Qwen2-7B-Instruct
| Property | Value |
|---|---|
| Parameter Count | 7.62B |
| License | Apache 2.0 |
| Context Length | 131,072 tokens |
| Paper | YARN Paper |
What is Qwen2-7B-Instruct?
Qwen2-7B-Instruct is a state-of-the-art instruction-tuned language model that represents the latest advancement in the Qwen series. Built on a 7.62B parameter architecture, this model demonstrates exceptional capabilities across various benchmarks, particularly excelling in coding tasks with up to 79.9% accuracy on HumanEval and impressive performance in mathematical reasoning.
Implementation Details
The model leverages advanced architectural elements including SwiGLU activation, attention QKV bias, and group query attention. It implements YARN technology for handling long contexts up to 131,072 tokens, making it particularly suitable for processing extensive documents. The model utilizes BF16 tensor type for efficient computation.
- Advanced Transformer architecture with SwiGLU activation
- Supports context length of 131,072 tokens through YARN implementation
- Optimized for both English and Chinese language tasks
- Comprehensive instruction tuning through supervised finetuning and direct preference optimization
Core Capabilities
- Strong performance in coding tasks (79.9% on HumanEval)
- Excellent mathematical reasoning (82.3% on GSM8K)
- High-quality multilingual understanding (77.2% on C-Eval)
- Superior performance on MT-Bench (8.41 score)
- Robust long-text processing capabilities
Frequently Asked Questions
Q: What makes this model unique?
The model stands out for its exceptional balance between size and performance, particularly in coding and mathematical tasks. Its implementation of YARN technology for handling long contexts up to 131K tokens is a significant differentiator from similar-sized models.
Q: What are the recommended use cases?
The model excels in coding assistance, mathematical problem-solving, and general language understanding tasks. It's particularly well-suited for applications requiring processing of long documents and multilingual capabilities in both English and Chinese.
