StarCoder GPTeacher Code Instruct
| Property | Value |
|---|---|
| Model Size | 15.5B parameters |
| Architecture | GPT-2 with Multi-Query Attention |
| Context Window | 8192 tokens |
| License | BigCode OpenRAIL-M |
| Training Data | 80+ Programming Languages |
What is starcoder-gpteacher-code-instruct?
This model represents a significant advancement in code generation AI, built upon the robust StarCoder architecture and fine-tuned with the GPTeacher dataset. It's specifically designed to understand and respond to natural language instructions for programming tasks, combining the comprehensive coding knowledge of StarCoder with enhanced instruction-following capabilities.
Implementation Details
The model is built on a sophisticated architecture that includes Multi Query Attention and was trained using the Fill-in-the-Middle objective on 1 trillion tokens. The fine-tuning process involved 4.5k instruct-response pairs over 3 epochs, utilizing 8 Tesla A100 GPUs with FSDP optimization.
- Trained on 80+ programming languages from The Stack v1.2
- Uses bfloat16 precision for efficient computation
- Implements a context window of 8192 tokens
- Fine-tuned with a learning rate of 2e-5
Core Capabilities
- Code generation across multiple programming languages
- Natural language instruction understanding
- Technical problem-solving and explanation
- Code completion and enhancement
- Documentation generation
Frequently Asked Questions
Q: What makes this model unique?
This model uniquely combines StarCoder's extensive programming knowledge with instruction-following capabilities, making it particularly effective for direct coding tasks and technical assistance. The use of Multi Query Attention and Fill-in-the-Middle objective sets it apart from traditional code generation models.
Q: What are the recommended use cases?
The model excels at tasks such as writing functions based on natural language descriptions, explaining code functionality, debugging, and providing technical assistance. It's particularly useful for developers seeking AI assistance in coding tasks while maintaining contextual awareness of proper programming practices.
