Qwen2.5-14B-Instruct-GPTQ-Int4
| Property | Value |
|---|---|
| Parameter Count | 14.7B (13.1B Non-Embedding) |
| Model Type | Causal Language Model (Instruction-tuned) |
| Architecture | Transformer with RoPE, SwiGLU, RMSNorm, and Attention QKV bias |
| Context Length | 131,072 tokens (128K) |
| Generation Length | 8,192 tokens |
| Quantization | GPTQ 4-bit |
What is Qwen2.5-14B-Instruct-GPTQ-Int4?
Qwen2.5-14B-Instruct-GPTQ-Int4 is a quantized version of the latest Qwen2.5 series, representing a significant advancement in large language models. This 4-bit quantized model maintains the powerful capabilities of the original while reducing the computational requirements through efficient compression. It features 48 layers and 40 attention heads for queries with 8 for keys/values, implementing grouped-query attention (GQA) for improved efficiency.
Implementation Details
The model leverages advanced architectural components including rotary positional embeddings (RoPE), SwiGLU activations, and RMSNorm for enhanced performance. For handling long contexts, it implements YaRN scaling, allowing for effective processing of sequences up to 128K tokens while maintaining the ability to generate up to 8K tokens in response.
- Advanced architecture with 48 layers and GQA attention mechanism
- 4-bit quantization using GPTQ for efficient deployment
- Support for over 29 languages including major world languages
- Integrated YaRN scaling for improved long-context handling
Core Capabilities
- Enhanced coding and mathematical problem-solving abilities
- Improved instruction following and long-text generation
- Superior handling of structured data and JSON output
- Robust multilingual support across 29+ languages
- Efficient processing of long contexts up to 128K tokens
- Advanced role-play implementation and chatbot condition-setting
Frequently Asked Questions
Q: What makes this model unique?
This model stands out for its combination of efficient 4-bit quantization while maintaining high performance across technical tasks, especially in coding and mathematics. Its extensive context length of 128K tokens and support for 29+ languages make it particularly versatile for diverse applications.
Q: What are the recommended use cases?
The model excels in technical applications including code generation, mathematical problem-solving, and handling structured data. It's particularly well-suited for applications requiring long context understanding, multilingual capabilities, and complex instruction following in production environments where computational efficiency is crucial.
