indigoskyai/Qwen2.5-Coder-7B-Instruct
The Qwen2.5-Coder-7B-Instruct is a 7.61 billion parameter instruction-tuned causal language model developed by Qwen, part of the Qwen2.5-Coder series. This model is specifically optimized for code generation, code reasoning, and code fixing, building upon the Qwen2.5 architecture with 5.5 trillion training tokens. It supports a long context length of up to 131,072 tokens, making it suitable for complex coding tasks and real-world applications like Code Agents.
Loading preview...
Overview
Qwen2.5-Coder-7B-Instruct is an instruction-tuned model from the Qwen2.5-Coder series, a family of code-specific large language models developed by Qwen. This 7.61 billion parameter model is built on the robust Qwen2.5 foundation and has been extensively trained on 5.5 trillion tokens, including source code, text-code grounding, and synthetic data. It significantly enhances capabilities in code generation, reasoning, and fixing, while also maintaining strong performance in mathematics and general competencies.
Key Capabilities
- Enhanced Code Performance: Demonstrates significant improvements in code generation, code reasoning, and code fixing compared to its predecessor, CodeQwen1.5.
- Long Context Support: Features a full context length of 131,072 tokens, utilizing techniques like YaRN for effective long-text processing.
- Foundation for Code Agents: Provides a comprehensive base for real-world applications such as Code Agents, balancing coding prowess with general intelligence.
- Architecture: Employs a transformer architecture with RoPE, SwiGLU, RMSNorm, and Attention QKV bias.
Good For
- Developers requiring advanced code generation and debugging assistance.
- Applications that involve complex code reasoning or require processing extensive codebases.
- Building intelligent Code Agents that need both strong coding and general problem-solving abilities.