Hugging Face Overview
Qwen2.5-Coder-7B-Instruct: Code-Optimized LLM
This model is the instruction-tuned 7.61 billion parameter variant of the Qwen2.5-Coder series, developed by Qwen. It represents a significant advancement over its predecessor, CodeQwen1.5, with enhanced capabilities across various coding tasks.
Key Capabilities & Features
- Advanced Code Performance: Demonstrates significant improvements in code generation, code reasoning, and code fixing.
- Extensive Training: Trained on 5.5 trillion tokens, including a substantial amount of source code, text-code grounding, and synthetic data.
- Long-Context Support: Features a full context length of 131,072 tokens, with support for handling even longer texts up to 128K tokens using YaRN scaling.
- Comprehensive Foundation: Designed to serve as a robust foundation for real-world applications like Code Agents, while also maintaining strong performance in mathematics and general competencies.
- Architecture: Utilizes transformers with RoPE, SwiGLU, RMSNorm, and Attention QKV bias.
Good For
- Code Generation: Ideal for generating high-quality code snippets and functions.
- Code Reasoning: Excels at understanding and solving complex coding problems.
- Code Fixing: Capable of identifying and correcting errors in code.
- Code Agent Development: Provides a strong base for building intelligent code-centric agents.
- Long Code Contexts: Suitable for tasks requiring analysis or generation over very large codebases or extensive documentation due to its 131K context window.