Qwen2.5-14B-Instruct-unsloth-bnb-4bit
| Property | Value |
|---|---|
| Model Size | 14B parameters |
| Quantization | 4-bit Dynamic Quantization |
| Developer | Unsloth (Based on Qwen2.5) |
| Paper | Qwen2 Technical Report |
What is Qwen2.5-14B-Instruct-unsloth-bnb-4bit?
This is an optimized version of the Qwen2.5-14B-Instruct model, specifically quantized by Unsloth to run efficiently in 4-bit precision. The model implements Dynamic 4-bit Quantization, which selectively quantizes different parts of the model to maintain accuracy while significantly reducing memory usage and increasing processing speed.
Implementation Details
The model is built on the Qwen2.5 architecture, featuring transformers with RoPE, SwiGLU, RMSNorm, and Attention QKV bias. It utilizes Unsloth's optimization techniques to achieve up to 70% memory reduction while maintaining model performance.
- Selective 4-bit quantization for optimal accuracy
- 60% reduced memory footprint
- 2x faster training capabilities
- Compatible with various export formats (GGUF, vLLM)
Core Capabilities
- Context length support up to 128K tokens
- Generation capability up to 8K tokens
- Multilingual support for 29+ languages
- Enhanced instruction following and structured data handling
- Improved capabilities in coding and mathematics
- Advanced role-play implementation and condition-setting
Frequently Asked Questions
Q: What makes this model unique?
This model combines the powerful capabilities of Qwen2.5 with Unsloth's innovative 4-bit quantization approach, offering significant memory savings and speed improvements without compromising performance. The selective quantization technique sets it apart from standard 4-bit quantized models.
Q: What are the recommended use cases?
The model is particularly well-suited for applications requiring efficient deployment of large language models with limited computational resources. It excels in tasks involving coding, mathematics, multilingual processing, and structured data handling while maintaining low memory overhead.
