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.

Hunyuan-A13B-Instruct-GPTQ-Int4 is produced by AngelSlim , a more user-friendly, comprehensive and efficient model compression toolkit for models.

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 Inference 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 and τ-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.

Benchmark

Note: The following benchmarks are evaluated by TRT-LLM-backend

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	BDCL 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

Use with transformers

The following code snippet shows how to use the transformers library to load and apply the model. It also demonstrates how to enable and disable the reasoning mode , and how to parse the reasoning process along with the final output.

python

from transformers import AutoModelForCausalLM, AutoTokenizer
import os
import re

model_name_or_path = os.environ['MODEL_PATH']
# model_name_or_path = "tencent/Hunyuan-A13B-Instruct"

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
messages = [
    {"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",
                                                enable_thinking=True # Toggle thinking mode (default: True)
                                                )
                                                
outputs = model.generate(tokenized_chat.to(model.device), max_new_tokens=4096)

output_text = tokenizer.decode(outputs[0])

think_pattern = r'<think>(.*?)</think>'
think_matches = re.findall(think_pattern, output_text, re.DOTALL)

answer_pattern = r'<answer>(.*?)</answer>'
answer_matches = re.findall(answer_pattern, output_text, re.DOTALL)

think_content = [match.strip() for match in think_matches][0]
answer_content = [match.strip() for match in answer_matches][0]
print(f"thinking_content:{think_content}\n\n")
print(f"answer_content:{answer_content}\n\n")

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 vllm release is currently under development， note: cuda 12.8 is require for this docker.

To get started:

https://hub.docker.com/r/hunyuaninfer/hunyuan-large/tags

# docker hub:
docker pull hunyuaninfer/hunyuan-a13b:hunyuan-moe-A13B-vllm

# china mirror
docker pull docker.cnb.cool/tencent/hunyuan/hunyuan-a13b:hunyuan-moe-A13B-vllm

Download Model file:
- Huggingface: will download automicly by vllm.
- ModelScope: modelscope download --model Tencent-Hunyuan/Hunyuan-A13B-Instruct-GPTQ-Int4
Start the API server:

model download by huggingface:

docker run  --privileged --user root  --net=host --ipc=host \
        -v ~/.cache:/root/.cache/ \
        --gpus=all -it --entrypoint python  hunyuaninfer/hunyuan-a13b:hunyuan-moe-A13B-vllm
 \
         -m vllm.entrypoints.openai.api_server --host 0.0.0.0 --port 8000 \
         --tensor-parallel-size 2 --quantization gptq_marlin --model tencent/Hunyuan-A13B-Instruct-GPTQ-Int4 --trust-remote-code

model downloaded by modelscope:

docker run  --privileged --user root  --net=host --ipc=host \
        -v ~/.cache/modelscope:/root/.cache/modelscope \
        --gpus=all -it --entrypoint python   hunyuaninfer/hunyuan-a13b:hunyuan-moe-A13B-vllm \
         -m vllm.entrypoints.openai.api_server --host 0.0.0.0 --quantization gptq_marlin --tensor-parallel-size 2 --port 8000 \ 
         --model /root/.cache/modelscope/hub/models/Tencent-Hunyuan/Hunyuan-A13B-Instruct-GPTQ-Int4/ --trust_remote_code

TensorRT-LLM

Support for INT4 quantization on TensorRT-LLM for this model is in progress and will be available in a future update.

SGLang

Support for INT4 quantization on sglang for this model is in progress and will be available in a future update.

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_opensource@tencent.com).

tencent/Hunyuan-A13B-Instruct-GPTQ-Int4