Qwen2.5-32B-Instruct-GPTQ-Int8
| Property | Value |
|---|---|
| Parameter Count | 32.5B (31.0B Non-Embedding) |
| License | Apache 2.0 |
| Context Length | 131,072 tokens |
| Quantization | GPTQ 8-bit |
| Research Paper | arxiv:2407.10671 |
What is Qwen2.5-32B-Instruct-GPTQ-Int8?
Qwen2.5-32B-Instruct-GPTQ-Int8 is a quantized version of the latest Qwen2.5 series large language model, optimized for efficient deployment while maintaining high performance. This model represents a significant advancement in the Qwen series, featuring 8-bit precision quantization that reduces memory requirements while preserving model capabilities.
Implementation Details
The model is built on a transformer architecture with several advanced features including RoPE, SwiGLU, RMSNorm, and Attention QKV bias. It employs 64 layers with 40 attention heads for Q and 8 for KV, implementing Group-Query Attention (GQA) for efficient processing.
- Architecture: Transformer-based with advanced attention mechanisms
- Layer Count: 64 layers
- Attention Structure: 40 heads for queries, 8 for key-value pairs
- Context Processing: Supports up to 131,072 tokens with 8,192 token generation
- Optimization: GPTQ 8-bit quantization for efficient deployment
Core Capabilities
- Enhanced knowledge base and improved capabilities in coding and mathematics
- Superior instruction following and long-text generation
- Structured data understanding and JSON output generation
- Multilingual support for 29+ languages
- Long-context processing with YaRN technology support
- Improved role-play implementation and chatbot condition-setting
Frequently Asked Questions
Q: What makes this model unique?
This model uniquely combines extensive parameter count (32.5B) with efficient 8-bit quantization, making it more deployable while maintaining strong performance across multiple domains. Its implementation of YaRN technology for long-context processing sets it apart in handling extensive text inputs.
Q: What are the recommended use cases?
The model excels in multilingual applications, coding tasks, mathematical problems, and scenarios requiring long-context understanding. It's particularly suitable for applications needing structured output generation, chatbot implementations, and complex instruction-following tasks while operating under memory constraints.
