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tencent / Hunyuan-A13B-Instruct-GGUF
Model's Last Updated: July 15 2025
Introduction of Hunyuan-A13B-Instruct-GGUF
Model Details of Hunyuan-A13B-Instruct-GGUF
🤗 Hugging Face | 🖥️ Official Website | 🕖 HunyuanAPI | 🕹️ Demo | 🤖 ModelScope
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 -m Hunyuan-A13B-Instruct-Q4_0.gguf -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
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] ).
Runs of tencent Hunyuan-A13B-Instruct-GGUF on huggingface.co
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