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---
license: other
license_name: tencent-hunyuan-a13b
license_link: LICENSE
---
<p align="center">
<img src="https://dscache.tencent-cloud.cn/upload/uploader/hunyuan-64b418fd052c033b228e04bc77bbc4b54fd7f5bc.png" width="400"/> <br>
</p><p></p>
<p align="center">
 <a href="https://github.com/Tencent/Hunyuan-A13B"><b>GITHUB</b></a>  
## Model Introduction
The A13B models released by Tencent Hunyuan this time: [Tencent-Hunyuan-A13B-Pretrain](https://huggingface.co/tencent/Hunyuan-A13B-Pretrain) , [Tencent-Hunyuan-A13B-Instruct](https://huggingface.co/tencent/Hunyuan-A13B-Instruct) , [Tencent-Hunyuan-A13B-Instruct-FP8](https://huggingface.co/tencent/Tencent-Hunyuan-A13B-Instruct-FP8) and [Tencent-Hunyuan-A13B-Instruct-FP8](https://huggingface.co/tencent/Tencent-Hunyuan-A13B-Instruct-FP8), use better data allocation and training, have strong performance, and have achieved a good balance between computing and performance. It stands out from many large-scale language models and is currently one of the strongest Chinese Mixture of Experts (MoE) models, featuring a total of 80 billion parameters and 13 billion active parameters.
### Introduction to Technical Advantages
**Model**
- **High-Quality Synthetic Data**: By enhancing training with synthetic data, Hunyuan-A13B is able to learn richer representations, handle long-context inputs, and generalize better to unseen data.
- **KV Cache Compression**: Utilizing Grouped Query Attention (GQA) and Cross-Layer Attention (CLA) strategies, it significantly reduces memory usage and computational overhead of the KV cache, thereby improving inference throughput.
- **Expert-Specific Learning Rate Scaling**: Different learning rates are assigned to different experts, ensuring that each sub-model can effectively learn from the data and contribute to overall performance.
- **Long-Context Processing Capability**: Both the pre-trained model and the instruction-tuned model support text sequences of up to 256K tokens, significantly enhancing the ability to handle long-context tasks.
- **Extensive Benchmarking**: Extensive experiments across multiple languages and tasks have validated the practical effectiveness and safety of Hunyuan-A13B.
- **Hybrid Reasoning Capability**: It supports both fast thinking and slow thinking inference modes.
**Architecture**
Hunyuan-A13B adopts a Fine-grained Mixture of Experts (Fine-grained MoE) architecture, comprising a total of 80 billion parameters with 13 billion active parameters. The model has been trained on over 20 trillion tokens. It supports a context length of up to 256K tokens. The following are the detailed specifications of the model architecture:
- **Total Parameters**: 80B
- **Active Parameters**: 13B
- **Number of Layers**: 32
- **Attention Heads**: 32
- **Number of Shared Experts**: 1
- **Number of Non-Shared Experts**: 64
- **Routing Strategy**: Top-8
- **Activation Function**: SwiGLU
- **Hidden Layer Dimension**: 4096
- **Expert Hidden Layer Dimension**: 3072
## Related News
* 2025.6.27 We have open-sourced **Hunyuan-A13B-Pretrain** , **Hunyuan-A13B-Instruct** , **Hunyuan-A13B-Instruct-FP8** , **Hunyuan-A13B-Instruct** on Hugging Face.
<br>
## Benchmark
Note: The following benchmarks are evaluated by TRT-LLM-backend
| Model | Hunyuan-Large | Qwen2.5-72B | Qwen3-32B | Qwen3-A22B | Hunyuan-A13B |
|------------------|---------------|--------------|---------------|-------------|---------------|
| MMLU | 88.4 | 86.1 | 83.61 | 87.81 | 88.17 |
| MMLU-Pro | 60.20 | 58.10 | 65.54 | 68.18 | 67.23 |
| MMLU-Redux | 87.47 | 83.90 | 83.41 | 87.40 | 87.67 |
| BBH | 86.30 | 85.8 | 87.38 | 88.87 | 87.56 |
| SuperGPQA | 38.90 | 37.84 * | 39.78 | 44.06 | 41.32 |
| EvalPlus | 75.69 | 66.05 | 72.05 | 77.60 | 78.64 |
| MultiPL-E | 59.13 | 61.00 | 67.06 | 65.94 | 69.33 |
| MBPP | 72.60 | 84.70 | 78.20 | 81.40 | 83.86 |
| CRUX-O | 60.63 | 56.00 * | 72.50 | 79.00 | 77.00 |
| MATH | 69.80 | 62.1 | 61.62 | 71.84 | 72.35 |
| GSM8k | 92.80 | 91.5 | 93.40 | 94.39 | 91.83 |
| GPQA | - | 45.9 | 47.97 | 47.47 | 43.44 |
| INCLUDE | 66.48 | 76.98 * | 67.97 | 73.46 | 74.90 |
| MGSM | 67.52 | 79.53 * | 82.68 | 83.53 | 76.00 |
| MMMLU | 76.89 | 79.28 * | 83.83 | 86.70 | 84.68 |
| Topic | Bench | OpenAI-o1-1217 | DeepSeek R1 | Qwen3-A22B | Hunyuan-A13B-Instruct |
|:-------------------:|:-----------------------------:|:-------------:|:------------:|:-----------:|:---------------------:|
| **Mathematics** | AIME 2024<br>AIME 2025<br>MATH | 74.3<br>79.2<br>96.4 | 79.8<br>70<br>94.9 | 85.7<br>81.5<br>94.0 | 87.3<br>76.8<br>94.3 |
| **Science** | GPQA-Diamond<br>OlympiadBench | 78<br>83.1 | 71.5<br>82.4 | 71.1<br>85.7 | 71.2<br>82.7 |
| **Coding** | Livecodebench<br>Fullstackbench<br>ArtifactsBench | 63.9<br>64.6<br>38.6 | 65.9<br>71.6<br>44.6 | 70.7<br>65.6<br>44.6 | 63.9<br>67.8<br>43 |
| **Reasoning** | BBH<br>DROP<br>ZebraLogic | 80.4<br>90.2<br>81 | 83.7<br>92.2<br>78.7 | 88.9<br>90.3<br>80.3 | 89.1<br>91.1<br>84.7 |
| **Instruction<br>Following** | IF-Eval<br>SysBench | 91.8<br>82.5 | 88.3<br>77.7 | 83.4<br>74.2 | 84.7<br>76.1 |
| **Text<br>Creation**| LengthCtrl<br>InsCtrl | 60.1<br>74.8 | 55.9<br>69 | 53.3<br>73.7 | 55.4<br>71.9 |
| **NLU** | ComplexNLU<br>Word-Task | 64.7<br>67.1 | 64.5<br>81.8 | 59.8<br>56.4 | 61.2<br>62.9 |
| **Agent** | BDCL v3<br> $\tau$-bench<br>ComplexFuncBench<br> $C^3$-Bench | 67.8<br>60.4<br>47.6<br>58.8 | 63.8<br>58.7<br>n/a<br>55.3 | 70.8<br>46.7<br>n/a<br>51.7 | 78.3<br>54.7<br>51.2<br>63.5 |
| **Average** | - | n/a | n/a | n/a | n/a |
## Quick Start
You can refer to the content in [Hunyuan-A13B](https://github.com/Tencent-Hunyuan/Hunyuan-A13B) to get started quickly. The training and inference code can use the version provided in this github repository.
### Transformer
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
import os
def main():
model_name_or_path = os.environ['MODEL_PATH']
tokenizer = AutoTokenizer.from_pretrained(model_name_or_path, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(model_name_or_path, device_map="auto",
trust_remote_code=True) # You may want to use bfloat16 and/or move to GPU here
for name, param in model.named_parameters():
print(f"{name}: {param.size()}")
messages = [
{
"role": "system",
"content": "You are a helpful assistant.",
},
{"role": "user", "content": "Write a short summary of the benefits of regular exercise."},
]
tokenized_chat = tokenizer.apply_chat_template(messages, tokenize=True, add_generation_prompt=True, return_tensors="pt")
outputs = model.generate(tokenized_chat.to(model.device), max_new_tokens=100,do_sample=True)
print(tokenizer.decode(outputs[0]))
if __name__ == '__main__':
main()
```
## Deployment
For deployment, you can use frameworks such as *vLLM*, *SGLang*, or *TensorRT-LLM* to serve the model and create an OpenAI-compatible API endpoint.
### vllm
#### Docker Image
We provide a pre-built Docker image containing vLLM 0.8.5 with full support for this model. The official support is currently under development.
- To get started:
```
Pull the Docker image:docker pull xxx
```
- Start the API server:
```
docker start xxx
```
#### Source Code
Support for this model has been added via this PR: (https://github.com/vllm-project/vllm/pull/20114 )in the vLLM project.
You can build and run vLLM from source after merging this pull request into your local repository.
After applying the changes, you can start the API server by following the standard vLLM setup instructions.
### SGLlang
#### Docker Image
We also provide a pre-built Docker image based on the latest version of SGLang.
To get started:
- Pull the Docker image
```
docker pull xxx
```
- Start the API server:
```
docker run --gpus all \
--shm-size 32g \
-p 30000:30000 \
--ipc=host \
xxx \
python3 -m sglang.launch_server --model-path hunyuan/huanyuan_A13B --tp 4 --trust-remote-code --host 0.0.0.0 --port 30000
```
#### Source Code
The necessary integration has already been merged into the main branch via this PR(https://github.com/sgl-project/sglang/pull/7549 ).
Once you have cloned or updated your local SGLang repository, you can build and run the API server using the standard SGLang setup process.
After applying the changes, you can start the API server by following the standard SGLang setup instructions.
```
python3 -m sglang.launch_server --model-path hunyuan/huanyuan_A13B --tp 4 --trust-remote-code --host 0.0.0.0 --port 30000
```
### TensorRT-LLM
#### Docker Image
We also provide a pre-built Docker image based on the latest version of TensorRT-LLM.
To get started:
- Pull the Docker image
```
docker pull xxx
```
- Start the API server:
```
docker run --gpus all \
--shm-size 32g \
-p 30000:30000 \
--ipc=host \
xxx \
python3 -m sglang.launch_server --model-path hunyuan/huanyuan_A13B --tp 4 --trust-remote-code --host 0.0.0.0 --port 30000
```
#### Source Code
The necessary integration has already been merged into the main branch via this PR(xxx ).
Once you have cloned or updated your local TensorRT-LLM. repository, you can build and run the API server using the standard TensorRT-LLM. setup process.
After applying the changes, you can start the API server by following the standard TensorRT-LLM. setup instructions.
## Inference Performance
This section presents the efficiency test results of deploying various models using vLLM, including inference speed (tokens/s) under different batch sizes.
Evaluation Script:
```python
python3 benchmark_throughput.py --backend vllm \
--input-len 2048 \
--output-len 14336 \
--model $MODEL_PATH \
--tensor-parallel-size $TP \
--use-v2-block-manager \
--async-engine \
--trust-remote-code \
--num_prompts $BATCH_SIZE \
--max-num-seqs $BATCH_SIZE
```
| Inference Framework | Model | Number of GPUs (GPU productA) | input_length | batch=1 | batch=16 | batch=32 |
|------|-----------------------------|-----------|-------------------------|---------------------|----------------------|----------------------|
| vLLM | Hunyuan-A13B-Instruct | 8 | 2048 | 190.84 | 1246.54 | 1981.99 |
| vLLM | Hunyuan-A13B-Instruct | 4 | 2048 | 158.90 | 779.10 | 1301.75 |
| vLLM | Hunyuan-A13B-Instruct | 2 | 2048 | 111.72 | 327.31 | 346.54 |
| vLLM | Hunyuan-A13B-Instruct(int8 weight only) | 2 | 2048 | 109.10 | 444.17 | 721.93 |
| vLLM | Hunyuan-A13B-Instruct(W8A8C8-FP8) | 2 | 2048 | 91.83 | 372.01 | 617.70 |
| vLLM | Hunyuan-A13B-Instruct(W8A8C8-FP8) | 1 | 2048 | 60.07 | 148.80 | 160.41 |
## 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 (hunyuan_[email protected]). |