unsloth/Qwen2.5-Coder-14B is a 14.8 billion parameter causal language model from the Qwen2.5-Coder series, developed by Qwen. This model is specifically optimized for code generation, code reasoning, and code fixing, building upon the strong Qwen2.5 foundation. It incorporates 5.5 trillion training tokens, including extensive source code and text-code grounding, making it highly effective for real-world coding applications and Code Agents.
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Qwen2.5-Coder-14B Overview
Qwen2.5-Coder-14B is a 14.8 billion parameter model from the latest Qwen2.5-Coder series, developed by Qwen. This series, formerly known as CodeQwen, focuses on enhancing coding capabilities across various model sizes. The 14B variant is part of a family that scales from 0.5B to 32B parameters.
Key Capabilities
- Significant improvements in code generation, code reasoning, and code fixing.
- Extensive training data: Trained on 5.5 trillion tokens, including source code, text-code grounding, and synthetic data.
- Foundation for Code Agents: Designed to support real-world applications like Code Agents, while maintaining strong performance in mathematics and general competencies.
- Causal Language Model: Utilizes a transformer architecture with RoPE, SwiGLU, RMSNorm, Attention QKV bias, and tied word embeddings.
- Large Context Length: Supports a full context length of 32,768 tokens.
Good For
- Code-specific tasks: Ideal for developers requiring advanced code generation, debugging, and reasoning.
- Building Code Agents: Provides a robust foundation for developing intelligent coding assistants.
- Post-training applications: Suitable as a base model for further fine-tuning (SFT, RLHF) or continued pretraining to adapt to specific conversational or text completion needs.