KristianS7/Ouro-2.6B-Thinking
KristianS7/Ouro-2.6B-Thinking is a 2.6 billion parameter language model, a reasoning-specialized variant of the Ouro-2.6B base model, fine-tuned on high-quality reasoning data. It features a recurrent architecture with 4 steps and a 32K context length, designed for advanced mathematical and scientific reasoning tasks. This model is notable for its compact size, performing competitively with 4B models, and its ability to generate explicit, detailed reasoning steps, making it suitable for applications requiring transparent problem-solving.
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Ouro-2.6B-Thinking: A Reasoning-Specialized LLM
KristianS7/Ouro-2.6B-Thinking is a 2.6 billion parameter model specifically optimized for complex reasoning tasks. Derived from the Ouro-2.6B base model, it has undergone supervised fine-tuning on extensive, high-quality reasoning datasets, including mathematics, code, and science. This model is designed to generate explicit, detailed reasoning steps, making its problem-solving process transparent.
Key Capabilities
- Advanced Reasoning: Excels in mathematical and scientific problem-solving, generating detailed intermediate steps.
- Compact Efficiency: Achieves performance comparable to larger 4B parameter models despite its smaller 2.6B size.
- Recurrent Architecture: Utilizes 4 recurrent steps, configurable via
total_ut_steps, to enhance reasoning depth. - Adaptive Exit Mechanism: Supports early exit to optimize computation, particularly when used with Loop-SGLang.
- Cross-Step Consistency: Intermediate outputs from recurrent steps can reliably approximate final answers.
Good For
- Applications requiring transparent, step-by-step reasoning in mathematics and science.
- Scenarios where computational efficiency is crucial, leveraging its compact size and adaptive exit.
- Research into recurrent neural network architectures for enhanced reasoning capabilities.
- Developing systems that benefit from explicit thinking processes rather than just final answers.