GetSoloTech/Qwen3-Code-Reasoning-4B
GetSoloTech/Qwen3-Code-Reasoning-4B is a 4 billion parameter causal language model, LoRA-finetuned by GetSoloTech from Qwen3-4B-Thinking-2507. Optimized for competitive programming and code reasoning, it leverages a 4096-token context length and is trained on high-quality solutions with at least 50% test case pass rates. This model excels at generating detailed, well-reasoned programming solutions for complex problems.
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Model Overview
GetSoloTech/Qwen3-Code-Reasoning-4B is a 4 billion parameter causal language model, specifically LoRA-finetuned from the Qwen3-4B-Thinking-2507 base model. Developed by GetSoloTech, this model is engineered to excel in competitive programming and complex code reasoning tasks. It utilizes a 4096-token context length, with configurability up to 262,144 tokens, and inherits advanced "thinking capabilities" from its base.
Key Capabilities
- Enhanced Code Reasoning: Trained on the
GetSoloTech/Code-Reasoningdataset, which comprises high-quality competitive programming problems from sources like TACO, APPS, CodeContests, and Codeforces. - High-Quality Solutions: Focuses on generating solutions that previously achieved at least a 50% test case pass rate, ensuring practical effectiveness.
- Structured Output: Optimized for producing well-reasoned and comprehensive programming solutions.
- Efficient Training: Utilizes LoRA (Low-Rank Adaptation) with Unsloth and QLoRA for efficient finetuning.
Good For
- Competitive Programming: Understanding problem constraints and generating accurate, efficient solutions.
- Code Generation: Producing high-quality code with improved accuracy.
- Reasoning Tasks: Providing enhanced step-by-step reasoning for intricate coding challenges.
- Solution Completeness: Handling edge cases and delivering more comprehensive solutions.