Qwen2.5-7B
| Property | Value |
|---|---|
| Parameter Count | 7.61B |
| License | Apache-2.0 |
| Context Length | 131,072 tokens |
| Architecture | Transformers with RoPE, SwiGLU, RMSNorm |
| Paper | Technical Report |
What is Qwen2.5-7B?
Qwen2.5-7B is a state-of-the-art base language model that represents the latest advancement in the Qwen series. As a foundation model with 7.61B parameters, it's designed to serve as a versatile base for various downstream applications through fine-tuning and additional training.
Implementation Details
The model features a sophisticated architecture implementing 28 layers with specialized attention mechanisms, including 28 attention heads for queries and 4 for key-values (GQA). It's built using the transformers framework with advanced components like RoPE, SwiGLU, and RMSNorm, enabling efficient processing and generation of text.
- BF16 tensor type for optimal performance
- 28-layer architecture with GQA attention mechanism
- 6.53B non-embedding parameters
- Supports context length up to 131,072 tokens
Core Capabilities
- Enhanced knowledge representation and reasoning
- Superior coding and mathematical capabilities
- Support for 29+ languages including major world languages
- Extended context processing up to 128K tokens
- Generation capability up to 8K tokens
- Improved structured data handling and JSON output
Frequently Asked Questions
Q: What makes this model unique?
Qwen2.5-7B stands out for its exceptional balance of size and capability, offering extensive multilingual support and significantly improved capabilities in specialized domains like coding and mathematics, while maintaining a manageable parameter count of 7.61B.
Q: What are the recommended use cases?
While this is a base model not recommended for direct conversational use, it's ideal for further fine-tuning through SFT, RLHF, or continued pretraining for specific applications in areas such as code generation, mathematical problem-solving, and multilingual text processing.
