vkerkez/GitVac-R-14B

GitVac-R-14B: A Reasoning Model for Code Fix Roleplay

vkerkez/GitVac-R-14B is a 14.8 billion parameter model designed to generate realistic, step-by-step roleplay scenarios for automated code fixes. Part of the GitVac series, this model is specifically tuned to produce detailed reasoning and actions, mimicking a developer's thought process when approaching a code problem. It leverages a 131072 token context window to process extensive code and problem descriptions.

Key Capabilities

• Detailed Reasoning: Generates comprehensive reason tags explaining the rationale behind each action, enabling a deeper understanding of the problem-solving process.
• Tool-Use Simulation: Employs a "Cursor Format" for tool calls, including read_file, str_replace_editor, ripgrep_search, list_directory, and terminal_access, to simulate realistic code interaction.
• Problem-Solving Roleplay: Unlike models that jump directly to solutions, GitVac-R-14B simulates exploration and discovery, such as listing directories before opening files, to create more natural and educational datasets.
• High Accuracy: Achieves a 92% success rate on unseen code patch problems, demonstrating strong performance in generating actionable code fixes.
• Dataset Generation: Optimized for creating high-quality synthetic datasets for training on-premise coding agents, focusing on the how as much as the what of code repair.

Good For

• Training Advanced Coding Agents: Ideal for distilling complex problem-solving methodologies into training data for other LLMs.
• Generating Detailed Code Fix Plans: Provides structured outputs with clear reasoning and sequential actions for automated software development tasks.
• Simulating Developer Workflow: Useful for scenarios requiring an agent to mimic a human developer's investigative and modification process within a codebase.
• High-Quality Code Patch Extraction: Excels at producing accurate and well-reasoned code patches, especially when detailed planning is crucial.