roskosmos19/Rhea-4B-Coding
Rhea-4B-Coding by Roskosmos19 is a 4 billion parameter model based on the Qwen3 architecture, optimized for code reasoning, debugging, and multi-pass problem solving. It specializes in detailed 3-pass reasoning for software development, algorithm design, and code comprehension tasks, utilizing explicit reasoning traces. Fine-tuned on high-quality programming data, this model excels at iterative code refinement and agentic tool integration.
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Model Overview
Rhea-4B-Coding, developed by Roskosmos19, is a 4 billion parameter model built on the Qwen3 architecture. It is an optimized version of Aquiles-ai/Athenea-4B-Coding, specifically designed for advanced code reasoning, debugging, agentic tools, and multi-pass problem solving. The model is trained on high-quality programming data, including explicit reasoning traces, to facilitate a detailed 3-pass reasoning process for software development and algorithm design.
Key Capabilities
- Multi-Pass Processing: Employs a unique 3-step reasoning process using
<think>,<review>, and<final>tags for initial implementation, self-review, and final optimization. - Agentic Tools: Designed to integrate with AI agents for enhanced problem-solving.
- Step-by-step Code Reasoning: Generates detailed reasoning within
thinkingblocks. - Self-review: Capable of detecting bugs and optimizing code iteratively.
- Specialization: Excels in algorithmic and debugging tasks, improving logical consistency.
- Uncensored Output: Provides full expressive freedom for research and experimentation.
Training and Usage
The model was fine-tuned using the Aquiles-ai/Athenea-Coding-100k dataset, which contains diverse programming challenges and structured reasoning chains across multiple languages. It is compatible with open inference frameworks like Transformers and vLLM, with recommended multi-pass inference for optimal results. The model configuration emphasizes full multi-pass output capacity and faster inference for long outputs.