Qwen2.5-3B-Instruct-unsloth-bnb-4bit
| Property | Value |
|---|---|
| Model Type | Instruction-tuned Language Model |
| Base Architecture | Qwen2.5 |
| Quantization | 4-bit Dynamic Quantization |
| Context Length | 32,768 tokens |
| Repository | Hugging Face |
What is Qwen2.5-3B-Instruct-unsloth-bnb-4bit?
This is a highly optimized 4-bit quantized version of the Qwen2.5-3B-Instruct model, enhanced by Unsloth's dynamic quantization technology. It offers significant memory savings while maintaining model performance through selective quantization techniques.
Implementation Details
The model utilizes transformers architecture with RoPE, SwiGLU, RMSNorm, and Attention QKV bias. It features specialized optimizations that enable up to 70% memory reduction and 2-5x faster training speeds compared to standard implementations.
- Dynamic 4-bit quantization for optimal performance-memory trade-off
- Supports full 32,768 token context length
- Compatible with various export formats including GGUF and vLLM
- Integrates with Hugging Face Transformers library (requires version ≥4.37.0)
Core Capabilities
- Multilingual support for 29+ languages
- Enhanced instruction following and long-text generation
- Improved structured data handling and JSON output
- Advanced role-play implementation and condition-setting
- Specialized capabilities in coding and mathematics
Frequently Asked Questions
Q: What makes this model unique?
This model stands out through Unsloth's Dynamic 4-bit Quants technology, which selectively quantizes the model to maintain accuracy while significantly reducing memory usage and increasing training speed. It's specifically optimized for efficient deployment while preserving the advanced capabilities of the Qwen2.5 architecture.
Q: What are the recommended use cases?
The model is well-suited for instruction-following tasks, multilingual applications, code generation, and mathematical problems. It's particularly effective in scenarios where memory efficiency is crucial while maintaining high-quality output.
