DeepSeek-R1-1.5B-Qwen-MNN
| Property | Value |
|---|---|
| Model Size | 1.5B parameters |
| Quantization | 4-bit |
| Framework | MNN |
| Source Model | DeepSeek-R1-1.5B-Qwen |
| Repository | Hugging Face |
What is DeepSeek-R1-1.5B-Qwen-MNN?
DeepSeek-R1-1.5B-Qwen-MNN is a highly optimized version of the DeepSeek language model, specifically converted for deployment using the MNN (Mobile Neural Network) framework. This model represents a significant achievement in model optimization, featuring 4-bit quantization that substantially reduces its memory footprint while maintaining performance.
Implementation Details
The model is implemented using MNN's specialized architecture, with several key optimizations enabled including low memory mode and transformer fusion support. It requires specific compilation flags for optimal performance, including MNN_LOW_MEMORY, MNN_CPU_WEIGHT_DEQUANT_GEMM, and MNN_SUPPORT_TRANSFORMER_FUSE.
- 4-bit quantization for efficient deployment
- Optimized for CPU inference
- Transformer fusion support for improved performance
- Low memory operation mode
Core Capabilities
- Efficient inference on resource-constrained devices
- Reduced memory footprint through quantization
- Compatible with MNN's compiler and runtime optimizations
- Supports standard language model operations
Frequently Asked Questions
Q: What makes this model unique?
This model stands out for its optimization for mobile and edge devices through MNN framework integration and 4-bit quantization, making it particularly suitable for deployment in resource-constrained environments.
Q: What are the recommended use cases?
The model is ideal for applications requiring efficient language model inference on edge devices, mobile applications, or environments where memory and computational resources are limited.
