CodeQwen1.5-7B-Chat-GGUF
| Property | Value |
|---|---|
| Parameter Count | 7.25B |
| License | tongyi-qianwen |
| Research Paper | arXiv:2309.16609 |
| Context Length | 64K tokens |
| Supported Languages | 92 coding languages |
What is CodeQwen1.5-7B-Chat-GGUF?
CodeQwen1.5-7B-Chat-GGUF is a specialized code generation model based on the Qwen1.5 architecture, specifically optimized for programming tasks. It's a transformer-based decoder-only language model that has been trained on 3 trillion tokens of code data, making it particularly effective for software development tasks.
Implementation Details
The model implements Group Query Attention (GQA) for efficient inference and is available in various GGUF quantization formats including q2_k, q3_k_m, q4_0, q4_k_m, q5_0, q5_k_m, q6_k, and q8_0. This enables deployment across different computational resources while maintaining performance.
- Transformer-based decoder-only architecture
- GQA implementation for faster inference
- Multiple quantization options for flexibility
- 64K token context window
Core Capabilities
- Advanced code generation across 92 programming languages
- Strong performance in text-to-SQL tasks
- Bug fixing capabilities
- Long context understanding and generation
- Efficient deployment through GGUF optimization
Frequently Asked Questions
Q: What makes this model unique?
CodeQwen1.5 stands out for its specialized training on code data and support for an extensive range of programming languages, combined with a long context window of 64K tokens. The GGUF format makes it particularly suitable for efficient deployment and inference.
Q: What are the recommended use cases?
The model excels in code generation, text-to-SQL conversion, bug fixing, and general programming assistance. It's particularly useful for developers needing AI assistance across multiple programming languages and for projects requiring long-context understanding.
