Qwen2.5-14B
| Property | Value |
|---|---|
| Parameter Count | 14.8B (13.1B Non-Embedding) |
| Architecture | Transformers with RoPE, SwiGLU, RMSNorm, and Attention QKV bias |
| Context Length | 131,072 tokens |
| License | Apache 2.0 |
| Paper | Technical Report |
What is Qwen2.5-14B?
Qwen2.5-14B is a state-of-the-art base language model that represents a significant advancement in the Qwen series. As a core component of the Qwen2.5 family, this 14B parameter model is designed to serve as a foundation for various downstream tasks and specialized applications. The model features 48 layers and implements a sophisticated attention mechanism with 40 heads for queries and 8 for key-values using Group Query Attention (GQA).
Implementation Details
The model architecture incorporates several cutting-edge techniques including RoPE (Rotary Position Embedding), SwiGLU activation functions, and RMSNorm layer normalization. It's implemented using BF16 tensor type for optimal performance and efficiency.
- 48 transformer layers with advanced attention mechanisms
- Supports context length of up to 131,072 tokens
- Implements Group Query Attention with 40/8 head configuration
- Optimized for BF16 precision training and inference
Core Capabilities
- Enhanced knowledge representation and retrieval
- Superior coding and mathematical reasoning abilities
- Support for 29+ languages including major world languages
- Capability to generate up to 8K tokens of coherent text
- Improved structured data understanding and JSON generation
- Advanced long-context processing up to 128K tokens
Frequently Asked Questions
Q: What makes this model unique?
This model stands out for its exceptional balance of size and capabilities, featuring enhanced knowledge representation and multilingual support while maintaining efficient computational requirements. The implementation of GQA and advanced architecture components makes it particularly suitable for various downstream applications.
Q: What are the recommended use cases?
While this is a base model not recommended for direct conversational use, it's ideal for further fine-tuning through SFT, RLHF, or continued pretraining. It's particularly well-suited for tasks requiring deep knowledge processing, code generation, and mathematical reasoning when appropriately fine-tuned.
