KristianS7/Ouro-1.4B-Thinking
KristianS7/Ouro-1.4B-Thinking is a 1.4 billion parameter language model, a reasoning-specialized variant of the Ouro-1.4B base model, fine-tuned on high-quality reasoning data. It features a 32K context length and is optimized for mathematical and scientific reasoning tasks, explicitly generating detailed thinking steps. This compact model offers advanced reasoning capabilities competitive with larger 4B models, making it suitable for applications requiring robust problem-solving and logical deduction.
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Ouro-1.4B-Thinking: A Reasoning-Specialized Language Model
KristianS7/Ouro-1.4B-Thinking is a 1.4 billion parameter model, a specialized variant of the Ouro-1.4B base, enhanced through supervised fine-tuning on extensive high-quality reasoning datasets. This model is designed to excel in complex problem-solving, particularly in mathematical and scientific domains, by explicitly generating detailed reasoning steps.
Key Capabilities
- Advanced Reasoning: Optimized for mathematical and scientific reasoning tasks, providing robust problem-solving.
- Compact Efficiency: Achieves performance competitive with 4B parameter models despite its smaller 1.4B size.
- Explicit Thinking Process: Trained to generate detailed, step-by-step reasoning, making its thought process transparent.
- Cross-Step Consistency: Intermediate recurrent outputs are reliable proxies for final answers, enhancing interpretability.
- Configurable Recurrent Steps: Allows adjustment of
total_ut_stepsandearly_exit_thresholdfor performance-computation trade-offs.
Good For
- Research in Reasoning: Ideal for exploring and developing advanced reasoning capabilities in LLMs.
- Mathematical Problem Solving: Excels at tasks requiring logical deduction and step-by-step mathematical solutions.
- Scientific Inquiry: Suitable for applications in scientific domains where explicit reasoning is crucial.
- Resource-Constrained Environments: Its compact size (1.4B parameters) makes it efficient while maintaining strong reasoning performance.
- Interpretable AI: Useful for scenarios where understanding the model's "thought process" is as important as the final answer.