DeepSeek-R1-Distill-Qwen-1.5B-unsloth-bnb-4bit
| Property | Value |
|---|---|
| Base Model | Qwen2.5-Math-1.5B |
| License | MIT License |
| Quantization | 4-bit |
| Memory Reduction | 70% |
| Training Speed Improvement | 2x faster |
What is DeepSeek-R1-Distill-Qwen-1.5B-unsloth-bnb-4bit?
This is an optimized version of DeepSeek's R1-Distill model, specifically the 1.5B parameter variant based on Qwen2.5, implemented with Unsloth's optimization techniques. It represents a significant achievement in model efficiency, offering impressive reasoning capabilities while maintaining a small footprint through 4-bit quantization.
Implementation Details
The model leverages Unsloth's optimization framework to achieve significant performance improvements while reducing resource requirements. It's based on the DeepSeek-R1 distillation process, which transfers knowledge from larger models to create more efficient smaller versions.
- 4-bit quantization for reduced memory usage
- Optimized training process with 2x faster speed
- Compatible with popular deployment options including GGUF and vLLM
- Supports context length matching the base Qwen model
Core Capabilities
- Strong mathematical reasoning abilities (83.9% on MATH-500 benchmark)
- Efficient memory utilization with 70% reduction
- Accelerated training capabilities
- Support for both inference and further fine-tuning
Frequently Asked Questions
Q: What makes this model unique?
This model combines DeepSeek's powerful reasoning capabilities with Unsloth's optimization techniques, offering a highly efficient solution for deployment and fine-tuning. The 4-bit quantization and performance optimizations make it particularly suitable for resource-constrained environments.
Q: What are the recommended use cases?
The model is particularly well-suited for applications requiring mathematical reasoning and general language understanding while operating under memory constraints. It's ideal for research and production environments where efficiency is crucial but performance cannot be compromised.
