KalamZiraAI313786/Qwen2.5-Coder-3B
KalamZiraAI313786/Qwen2.5-Coder-3B is a 3.09 billion parameter causal language model from the Qwen2.5-Coder series, developed by Qwen. This pre-trained model is specifically optimized for code generation, code reasoning, and code fixing, building upon the strong Qwen2.5 foundation. It features a full 32,768 token context length and is designed for real-world applications like Code Agents, while also maintaining strengths in mathematics and general competencies.
Loading preview...
Qwen2.5-Coder-3B Overview
KalamZiraAI313786/Qwen2.5-Coder-3B is a 3.09 billion parameter model from the Qwen2.5-Coder series, a family of code-specific large language models developed by Qwen. This series, formerly known as CodeQwen, significantly improves upon CodeQwen1.5 by scaling training tokens to 5.5 trillion, including source code, text-code grounding, and synthetic data.
Key Capabilities
- Enhanced Code Abilities: Offers significant improvements in code generation, code reasoning, and code fixing.
- Broad Competencies: Maintains strong performance in mathematics and general language tasks, making it suitable for diverse applications.
- Architecture: Utilizes a transformer architecture with RoPE, SwiGLU, RMSNorm, Attention QKV bias, and tied word embeddings.
- Context Length: Supports a full 32,768 tokens, enabling processing of extensive codebases and complex prompts.
When to Use This Model
This pre-trained base model is ideal for developers looking to build specialized applications. It is recommended for tasks requiring:
- Code Agents: Its comprehensive foundation supports advanced real-world coding applications.
- Further Fine-tuning: Suitable for post-training methods like SFT, RLHF, or continued pretraining to adapt it for specific conversational or fill-in-the-middle tasks.
- Code-centric Development: Excels in scenarios where robust code generation, understanding, and debugging are critical.