DeepSeek Coder 33B Instruct
| Property | Value |
|---|---|
| Parameter Count | 33.3B |
| Model Type | Instruction-tuned Code Generation |
| License | DeepSeek License |
| Tensor Type | BF16 |
| Training Data | 2T tokens (87% code, 13% language) |
What is deepseek-coder-33b-instruct?
DeepSeek Coder 33B Instruct is a state-of-the-art code generation model initialized from the deepseek-coder-33b-base and fine-tuned on 2B tokens of instruction data. It represents the largest variant in the DeepSeek Coder family, designed specifically for advanced code generation and understanding tasks across multiple programming languages.
Implementation Details
The model employs a sophisticated architecture with a 16K token window size and incorporates a unique fill-in-the-blank task during training. It's implemented using PyTorch and supports efficient inference through Transformers architecture.
- Built on 33.3B parameters for maximum capability
- Trained on a diverse dataset of code and natural language
- Supports both English and Chinese language interactions
- Uses BF16 tensor format for optimal performance
Core Capabilities
- Project-level code completion and infilling
- State-of-the-art performance on HumanEval, MultiPL-E, MBPP, DS-1000, and APPS benchmarks
- Advanced context understanding with 16K window size
- Multi-language code generation and analysis
- Instruction-following for complex coding tasks
Frequently Asked Questions
Q: What makes this model unique?
The model's massive scale (33B parameters), combined with its specialized training on 2T tokens of predominantly code data, makes it particularly powerful for code-related tasks. The addition of instruction-tuning and project-level context understanding sets it apart from traditional code models.
Q: What are the recommended use cases?
The model excels at code completion, code generation, debugging, and technical documentation. It's particularly well-suited for enterprise-level development environments where comprehensive code understanding and generation capabilities are required.
