Hunyuan 7B Instruct

🚀 Hunyuan-7B

The Hunyuan-7B models, including Hunyuan-7B-Pretrain and Hunyuan-7B-Instruct, use optimized data allocation and training, achieving a great balance between computing and performance, and standing out as one of the strongest Chinese 7B Dense models.

🚀 Quick Start

You can refer to the content in Tencent-Hunyuan-Large to get started quickly. The training and inference code can use the version provided in this github repository.

Inference Framework

• This open-source release offers two inference backend options tailored for the Hunyuan-7B model: the popular vLLM-backend and the TensorRT-LLM Backend. In this release, we are initially open-sourcing the vLLM solution, with plans to release the TRT-LLM solution in the near future.

✨ Features

Model Introduction

The 7B models released by Hunyuan this time: Hunyuan-7B-Pretrain and Hunyuan-7B-Instruct , 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 7B Dense models.

Introduction to Technical Advantages

Model

• Extended long text capability to 256K and utilizes Grouped Query Attention (GQA)

Inference Framework

• This open-source release offers two inference backend options tailored for the Hunyuan-7B model: the popular vLLM-backend and the TensorRT-LLM Backend. In this release, we are initially open-sourcing the vLLM solution, with plans to release the TRT-LLM solution in the near future.

Training Framework

• The Hunyuan-7B open-source model is fully compatible with the Hugging Face format, enabling researchers and developers to perform model fine-tuning using the hf-deepspeed framework. Learn more : Tencent-Hunyuan-Large 。

📚 Documentation

Related News

• 2025.1.24 We have open-sourced Hunyuan-7B-Pretrain , Hunyuan-7B-Instruct on Hugging Face.

Benchmark

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

Hunyuan-7B-Pretrain

Hunyuan-7B-Instruct

Inference Performance

This section presents the efficiency test results of deploying various models using vLLM, including inference speed (tokens/s) under different batch sizes.

📄 License

The model uses the tencent-license.

📞 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).