DeepSeek-V3-Pruned-Coder-411B
| Property | Value |
|---|---|
| Parameter Count | 441B |
| Model Type | Code Generation |
| Architecture | Pruned MoE (160 experts) |
| Original Model | DeepSeek-V3 |
| Author | huihui-ai |
| Model URL | huggingface.co/huihui-ai/DeepSeek-V3-Pruned-Coder-411B |
What is DeepSeek-V3-Pruned-Coder-411B?
DeepSeek-V3-Pruned-Coder-411B is an optimized version of the original DeepSeek-V3 model, specifically tailored for code generation tasks. The model has been strategically pruned from 256 experts to 160 experts, resulting in a 1/3 reduction in size while maintaining comparable performance levels. This pruning experiment demonstrates the possibility of optimizing large language models for specific professional requirements without sacrificing quality.
Implementation Details
The model can be implemented using either Ollama or the Transformers library. It supports 4-bit quantization and includes specialized configurations for efficient deployment. The implementation includes support for chat templates and streaming responses, making it suitable for interactive coding assistance.
- Reduced model size through expert pruning (160 experts)
- 4-bit quantization support with bfloat16 compute type
- Compatible with both Ollama and Transformers frameworks
- Maintains original model quality despite size reduction
Core Capabilities
- Specialized code generation and completion
- Efficient resource utilization through pruning
- Interactive chat-based coding assistance
- Support for long context windows
Frequently Asked Questions
Q: What makes this model unique?
This model represents a successful experiment in task-specific model pruning, achieving significant size reduction while maintaining performance. It demonstrates that large language models can be optimized for specific use cases without compromising their capabilities.
Q: What are the recommended use cases?
The model is specifically designed for code generation tasks and is ideal for developers seeking an efficient, resource-conscious coding assistant. It's particularly suitable for environments where computational resources are limited but high-quality code generation is required.
