DeepSeek Coder 7B Instruct v1.5
| Property | Value |
|---|---|
| Parameter Count | 6.91B |
| Model Type | Instruction-tuned Code Generation |
| License | DeepSeek License (Commercial use supported) |
| Tensor Type | BF16 |
| Context Window | 4K tokens |
What is deepseek-coder-7b-instruct-v1.5?
DeepSeek Coder 7B Instruct v1.5 is an advanced language model specifically designed for code generation and understanding. Built upon the DeepSeek-LLM 7B foundation, this model has undergone extensive pre-training on 2T tokens with a 4K token context window, followed by fine-tuning on 2B tokens of instruction data. This makes it particularly adept at handling programming-related tasks and generating high-quality code.
Implementation Details
The model leverages the Transformer architecture and implements next token prediction objectives during training. It's available in BF16 format and can be easily integrated using the Hugging Face Transformers library. The model supports chat-based interactions through a template system and can generate contextually relevant code responses.
- Extensive pre-training on 2T tokens
- Fine-tuning on 2B instruction tokens
- 4K token context window for handling longer sequences
- Built-in chat template support
Core Capabilities
- Code generation across multiple programming languages
- Understanding and responding to programming-related queries
- Context-aware code completion
- Support for interactive coding assistance
- Commercial usage capabilities
Frequently Asked Questions
Q: What makes this model unique?
The model's extensive pre-training on code-specific data, combined with its large context window and instruction-tuning, makes it particularly effective for programming tasks. The ability to handle 4K token contexts allows it to understand and generate longer code sequences effectively.
Q: What are the recommended use cases?
The model is ideal for code generation, programming assistance, code completion, and technical documentation tasks. It can be integrated into IDEs, used for automated code review, or implemented in educational programming tools.
