Qwen2.5-Coder-14B-Instruct
| Property | Value |
|---|---|
| Parameter Count | 14.7B |
| License | Apache 2.0 |
| Context Length | 131,072 tokens |
| Architecture | Transformers with RoPE, SwiGLU, RMSNorm |
| Paper | Technical Report |
What is Qwen2.5-Coder-14B-Instruct?
Qwen2.5-Coder-14B-Instruct is a specialized instruction-tuned language model designed specifically for code generation, reasoning, and fixing. Built upon the Qwen2.5 architecture, it represents a significant advancement in code-specific AI models, trained on 5.5 trillion tokens including source code and text-code grounding data.
Implementation Details
The model features a sophisticated architecture with 48 layers and 40 attention heads for queries and 8 for key-values using Group Query Attention (GQA). It supports an impressive context length of 128K tokens through YaRN technology, making it suitable for processing extensive codebases.
- 14.7B total parameters (13.1B non-embedding)
- Advanced attention mechanism with GQA
- YaRN-enabled long context processing
- BF16 tensor type for efficient computation
Core Capabilities
- Advanced code generation and completion
- Sophisticated code reasoning and analysis
- Efficient code fixing and debugging
- Support for code agent applications
- Strong mathematical and general competencies
Frequently Asked Questions
Q: What makes this model unique?
The model's distinctive feature is its combination of extensive parameter count (14.7B), extremely long context length (128K tokens), and specialized training on code-specific tasks, making it particularly effective for programming applications while maintaining strong general capabilities.
Q: What are the recommended use cases?
The model excels in code generation, debugging, and analysis tasks. It's particularly well-suited for software development workflows, code review processes, and educational programming contexts. The long context length makes it especially valuable for working with large codebases.
