Qwen2.5-7B-Instruct-AWQ
| Property | Value |
|---|---|
| Parameter Count | 7.61B |
| Model Type | Instruction-tuned Causal Language Model |
| Architecture | Transformers with RoPE, SwiGLU, RMSNorm |
| License | Apache 2.0 |
| Quantization | AWQ 4-bit |
| Context Length | 131,072 tokens |
What is Qwen2.5-7B-Instruct-AWQ?
Qwen2.5-7B-Instruct-AWQ represents a significant advancement in efficient language models, offering a 4-bit quantized version of the original model while maintaining high performance. As part of the Qwen2.5 series, it brings substantial improvements in knowledge representation, coding capabilities, and mathematical reasoning, all while reducing the computational footprint through AWQ quantization.
Implementation Details
The model features 28 layers with 28 attention heads for queries and 4 for key-values, implementing Group-Query Attention (GQA). It supports an impressive context length of 131,072 tokens and can generate up to 8,192 tokens, utilizing YaRN technology for enhanced length extrapolation.
- Architecture: Transformers with RoPE, SwiGLU, RMSNorm, and Attention QKV bias
- Quantization: AWQ 4-bit precision for efficient deployment
- Parameter Distribution: 6.53B non-embedding parameters out of 7.61B total
- Context Processing: YaRN-enabled scaling for long-text handling
Core Capabilities
- Enhanced instruction following and long text generation
- Improved structured data understanding and JSON output generation
- Multilingual support for 29+ languages
- Advanced coding and mathematical reasoning
- Efficient memory usage through 4-bit quantization
Frequently Asked Questions
Q: What makes this model unique?
The model combines the advanced capabilities of Qwen2.5 with efficient 4-bit quantization, making it particularly suitable for deployment scenarios where computational resources are constrained while maintaining high performance in tasks like coding, mathematics, and multilingual processing.
Q: What are the recommended use cases?
This model is ideal for applications requiring efficient deployment of language AI capabilities, including code generation, mathematical problem-solving, multilingual processing, and long-form content generation, particularly in resource-conscious environments.
