CodeQwen1.5-7B-Chat
| Property | Value |
|---|---|
| Parameter Count | 7.25B |
| Model Type | Decoder-only Language Model |
| License | tongyi-qianwen |
| Paper | arXiv:2309.16609 |
| Context Length | 64K tokens |
What is CodeQwen1.5-7B-Chat?
CodeQwen1.5-7B-Chat is a specialized code-generation language model built on the Qwen1.5 architecture. Trained on 3 trillion tokens of code data, it represents a significant advancement in AI-powered code generation and understanding. This model incorporates group query attention (GQA) for efficient inference and supports an impressive context length of 64K tokens.
Implementation Details
Built using the transformer architecture, CodeQwen1.5-7B-Chat requires transformers>=4.37.0 for proper functionality. The model utilizes BF16 tensor type and implements state-of-the-art attention mechanisms for optimal performance in code-related tasks.
- Comprehensive support for 92 programming languages
- Optimized for text-to-SQL operations and bug fixing
- Implements efficient group query attention (GQA)
- Extensive pretraining on 3 trillion tokens
Core Capabilities
- Advanced code generation across multiple programming languages
- Long context understanding up to 64K tokens
- Specialized text-to-SQL conversion
- Automated bug detection and fixing
- Chat-optimized interactions for programming assistance
Frequently Asked Questions
Q: What makes this model unique?
CodeQwen1.5-7B-Chat stands out for its specialized focus on code generation and understanding, supporting 92 programming languages while maintaining a relatively compact 7.25B parameter size. Its 64K token context length and optimization for text-to-SQL tasks make it particularly valuable for software development workflows.
Q: What are the recommended use cases?
The model excels in code generation, bug fixing, text-to-SQL conversion, and general programming assistance. It's particularly suitable for developers seeking AI assistance in coding tasks, code review, and database query optimization.
