Qwen2.5-14B-Instruct-AWQ
| Property | Value |
|---|---|
| Parameter Count | 14.7B (13.1B Non-Embedding) |
| Model Type | Causal Language Model (Instruction-tuned) |
| Architecture | Transformers with RoPE, SwiGLU, RMSNorm |
| License | Apache-2.0 |
| Context Length | 131,072 tokens |
| Quantization | AWQ 4-bit |
What is Qwen2.5-14B-Instruct-AWQ?
Qwen2.5-14B-Instruct-AWQ is an advanced quantized language model that represents a significant evolution in the Qwen series. As a 4-bit AWQ-quantized version of the original model, it maintains impressive capabilities while reducing computational requirements. The model features 48 layers and 40 attention heads for queries with 8 for key-values, implementing Grouped-Query Attention (GQA) architecture.
Implementation Details
The model leverages state-of-the-art architectural components including RoPE for positional encoding, SwiGLU activations, and RMSNorm for normalization. It supports an extensive context length of 131,072 tokens and can generate up to 8,192 tokens in a single pass. The implementation includes YaRN scaling for enhanced length extrapolation capabilities.
- 48-layer architecture with GQA attention mechanism
- 4-bit AWQ quantization for efficient deployment
- Support for 29+ languages including major global languages
- Integrated with latest transformers library
Core Capabilities
- Enhanced coding and mathematical reasoning abilities
- Improved instruction following and long-text generation
- Superior structured data understanding and JSON output generation
- Robust multilingual support across 29+ languages
- Extended context handling up to 128K tokens
Frequently Asked Questions
Q: What makes this model unique?
The model stands out for its combination of efficient 4-bit quantization while maintaining high performance across diverse tasks. Its extensive context length and multilingual capabilities make it particularly versatile for real-world applications.
Q: What are the recommended use cases?
The model excels in coding tasks, mathematical problem-solving, long-form content generation, and multilingual applications. It's particularly well-suited for applications requiring structured data handling and JSON output generation.
