Hunyuan is Tencent's open-source efficient large language model series, designed for versatile deployment across diverse computational environments. From edge devices to high-concurrency production systems, these models deliver optimal performance with advanced quantization support and ultra-long context capabilities.

We have released a series of Hunyuan dense models, comprising both pre-trained and instruction-tuned variants, with parameter scales of 0.5B, 1.8B, 4B, and 7B. These models adopt training strategies similar to the Hunyuan-A13B, thereby inheriting its robust performance characteristics. This comprehensive model family enables flexible deployment optimization - from resource-constrained edge computing with smaller variants to high-throughput production environments with larger models, all while maintaining strong capabilities across diverse scenarios.

Key Features and Advantages

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.

Benchmark

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

Model	Hunyuan-0.5B-Pretrain	Hunyuan-1.8B-Pretrain	Hunyuan-4B-Pretrain	Hunyuan-7B-Pretrain
MMLU	54.02	64.62	74.01	79.82
MMLU-Redux	54.72	64.42	73.53	79
MMLU-Pro	31.15	38.65	51.91	57.79
SuperGPQA	17.23	24.98	27.28	30.47
BBH	45.92	74.32	75.17	82.95
GPQA	27.76	35.81	43.52	44.07
GSM8K	55.64	77.26	87.49	88.25
MATH	42.95	62.85	72.25	74.85
EvalPlus	39.71	60.67	67.76	66.96
MultiPL-E	21.83	45.92	59.87	60.41
MBPP	43.38	66.14	76.46	76.19
CRUX-O	30.75	36.88	56.5	60.75
Chinese SimpleQA	12.51	22.31	30.53	38.86
simpleQA (5shot)	2.38	3.61	4.21	5.69

Topic	Bench	Hunyuan-0.5B-Instruct	Hunyuan-1.8B-Instruct	Hunyuan-4B-Instruct	Hunyuan-7B-Instruct
Mathematics	AIME 2024 AIME 2025 MATH	17.2 20 48.5	56.7 53.9 86	78.3 66.5 92.6	81.1 75.3 93.7
Science	GPQA-Diamond OlympiadBench	23.3 29.6	47.2 63.4	61.1 73.1	60.1 76.5
Coding	Livecodebench Fullstackbench	11.1 20.9	31.5 42	49.4 54.6	57 56.3
Reasoning	BBH DROP ZebraLogic	40.3 52.8 34.5	64.6 76.7 74.6	83 78.2 83.5	87.8 85.9 85.1
Instruction Following	IF-Eval SysBench	49.7 28.1	67.6 55.5	76.6 68	79.3 72.7
Agent	BFCL v3 τ-Bench ComplexFuncBench C3-Bench	49.8 14.4 13.9 45.3	58.3 18.2 22.3 54.6	67.9 30.1 26.3 64.3	70.8 35.3 29.2 68.5
Long Context	PenguinScrolls longbench-v2 FRAMES	53.9 34.7 41.9	73.1 33.2 55.6	83.1 44.1 79.2	82 43 78.6

Use with transformers

First, please install transformers. We will merge it into the main branch later.

pip install git+https://github.com/huggingface/transformers@4970b23cedaf745f963779b4eae68da281e8c6ca

Our model defaults to using slow-thinking reasoning, and there are two ways to disable CoT reasoning.

Pass "enable_thinking=False" when calling apply_chat_template.
Adding "/no_think" before the prompt will force the model not to use perform CoT reasoning. Similarly, adding "/think" before the prompt will force the model to perform CoT reasoning.

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.

we use tencent/Hunyuan-7B-Instruct for example

python

from transformers import AutoModelForCausalLM, AutoTokenizer
import os
import re

model_name_or_path = "tencent/Hunyuan-7B-Instruct"

tokenizer = AutoTokenizer.from_pretrained(model_name_or_path)
model = AutoModelForCausalLM.from_pretrained(model_name_or_path, device_map="auto")  # 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=2048)

output_text = tokenizer.decode(outputs[0])
print("output_text=",output_text)
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")

We recommend using the following set of parameters for inference. Note that our model does not have the default system_prompt.

json

{
  "do_sample": true,
  "top_k": 20,
  "top_p": 0.8,
  "repetition_penalty": 1.05,
  "temperature": 0.7
}

Training Data Format

If you need to fine-tune our Instruct model, we recommend processing the data into the following format, corresponding to both slow-thinking and fast-thinking scenarios.

python

# think_pattern
think = ""
answer = ""
think_pattern = f"<think>\n{think}\n</think>\n<answer>\n{answer}\n</answer>"

# fast think pattern
messages = [
    {"role": "system", "content": "You are a helpful assistant."},
    {"role": "user", "content": "/no_think海水为什么是咸的" },
    {"role": "assistant", "content": "<think>\n\n</think>\n<answer>\n海水是咸的主要是因为其中含有许多溶解在水中的盐类和矿物质。这些盐类和矿物质来自于地球表面的岩石和土壤中的化学物质，随着时间的推移，它们被带到了海洋中。当海水蒸发时，水分蒸发掉了，但盐类和矿物质仍然留在水中，导致海水变得更加咸味。因此，海水的咸度是由其中的盐类和矿物质的含量决定的。\n</answer>"}
]

# slow think pattern
messages = [
    {"role": "system", "content": "You are a helpful assistant."},
    {"role": "user", "content": "1+1=" },
    {"role": "assistant", "content": "<think>\n嗯，用户问的是1加1等于多少。首先，我需要确认这是一个基本的算术问题。1加1在十进制的数学体系中，通常的结果是2。不过，可能需要考虑是否有其他情况，比如二进制或者其他数制，但用户没有特别说明，所以默认应该是十进制。另外，有时候可能会有脑筋急转弯的情况，比如在某些语境下1+1可能等于1（比如1滴水加1滴水还是1滴水），但通常数学问题中都是2。所以最准确的回答应该是2。</think>\n<answer>\n在十进制的基本算术运算中，1加1的结果是2。这是数学中最基础的加法运算之一，遵循自然数的加法规则。因此，1 + 1 = 2。\n</answer>"}
]

from transformers import AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("your_tokenizer_path", trust_remote_code=True)
train_ids = tokenizer.apply_chat_template(messages)

Train with LLaMA-Factory

In the following chapter, we will introduce how to use LLaMA-Factory to fine-tune the Hunyuan model.

Prerequisites

Verify installation of the following dependencies:

LLaMA-Factory: Follow official installation guide
DeepSpeed (optional): Follow official installation guide
Transformer Library: Use the companion branch (Hunyuan-submitted code is pending review)
pip install git+https://github.com/huggingface/transformers@4970b23cedaf745f963779b4eae68da281e8c6ca

Data preparation

We need to prepare a custom dataset:

Organize your data in json format and place it in the data directory in LLaMA-Factory. The current implementation uses the sharegpt dataset format, which requires the following structure:

[
  {
    "messages": [
      {
        "role": "system",
        "content": "System prompt (optional)"
      },
      {
        "role": "user",
        "content": "Human instruction"
      },
      {
        "role": "assistant",
        "content": "Model response"
      }
    ]
  }
]

Refer to the Data Format section mentioned earlier for details.

Define your dataset in the data/dataset_info.json file using the following format:

"dataset_name": {
  "file_name": "dataset.json",
  "formatting": "sharegpt",
  "columns": {
    "messages": "messages"
  },
  "tags": {
    "role_tag": "role",
    "content_tag": "content",
    "user_tag": "user",
    "assistant_tag": "assistant",
    "system_tag": "system"
  }
}

Training execution

Copy all files from the train/llama_factory_support/example_configs directory to the example/hunyuan directory in LLaMA-Factory.
Modify the model path and dataset name in the configuration file hunyuan_full.yaml. Adjust other configurations as needed:

### model
model_name_or_path: [!!!add the model path here!!!]

### dataset
dataset: [!!!add the dataset name here!!!]

Execute training commands: *Single-node training Note: Set the environment variable DISABLE_VERSION_CHECK to 1 to avoid version conflicts.
```
export DISABLE_VERSION_CHECK=1
llamafactory-cli train examples/hunyuan/hunyuan_full.yaml
```
*Multi-node training Execute the following command on each node. Configure NNODES, NODE_RANK, MASTER_ADDR, and MASTER_PORT according to your environment:
```
export DISABLE_VERSION_CHECK=1
FORCE_TORCHRUN=1 NNODES=${NNODES} NODE_RANK=${NODE_RANK} MASTER_ADDR=${MASTER_ADDR} MASTER_PORT=${MASTER_PORT} \
llamafactory-cli train examples/hunyuan/hunyuan_full.yaml
```

Quantization Compression

We used our own AngleSlim compression tool to produce FP8 and INT4 quantization models. AngleSlim is a toolset dedicated to creating a more user-friendly, comprehensive and efficient model compression solution.

FP8 Quantization

We use FP8-static quantization, FP8 quantization adopts 8-bit floating point format, through a small amount of calibration data (without training) to pre-determine the quantization scale, the model weights and activation values will be converted to FP8 format, to improve the inference efficiency and reduce the deployment threshold. We you can use AngleSlim quantization, you can also directly download our quantization completed open source model to use LINK.

Int4 Quantization

We use the GPTQ and AWQ algorithm to achieve W4A16 quantization.

GPTQ processes the model weights layer by layer, uses a small amount of calibration data to minimize the reconfiguration error of the quantized weights, and adjusts the weights layer by layer by the optimization process of approximating the Hessian inverse matrix. The process eliminates the need to retrain the model and requires only a small amount of calibration data to quantize the weights, improving inference efficiency and lowering the deployment threshold. AWQ using a small amount of calibration data (without the need for training), the amplitude of the activation values is statistically calculated. For each weight channel, a scaling coefficient s is computed to expand the numerical range of important weights, allowing more information to be retained during quantization.

You can use AngleSlim quantization, you can also directly download our quantization completed open source model to use LINK.

Quantization Benchmark

This subsection describes the Benchmark metrics for the Hunyuan quantitative model.

Bench	Quantization	Hunyuan-0.5B-Instruct	Hunyuan-1.8B-Instruct	Hunyuan-4B-Instruct	Hunyuan-7B-Instruct
DROP	B16 FP8 Int4GPTQ Int4AWQ	52.8 51.6 50.9 48.9	76.7 75.1 73.0 71.7	78.2 78.3 78.1 78.2	85.9 86.0 85.7 85.9
GPQA-Diamond	B16 FP8 Int4GPTQ Int4AWQ	23.3 22.5 23.3 23.3	47.2 47.7 44.43 43.62	61.1 60.2 58.1 -	60.1 60.1 60.0 60.1
OlympiadBench	B16 FP8 Int4GPTQ Int4AWQ	29.6 29.6 26.8 26.3	63.4 62.5 60.9 61.7	73.1 73.1 71.1 71.2	76.5 76.6 76.2 76.4
AIME 2024	B16 FP8 Int4GPTQ Int4AWQ	17.2 17.2 - -	56.7 55.17 - -	78.3 76.6 - -	81.1 80.9 81.0 80.9

Deployment

For deployment, you can use frameworks such as TensorRT-LLM, vLLM, or SGLang to serve the model and create an OpenAI-compatible API endpoint.

image: https://hub.docker.com/r/hunyuaninfer/hunyuan-7B/tags

TensorRT-LLM

Docker Image

We provide a pre-built Docker image based on the latest version of TensorRT-LLM.

We use tencent/Hunyuan-7B-Instruct for example

To get started:

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

docker pull hunyuaninfer/hunyuan-7B:hunyuan-moe-7B-trtllm

docker run --privileged --user root --name hunyuanLLM_infer --rm -it --ipc=host --ulimit memlock=-1 --ulimit stack=67108864 --gpus=all hunyuaninfer/hunyuan-7B:hunyuan-moe-7B-trtllm

Prepare Configuration file:

cat >/path/to/extra-llm-api-config.yml <<EOF
use_cuda_graph: true
cuda_graph_padding_enabled: true
cuda_graph_batch_sizes:
- 1
- 2
- 4
- 8
- 16
- 32
print_iter_log: true
EOF

Start the API server:

trtllm-serve \
  /path/to/HunYuan-moe-7B \
  --host localhost \
  --port 8000 \
  --backend pytorch \
  --max_batch_size 32 \
  --max_num_tokens 16384 \
  --tp_size 2 \
  --kv_cache_free_gpu_memory_fraction 0.6 \
  --trust_remote_code \
  --extra_llm_api_options /path/to/extra-llm-api-config.yml

vllm

Start

Please use vLLM version v0.10.0 or higher for inference.

We use tencent/Hunyuan-7B-Instruct for example

Download Model file:
- Huggingface: will download automicly by vllm.
- ModelScope: modelscope download --model Tencent-Hunyuan/Hunyuan-7B-Instruct
model download by huggingface:

export MODEL_PATH=tencent/Hunyuan-7B-Instruct

model downloaded by modelscope:

export MODEL_PATH=/root/.cache/modelscope/hub/models/Tencent-Hunyuan/Hunyuan-7B-Instruct/

Start the API server:

shell

python3 -m vllm.entrypoints.openai.api_server \
    --host 0.0.0.0 \
    --port 8000 \
    --trust-remote-code \
    --model ${MODEL_PATH} \
    --tensor-parallel-size 1 \
    --dtype bfloat16 \
    --quantization experts_int8 \
    --served-model-name hunyuan \
    2>&1 | tee log_server.txt

After running service script successfully, run the request script

shell

curl http://0.0.0.0:8000/v1/chat/completions -H 'Content-Type: application/json' -d '{
"model": "hunyuan",
"messages": [
    {
        "role": "system",
        "content": [{"type": "text", "text": "You are a helpful assistant."}]
    },
    {
        "role": "user",
        "content": [{"type": "text", "text": "请按面积大小对四大洋进行排序，并给出面积最小的洋是哪一个？直接输出结果。"}]
    }
],
"max_tokens": 2048,
"temperature":0.7,
"top_p": 0.6,
"top_k": 20,
"repetition_penalty": 1.05,
"stop_token_ids": [127960]
}'

Quantitative model deployment

This section describes the process of deploying a post-quantization model using vLLM.

Default server in BF16.

Int8 quantitative model deployment

Deploying the Int8-weight-only version of the HunYuan-7B model only requires setting the environment variables

Next we start the Int8 service. Run:

shell

python3 -m vllm.entrypoints.openai.api_server \
    --host 0.0.0.0 \
    --port 8000 \
    --trust-remote-code \
    --model ${MODEL_PATH} \
    --tensor-parallel-size 1 \
    --dtype bfloat16 \
    --served-model-name hunyuan \
    --quantization experts_int8 \
    2>&1 | tee log_server.txt

Int4 quantitative model deployment

Deploying the Int4-weight-only version of the HunYuan-7B model only requires setting the environment variables , using the GPTQ method

export MODEL_PATH=PATH_TO_INT4_MODEL

Next we start the Int4 service. Run

shell

python3 -m vllm.entrypoints.openai.api_server \
    --host 0.0.0.0 \
    --port 8000 \
    --trust-remote-code \
    --model ${MODEL_PATH} \
    --tensor-parallel-size 1 \
    --dtype bfloat16 \
    --served-model-name hunyuan \
    --quantization gptq_marlin \
    2>&1 | tee log_server.txt

FP8 quantitative model deployment

Deploying the W8A8C8 version of the HunYuan-7B model only requires setting the environment variables

Next we start the FP8 service. Run

shell

python3 -m vllm.entrypoints.openai.api_server \
    --host 0.0.0.0 \
    --port 8000 \
    --trust-remote-code \
    --model ${MODEL_PATH} \
    --tensor-parallel-size 1 \
    --dtype bfloat16 \
    --served-model-name hunyuan \
    --kv-cache-dtype fp8 \
    2>&1 | tee log_server.txt

SGLang

Docker Image

We also provide a pre-built Docker image based on the latest version of SGLang.

We use tencent/Hunyuan-7B-Instruct for example

To get started:

Pull the Docker image

docker pull lmsysorg/sglang:latest

Start the API server:

docker run --entrypoint="python3" --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    --ulimit nproc=10000 \
    --privileged \
    --ipc=host \
     lmsysorg/sglang:latest \
    -m sglang.launch_server --model-path hunyuan/huanyuan_7B --tp 4 --trust-remote-code --host 0.0.0.0 --port 30000

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).

Yi30/Hunyuan-7B-Instruct-FP8-G2

Model Introduction

Key Features and Advantages

Related News

Benchmark

Use with transformers

Training Data Format

Train with LLaMA-Factory

Prerequisites

Data preparation

Training execution

Quantization Compression

FP8 Quantization

Int4 Quantization

Quantization Benchmark

Deployment

TensorRT-LLM

Docker Image

vllm

Start

Quantitative model deployment

Int8 quantitative model deployment

Int4 quantitative model deployment

FP8 quantitative model deployment

SGLang

Docker Image

Contact Us

Yi30/
Hunyuan-7B-Instruct-FP8-G2