tencent/Hunyuan-A13B-Instruct-GGUF

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Technical Report | GITHUB | cnb.cool | LICENSE

Welcome to the official repository of Hunyuan-A13B, an innovative and open-source large language model (LLM) built on a fine-grained Mixture-of-Experts (MoE) architecture. Designed for efficiency and scalability, Hunyuan-A13B delivers cutting-edge performance with minimal computational overhead, making it an ideal choice for advanced reasoning and general-purpose applications, especially in resource-constrained environments.

Model Introduction

With the rapid advancement of artificial intelligence technology, large language models (LLMs) have achieved remarkable progress in natural language processing, computer vision, and scientific tasks. However, as model scales continue to expand, optimizing resource consumption while maintaining high performance has become a critical challenge. To address this, we have explored Mixture of Experts (MoE) architectures. The newly introduced Hunyuan-A13B model features a total of 80 billion parameters with 13 billion active parameters. It not only delivers high-performance results but also achieves optimal resource efficiency, successfully balancing computational power and resource utilization.

Key Features and Advantages

• Compact yet Powerful: With only 13 billion active parameters (out of a total of 80 billion), the model delivers competitive performance on a wide range of benchmark tasks, rivaling much larger models.
• Hybrid Reasoning Support: Supports both fast and slow thinking modes, allowing users to flexibly choose according to their needs.
• Ultra-Long Context Understanding: Natively supports a 256K context window, maintaining stable performance on long-text tasks.
• Enhanced Agent Capabilities: Optimized for agent tasks, achieving leading results on benchmarks such as BFCL-v3, τ-Bench and C3-Bench.
• Efficient Inference: Utilizes Grouped Query Attention (GQA) and supports multiple quantization formats, enabling highly efficient inference.

Why Choose Hunyuan-A13B?

As a powerful yet computationally efficient large model, Hunyuan-A13B is an ideal choice for researchers and developers seeking high performance under resource constraints. Whether for academic research, cost-effective AI solution development, or innovative application exploration, this model provides a robust foundation for advancement.

 

Related News

• 2025.6.27 We have open-sourced Hunyuan-A13B-Pretrain , Hunyuan-A13B-Instruct , Hunyuan-A13B-Instruct-FP8 , Hunyuan-A13B-Instruct-GPTQ-Int4 on Hugging Face. In addition, we have released a technical report and a training and inference operation manual, which provide detailed information about the model’s capabilities as well as the operations for training and inference.

Benchmark

Note: The following benchmarks are evaluated by TRT-LLM-backend on several base models.

Model	Hunyuan-Large	Qwen2.5-72B	Qwen3-A22B	Hunyuan-A13B
MMLU	88.40	86.10	87.81	88.17
MMLU-Pro	60.20	58.10	68.18	67.23
MMLU-Redux	87.47	83.90	87.40	87.67
BBH	86.30	85.80	88.87	87.56
SuperGPQA	38.90	36.20	44.06	41.32
EvalPlus	75.69	65.93	77.60	78.64
MultiPL-E	59.13	60.50	65.94	69.33
MBPP	72.60	76.00	81.40	83.86
CRUX-I	57.00	57.63	-	70.13
CRUX-O	60.63	66.20	79.00	77.00
MATH	69.80	62.12	71.84	72.35
CMATH	91.30	84.80	-	91.17
GSM8k	92.80	91.50	94.39	91.83
GPQA	25.18	45.90	47.47	49.12

Hunyuan-A13B-Instruct has achieved highly competitive performance across multiple benchmarks, particularly in mathematics, science, agent domains, and more. We compared it with several powerful models, and the results are shown below.

Topic	Bench	OpenAI-o1-1217	DeepSeek R1	Qwen3-A22B	Hunyuan-A13B-Instruct
Mathematics	AIME 2024 AIME 2025 MATH	74.3 79.2 96.4	79.8 70 94.9	85.7 81.5 94.0	87.3 76.8 94.3
Science	GPQA-Diamond OlympiadBench	78 83.1	71.5 82.4	71.1 85.7	71.2 82.7
Coding	Livecodebench Fullstackbench ArtifactsBench	63.9 64.6 38.6	65.9 71.6 44.6	70.7 65.6 44.6	63.9 67.8 43
Reasoning	BBH DROP ZebraLogic	80.4 90.2 81	83.7 92.2 78.7	88.9 90.3 80.3	89.1 91.1 84.7
Instruction Following	IF-Eval SysBench	91.8 82.5	88.3 77.7	83.4 74.2	84.7 76.1
Text Creation	LengthCtrl InsCtrl	60.1 74.8	55.9 69	53.3 73.7	55.4 71.9
NLU	ComplexNLU Word-Task	64.7 67.1	64.5 76.3	59.8 56.4	61.2 62.9
Agent	BFCL v3 τ-Bench ComplexFuncBench C3-Bench	67.8 60.4 47.6 58.8	56.9 43.8 41.1 55.3	70.8 44.6 40.6 51.7	78.3 54.7 61.2 63.5

 

Quickstart

llama.cpp

You can clone llama.cpp and install by its official guide. You can run inference through the following code.

llama-cli -hf tencent/Hunyuan-A13B-Instruct-GGUF:Q4_0 -p "Write a short summary of the benefits of regular exercise" -n 4096 temp 0.7 --top-k 20 --top-p 0.8 --repeat-penalty 1.05 --no-warmup

ollama

Will be supported in the future. Currently it is recommended to use llama.cpp for inference.

Contact Us

If you would like to leave a message for our R&D and product teams, Welcome to contact our open-source team . You can also contact us via email ([email protected]).