OpenCoder-8B-Instruct
| Property | Value |
|---|---|
| Parameter Count | 7.77B |
| Context Length | 8K tokens |
| License | INF License |
| Paper | View Paper |
| Languages | English, Chinese |
What is OpenCoder-8B-Instruct?
OpenCoder-8B-Instruct is a state-of-the-art code language model designed for both code generation and understanding. It's part of the OpenCoder family, trained on an impressive 2.5 trillion tokens comprising 90% raw code and 10% code-related web data. The model represents a significant advancement in open-source code AI, offering commercial-grade performance with full transparency.
Implementation Details
The model utilizes a transformer-based architecture with BF16 precision, optimized for efficient inference. It builds upon the OpenCoder-8B-Base model and has undergone extensive supervised fine-tuning with over 4.5M high-quality examples.
- Comprehensive training on both code and code-related web content
- Supports sequence lengths up to 8K tokens
- Developed with rigorous experimental analysis and ablation studies
- Includes complete data processing pipeline and training protocols
Core Capabilities
- Strong performance on HumanEval(+) with 83.5% (78.7%) pass rate
- Excellent MBPP(+) benchmark results: 79.1% (69.0%)
- Robust performance on BigCodeBench (40.3%) and BigCodeBench-Hard (16.9%)
- Bilingual support for English and Chinese programming tasks
- Commercial-ready with permissive licensing
Frequently Asked Questions
Q: What makes this model unique?
OpenCoder-8B-Instruct stands out for its complete transparency, providing not just the model weights but also the entire training pipeline, high-quality synthetic data, and comprehensive experimental analysis. It's one of the few models that delivers both commercial-grade performance and full open-source access.
Q: What are the recommended use cases?
The model excels in code generation, code completion, and programming assistance tasks. It's particularly well-suited for both English and Chinese programming environments, making it versatile for international development teams. The 8K token context length allows it to handle complex programming tasks and understand larger code contexts.
