DeepSeek Coder 6.7B Instruct GGUF
| Property | Value |
|---|---|
| Parameter Count | 6.7B |
| Model Type | Code Generation / Instruction |
| License | DeepSeek License |
| Training Data | 2T tokens (87% code, 13% language) |
| Context Length | 16K tokens |
What is deepseek-coder-6.7B-instruct-GGUF?
DeepSeek Coder 6.7B Instruct is a specialized coding assistant model converted to the efficient GGUF format. The model was trained from scratch on an extensive dataset of 2 trillion tokens, with a primary focus on programming content (87%) and supplementary language data in English and Chinese (13%). This GGUF version offers various quantization options, making it accessible for different hardware configurations while maintaining high performance.
Implementation Details
The model utilizes advanced quantization techniques in GGUF format, offering multiple versions from 2-bit to 8-bit quantization. The recommended Q4_K_M version provides an optimal balance between model size (4.08GB) and performance. The model features a 16K token context window and includes special fill-in-the-blank training for enhanced code completion capabilities.
- Multiple quantization options ranging from 2.83GB to 7.16GB file sizes
- GPU acceleration support with layer offloading capabilities
- Specialized prompt template for coding-focused interactions
- Project-level code completion and infilling support
Core Capabilities
- Advanced code generation and completion across multiple programming languages
- Context-aware code suggestions and problem-solving
- Project-level code understanding with 16K context window
- Fill-in-the-blank code completion functionality
- State-of-the-art performance on coding benchmarks
Frequently Asked Questions
Q: What makes this model unique?
This model stands out due to its specialized training on a massive code-focused dataset, extensive context window, and efficient GGUF format that enables deployment across various hardware configurations. The multiple quantization options make it highly accessible while maintaining performance.
Q: What are the recommended use cases?
The model excels at code generation, completion, and problem-solving tasks. It's particularly well-suited for software development, code review, and programming education. The model specifically focuses on computer science-related queries while declining to answer non-technical questions.
