ByteDance/Ouro-2.6B-Thinking
ByteDance/Ouro-2.6B-Thinking is a 2.6 billion parameter language model, a reasoning-specialized variant of the Ouro-2.6B base model, with a 32K context length. It is enhanced through supervised fine-tuning on high-quality reasoning data, specifically optimized for mathematical and scientific reasoning tasks. This compact model is designed to generate detailed reasoning steps and offers competitive performance with larger 4B models.
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Ouro-2.6B-Thinking: A Reasoning-Specialized LLM
Ouro-2.6B-Thinking, developed by ByteDance, is a 2.6 billion parameter language model built upon the Ouro-2.6B base architecture. It stands out due to its specialized supervised fine-tuning on extensive, high-quality reasoning datasets, including mathematics, code, and science. This optimization makes it particularly adept at complex problem-solving.
Key Capabilities
- Advanced Reasoning: Excels in mathematical and scientific reasoning tasks, generating explicit, detailed thinking processes.
- Compact Efficiency: Achieves performance comparable to 4 billion parameter models despite its smaller size.
- Cross-Step Consistency: Intermediate recurrent outputs are reliable proxies for final answers, enhancing transparency and debuggability.
- Configurable Recurrent Steps: Users can adjust
total_ut_stepsto balance performance and computational cost, though adaptive exit is not supported in vLLM. - Extensive Training: Pre-trained on 7.7T tokens and fine-tuned on ~8.3M examples across various reasoning domains.
Ideal Use Cases
- Mathematical Problem Solving: Suited for tasks requiring step-by-step mathematical derivations.
- Scientific Inquiry: Can assist in generating logical reasoning for scientific questions.
- Code Reasoning: Benefits from significant code-related fine-tuning data, making it useful for understanding and explaining code logic.
- Research and Development: Primarily intended for research purposes to explore and advance reasoning capabilities in compact LLMs.