Qwen2.5-Coder-14B
| Property | Value |
|---|---|
| Parameter Count | 14.8B parameters |
| Model Type | Causal Language Model |
| License | Apache-2.0 |
| Context Length | 128K tokens |
| Architecture | Transformers with RoPE, SwiGLU, RMSNorm, and QKV bias |
| Paper | Technical Report |
What is Qwen2.5-Coder-14B?
Qwen2.5-Coder-14B is part of the latest series of code-specific large language models from Qwen. Built upon the foundation of Qwen2.5, this model represents a significant advancement in code-related AI capabilities, trained on an impressive 5.5 trillion tokens including source code, text-code grounding, and synthetic data.
Implementation Details
The model features a sophisticated architecture with 48 layers and a unique attention head configuration using 40 heads for queries and 8 for key-values (GQA). It supports an extended context length of up to 128K tokens through YaRN technology, making it suitable for processing extensive codebases and documentation.
- 14.7B total parameters (13.1B non-embedding)
- Full 131,072 token context length support
- Advanced attention mechanism with GQA
- Implements YaRN for enhanced length extrapolation
Core Capabilities
- Advanced code generation and completion
- Sophisticated code reasoning and analysis
- Efficient code fixing and debugging
- Strong mathematical reasoning abilities
- Support for Code Agents development
- Long-context processing up to 128K tokens
Frequently Asked Questions
Q: What makes this model unique?
The model stands out for its extensive training on 5.5 trillion tokens and its ability to handle extremely long contexts up to 128K tokens. It's specifically optimized for code-related tasks while maintaining strong general capabilities.
Q: What are the recommended use cases?
While it's not recommended for direct conversations, the model excels in code generation, analysis, and fixing. It's ideal for development environments, code review processes, and building code-related tools. Post-training methods like SFT or RLHF can be applied for specific use cases.
