DeepSeek-R1-Distill-Qwen-1.5B-bnb-4bit
| Property | Value |
|---|---|
| Base Model | Qwen2.5-Math-1.5B |
| Quantization | 4-bit |
| License | MIT License |
| Hugging Face | unsloth/DeepSeek-R1-Distill-Qwen-1.5B-bnb-4bit |
What is DeepSeek-R1-Distill-Qwen-1.5B-bnb-4bit?
This model is a highly efficient 4-bit quantized version of DeepSeek-R1-Distill-Qwen-1.5B, designed to provide strong reasoning capabilities while maintaining minimal computational requirements. It's part of the DeepSeek-R1 family, which represents a significant advancement in AI reasoning capabilities through reinforcement learning and distillation techniques.
Implementation Details
The model is built upon Qwen2.5-Math-1.5B and has been fine-tuned using carefully curated samples from the larger DeepSeek-R1 model. The 4-bit quantization significantly reduces memory usage while preserving model performance. The implementation includes temperature recommendations between 0.5 and 0.7 for optimal output.
- Optimized for minimal memory footprint
- Supports commercial use and modifications
- Compatible with standard deployment tools like vLLM
- Maximum generation length of 32,768 tokens
Core Capabilities
- Strong performance on mathematical reasoning (AIME 2024: 28.9% pass@1)
- Efficient text generation and analysis
- Balanced performance across various benchmarks
- Compatible with existing Qwen deployment pipelines
Frequently Asked Questions
Q: What makes this model unique?
This model represents a sweet spot between efficiency and performance, offering reasoning capabilities of larger models in a compact 1.5B parameter package with 4-bit quantization. It's particularly notable for its ability to handle complex mathematical and reasoning tasks despite its small size.
Q: What are the recommended use cases?
The model is well-suited for applications requiring efficient reasoning capabilities with limited computational resources. It's particularly effective for mathematical problem-solving, text analysis, and general-purpose language understanding tasks where memory efficiency is crucial.
