WarriorCoder Reproduce
| Property | Value |
|---|---|
| Model Size | 6.7B parameters |
| Author | HuggingMicah |
| Original Paper | WarriorCoder Paper |
| Training Data | Dataset Repository |
What is warriorcoder_reproduce?
WarriorCoder Reproduce is an open-source implementation of Microsoft's WarriorCoder, achieving state-of-the-art performance in code generation tasks. This reproduction demonstrates the effectiveness of learning from expert battles to enhance code LLMs, surpassing the original paper's results with a 41.7% overall score across various programming libraries.
Implementation Details
The model is built on a 6.7B parameter architecture and utilizes supervised fine-tuning (SFT) to learn from expert code examples. It has been extensively tested across multiple programming frameworks including Matplotlib, NumPy, Pandas, PyTorch, SciPy, Sklearn, and TensorFlow.
- Achieves 56.1% accuracy on Matplotlib tasks
- Demonstrates 45.0% performance on NumPy challenges
- Shows significant improvements in TensorFlow tasks with 48.9% accuracy
- Outperforms other models like CodeLlama-Python and WizardCoder-CL
Core Capabilities
- Superior performance on HumanEval (79.9%) and HumanEval+ (75.4%)
- Strong results on MBPP (75.8%) and MBPP+ (64.5%)
- Comprehensive coverage across major Python libraries
- Enhanced code generation and understanding capabilities
Frequently Asked Questions
Q: What makes this model unique?
This model stands out by successfully reproducing and even surpassing the original WarriorCoder's performance using entirely open-source components, demonstrating better results across multiple benchmarks compared to the published paper.
Q: What are the recommended use cases?
The model excels in working with popular Python libraries and frameworks, making it ideal for code generation, automation tasks, and technical problem-solving across data science, machine learning, and general Python development.
