Vilyam888/Broken_Code_Generation.1.0
Vilyam888/Broken_Code_Generation.1.0 is a 3.1 billion parameter instruction-tuned model, fine-tuned from Qwen/Qwen2.5-Coder-3B-Instruct, specifically designed for generating ML bugfix programming tasks. It excels at producing structured JSON output containing task details, tests, requirements, and broken code based on user-defined tags and difficulty levels. This model is optimized for creating synthetic data for educational purposes, automated task generation, and integration with code analysis pipelines.
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Overview
Vilyam888/Broken_Code_Generation.1.0 is a specialized language model, fine-tuned from Qwen/Qwen2.5-Coder-3B-Instruct using QLoRA, to generate structured ML bugfix programming tasks. Unlike general-purpose chat models, its core function is to produce a single, complete task in JSON format, including a title, context, tests, requirements, constraints, and a broken code snippet, based on exactly three user-provided tags and a difficulty level (easy, medium, or hard).
Key Capabilities
- Structured Task Generation: Outputs programming tasks in a consistent JSON format, ideal for automated processing.
- ML Bugfix Focus: Specifically trained to create scenarios where users need to identify and fix errors in machine learning code.
- Customizable Input: Accepts three topic tags and a difficulty level to tailor task generation.
- Integration Ready: Designed to work seamlessly with other tools, such as code analysis models like
Vilyam888/Code_analyze.1.0, for creating dynamic educational pipelines.
Good For
- Generating New ML Bugfix Problems: Quickly create a large volume of unique programming challenges.
- Educational Content Creation: Develop structured examples for students learning ML and debugging.
- Synthetic Data Generation: Produce data for training and testing other models or systems.
- Automated Task Systems: Integrate into platforms that require on-demand, structured programming tasks.
Limitations
While highly specialized, the model may occasionally produce incomplete JSON, and the quality of generation can vary with prompts and parameters. Outputs might sometimes be stylistically similar, and manual review is recommended for critical applications.