Tencent’s Hunyuan workforce has launched Hunyuan-A13Ba brand new open-source giant language mannequin constructed on a sparse Combination-of-Consultants (MoE) structure. Whereas the mannequin consists of 80 billion whole parameters, solely 13 billion are lively throughout inference, providing a extremely environment friendly steadiness between efficiency and computational price. It helps Grouped Question Consideration (GQA), 256K context sizeand a dual-mode reasoning framework that toggles between quick and sluggish considering.
Designed for environment friendly deployment and sturdy reasoning, Hunyuan-A13B achieves top-tier efficiency throughout agentic benchmarks together with BFCL-V3, τ-Bench, C3-Benchand ComplexFuncBenchtypically 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 skilled and 64 non-shared consultantswith 8 consultants activated per ahead cross. This structure, backed by scaling experiments, ensures efficiency consistency whereas preserving inference prices low. The mannequin consists of 32 layers, makes use of SwiGLU activations, a vocabulary dimension 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 section, adopted by quick annealing and long-context adaptation. This final section scales the context window first to 32K after which to 256K tokens utilizing NTK-aware positional encoding, guaranteeing steady efficiency at giant sequence lengths.
Twin-Mode Reasoning: Quick and Gradual Considering
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 means of a easy tag system: /no assume for quick inference and /assume for reflective reasoning. This flexibility permits customers to adapt computational price to process complexity.
Put up-Coaching: Reinforcement Studying with Activity-Particular Reward Fashions
The post-training pipeline of Hunyuan-A13B consists of 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 suggestionstogether with sandbox execution environments for code and rule-based checks for brokers.
Within the agent coaching section, the workforce synthesized numerous tool-use eventualities with planner, checker, and gear roles, producing over 20,000 format mixtures. This bolstered Hunyuan-A13B’s skill to execute real-world workflows comparable to spreadsheet processing, info search, and structured reasoning.
Analysis: State-of-the-Artwork Agentic Efficiency
Hunyuan-A13B exhibits robust benchmark outcomes throughout numerous NLP duties:
- On MATH, CMATHand GPQUit 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 dutiesit leads on BFCL-V3 (78.3) and ComplexFuncBench (61.2)validating its tool-usage capabilities.
Lengthy-context comprehension is one other spotlight. On PenguinScrollsit scores 87.7—simply shy of Gemini 2.5 Professional. On RULERit sustains excessive efficiency (73.9) even at 64K–128K contextoutperforming bigger fashions like Qwen3-A22B and DeepSeek R1 in context resilience.
Inference Optimization and Deployment
Hunyuan-A13B is totally built-in with well-liked inference frameworks like VLLM, Sglangand TensorRT-LLM. It helps precision codecs comparable to W16A16, W8A8and KV Cache FP8together 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
Accessible 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 reasoningand open-source accessibilityTencent’s Hunyuan-A13B gives a compelling various to heavyweight LLMs, enabling broader experimentation and deployment with out sacrificing functionality.
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