CodeQwen1.5-7B
| Property | Value |
|---|---|
| Parameter Count | 7.25B |
| License | tongyi-qianwen-research |
| Architecture | Transformer Decoder-only with GQA |
| Paper | Research Paper |
| Context Length | 64K tokens |
What is CodeQwen1.5-7B?
CodeQwen1.5-7B is a specialized code generation model built on the Qwen1.5 architecture, specifically designed for programming tasks. Trained on 3 trillion tokens of code data, it represents a significant advancement in AI-powered code generation and understanding. The model incorporates Group Query Attention (GQA) for efficient inference and supports an impressive context length of 64K tokens.
Implementation Details
Built as a decoder-only transformer model, CodeQwen1.5-7B requires transformers>=4.37.0 for proper functionality. The model utilizes BF16 tensor types and implements advanced attention mechanisms for optimal performance in code-related tasks.
- Transformer-based decoder-only architecture
- Group Query Attention (GQA) implementation
- 3 trillion tokens training dataset
- 64K token context window
Core Capabilities
- Support for 92 programming languages
- Advanced code generation and completion
- Text-to-SQL conversion capabilities
- Bug fixing functionality
- Long context understanding and generation
Frequently Asked Questions
Q: What makes this model unique?
CodeQwen1.5-7B stands out for its specialized focus on code generation, extensive language support, and impressive context length of 64K tokens. Its training on 3 trillion tokens of code data makes it particularly effective for programming-related tasks.
Q: What are the recommended use cases?
The model is ideal for code infilling, generation, and bug fixing tasks. While it's not recommended for direct chat applications, it's well-suited for fine-tuning and specialized code-related applications.
