chetan272006/Qwen2.5-Coder-3B-Instruct
Qwen2.5-Coder-3B-Instruct is a 3.09 billion parameter instruction-tuned causal language model from the Qwen2.5-Coder series, developed by Qwen. This model is specifically designed for code-related tasks, offering significant improvements in code generation, reasoning, and fixing. With a full context length of 32,768 tokens, it is optimized for real-world applications such as Code Agents while maintaining strong general and mathematical competencies.
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Qwen2.5-Coder-3B-Instruct Overview
Qwen2.5-Coder-3B-Instruct is an instruction-tuned model from the Qwen2.5-Coder family, a series of Code-Specific Qwen large language models. This 3.09 billion parameter model builds upon the Qwen2.5 architecture, incorporating transformers with RoPE, SwiGLU, RMSNorm, Attention QKV bias, and tied word embeddings. It features a substantial context length of 32,768 tokens, making it suitable for complex coding tasks.
Key Capabilities
- Enhanced Code Performance: Demonstrates significant improvements in code generation, code reasoning, and code fixing compared to its predecessor, CodeQwen1.5.
- Extensive Training: Trained on a massive 5.5 trillion tokens, including source code, text-code grounding, and synthetic data, to bolster its coding abilities.
- Foundation for Code Agents: Provides a robust foundation for real-world applications like Code Agents, indicating its utility beyond basic code generation.
- Balanced Competencies: While excelling in coding, it also maintains strong performance in mathematics and general language understanding.
Good For
- Developers requiring a powerful, code-centric language model for tasks such as generating code snippets, debugging, or understanding complex code logic.
- Applications that benefit from a large context window for processing extensive codebases or detailed programming instructions.
- Integrating into development workflows or tools that require intelligent code assistance and agent-like capabilities.