 layers, makes use of SwiGLU activations, a vocabulary measurement of 128K, and integrates GQA for enhanced reminiscence effectivity throughout long-context inference.
The mannequin’s MoE setup is paired with an optimized coaching curriculum: a 20T-token pretraining part, adopted by quick annealing and long-context adaptation. This final part scales the context window first to 32K after which to 256K tokens utilizing NTK-aware positional encoding, guaranteeing steady efficiency at massive sequence lengths.
Twin-Mode Reasoning: Quick and Sluggish Pondering
A standout function of Hunyuan-A13B is its dual-mode Chain-of-Thought (CoT) functionality. It helps each a low-latency fast-thinking mode for routine queries and a extra elaborate slow-thinking mode for multi-step reasoning. These modes are managed by way of a easy tag system: /no suppose for quick inference and /suppose for reflective reasoning. This flexibility permits customers to adapt computational value to job complexity.
Publish-Coaching: Reinforcement Studying with Activity-Particular Reward Fashions
The post-training pipeline of Hunyuan-A13B contains multi-stage supervised fine-tuning (SFT) and reinforcement studying (RL) throughout each reasoning-specific and basic duties. The RL phases incorporate outcome-based rewards and tool-specific suggestions, together with sandbox execution environments for code and rule-based checks for brokers.
Within the agent coaching part, the staff synthesized numerous tool-use eventualities with planner, checker, and power roles, producing over 20,000 format combos. This strengthened Hunyuan-A13B’s potential to execute real-world workflows equivalent to spreadsheet processing, data search, and structured reasoning.
Analysis: State-of-the-Artwork Agentic Efficiency
Hunyuan-A13B reveals robust benchmark outcomes throughout numerous NLP duties:
- On MATH, CMATH, and GPQA, it scores on par or above bigger dense and MoE fashions.
- It surpasses Qwen3-A22B and DeepSeek R1 in logical reasoning (BBH: 89.1; ZebraLogic: 84.7).
- In coding, it holds its personal with 83.9 on MBPP and 69.3 on MultiPL-E.
- For agent duties, it leads on BFCL-v3 (78.3) and ComplexFuncBench (61.2), validating its tool-usage capabilities.
Lengthy-context comprehension is one other spotlight. On PenguinScrolls, it scores 87.7—simply shy of Gemini 2.5 Professional. On RULER, it sustains excessive efficiency (73.9) even at 64K–128K context, outperforming bigger fashions like Qwen3-A22B and DeepSeek R1 in context resilience.
Inference Optimization and Deployment
Hunyuan-A13B is totally built-in with fashionable inference frameworks like vLLM, SGLang, and TensorRT-LLM. It helps precision codecs equivalent to W16A16, W8A8, and KV Cache FP8, together with options like Auto Prefix Caching and Chunk Prefill. It achieves as much as 1981.99 tokens/sec throughput on a 32-batch enter (2048 enter, 14336 output size), making it sensible for real-time functions.
Open Supply and Trade Relevance
Out there on Hugging Face and GitHub, Hunyuan-A13B is launched with permissive open-source licensing. It’s engineered for environment friendly analysis and manufacturing use, particularly in latency-sensitive environments and long-context duties.
By combining MoE scalability, agentic reasoning, and open-source accessibility, Tencent’s Hunyuan-A13B provides a compelling various to heavyweight LLMs, enabling broader experimentation and deployment with out sacrificing functionality.
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