Hunyuan-7B-Instruct
| Property | Value |
|---|---|
| Author | Tencent |
| Model Size | 7B parameters |
| Context Length | 256K tokens |
| Model URL | https://huggingface.co/tencent/Hunyuan-7B-Instruct |
What is Hunyuan-7B-Instruct?
Hunyuan-7B-Instruct is a state-of-the-art language model developed by Tencent, representing one of the strongest Chinese 7B Dense models available. Released alongside its pre-trained counterpart, this instruction-tuned model demonstrates exceptional performance across various benchmarks while maintaining computational efficiency.
Implementation Details
The model leverages advanced architectural features including Grouped Query Attention (GQA) and supports an impressive 256K token context window. It's fully compatible with the Hugging Face format and can be deployed using either vLLM or TensorRT-LLM backends, with the vLLM solution currently available and TRT-LLM planned for future release.
- Achieves superior performance in Chinese-language tasks (CMMLLU: 82.29%, C-Eval: 81.8%)
- Demonstrates strong mathematical reasoning capabilities (GSM8K: 90.14%)
- Features efficient inference speed: 78.9 tokens/s for batch size 1, scaling to 279.5 tokens/s for batch size 4
- Supports both vLLM and TensorRT-LLM deployment options
Core Capabilities
- Exceptional performance in Chinese language understanding and generation
- Strong mathematical and reasoning capabilities
- Extended context length handling up to 256K tokens
- Efficient inference with multiple backend options
- Compatible with popular fine-tuning frameworks
Frequently Asked Questions
Q: What makes this model unique?
The model stands out for its balanced approach to performance and computation, particularly excelling in Chinese language tasks while maintaining strong capabilities across general language understanding and mathematical reasoning. The extended 256K context window and GQA attention mechanism make it particularly suitable for long-form content processing.
Q: What are the recommended use cases?
The model is well-suited for Chinese language processing tasks, including question-answering, content generation, and mathematical problem-solving. Its extended context length makes it particularly valuable for applications requiring long document understanding and generation.
