Qwen2.5-1.5B-Instruct-GGUF
| Property | Value |
|---|---|
| Parameter Count | 1.78B |
| License | Apache 2.0 |
| Context Length | 32,768 tokens |
| Paper | Technical Report |
| Architecture | Transformers with RoPE, SwiGLU, RMSNorm |
What is Qwen2.5-1.5B-Instruct-GGUF?
Qwen2.5-1.5B-Instruct-GGUF is part of the latest Qwen2.5 series, representing a significant advancement in compact language models. This GGUF-formatted version offers efficient deployment while maintaining impressive capabilities across multiple domains.
Implementation Details
The model features a sophisticated architecture with 28 layers and employs grouped-query attention (GQA) with 12 heads for queries and 2 for key-values. It supports various quantization options including q2_K through q8_0, enabling flexible deployment based on resource constraints.
- 1.54B total parameters (1.31B non-embedding)
- Full 32,768 token context window
- 8,192 token generation capacity
- Multiple quantization options for deployment flexibility
Core Capabilities
- Enhanced knowledge and expertise in coding and mathematics
- Improved instruction following and long-text generation
- Structured data understanding and JSON output generation
- Support for 29+ languages including major global languages
- Robust role-play implementation and conversation handling
Frequently Asked Questions
Q: What makes this model unique?
The model stands out for its efficient balance between size and capability, offering extensive multilingual support and long context handling in a relatively compact form factor. Its GGUF format makes it particularly suitable for deployment in resource-constrained environments.
Q: What are the recommended use cases?
This model is ideal for chatbot applications, code generation, mathematical problem-solving, and multilingual text processing. It's particularly well-suited for scenarios requiring structured output generation and long-context understanding while maintaining reasonable resource requirements.
