Qwen1.5-14B-Chat-GGUF
| Property | Value |
|---|---|
| Parameter Count | 14.2B |
| License | tongyi-qianwen |
| Paper | Research Paper |
| Architecture | Transformer-based decoder-only |
What is Qwen1.5-14B-Chat-GGUF?
Qwen1.5-14B-Chat-GGUF is a powerful quantized version of the Qwen1.5 language model series, representing a significant advancement in transformer-based AI technology. This 14.2B parameter model is designed for efficient deployment while maintaining high performance, featuring various quantization options from q2_k to q8_0 to balance efficiency and accuracy.
Implementation Details
The model is built on an advanced transformer architecture incorporating several key innovations such as SwiGLU activation, attention QKV bias, and group query attention. It supports an impressive 32K context length and includes an improved tokenizer optimized for multiple natural languages and code processing.
- Multiple quantization options (q2_k through q8_0) with documented perplexity metrics
- Stable 32K context length support
- Enhanced multilingual capabilities
- No requirement for trust_remote_code
Core Capabilities
- Advanced chat functionality with improved human preference alignment
- Efficient text generation and processing
- Strong multilingual support
- Code understanding and generation
- Long context handling up to 32K tokens
Frequently Asked Questions
Q: What makes this model unique?
This model stands out for its balance of size and capability, offering 14.2B parameters with various quantization options that maintain performance while reducing deployment requirements. It's part of the comprehensive Qwen1.5 series, which represents a significant improvement over previous versions in terms of human preference and multilingual support.
Q: What are the recommended use cases?
The model is well-suited for chat applications, text generation tasks, multilingual processing, and scenarios requiring long context understanding. It's particularly valuable for applications needing efficient deployment while maintaining high-quality output.
