Matt300209/Ouro-1B-Instruct
Matt300209/Ouro-1B-Instruct is a 1.4 billion parameter instruction-tuned causal language model developed by ByteDance, based on the Ouro-1.4B architecture. This model is specifically optimized for advanced mathematical and scientific reasoning tasks, featuring a 32K context length and a unique recurrent step mechanism for explicit thinking processes. It aims to provide competitive reasoning performance against larger 4B models while maintaining a compact size.
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Ouro-1.4B-Thinking: A Reasoning-Specialized LLM
Ouro-1.4B-Thinking, developed by ByteDance, is a 1.4 billion parameter language model specifically fine-tuned for advanced reasoning tasks. It is a variant of the Ouro-1.4B base model, enhanced through supervised fine-tuning on high-quality reasoning datasets. The model is designed for research purposes and emphasizes an explicit thinking process.
Key Capabilities & Features
- Advanced Reasoning: Optimized for mathematical and scientific problem-solving.
- Compact Efficiency: Achieves reasoning performance competitive with 4 billion parameter models despite its smaller size.
- Cross-Step Consistency: Intermediate recurrent outputs are reliable indicators of final answers.
- Explicit Thinking Process: Generates detailed reasoning steps, making its problem-solving transparent.
- Configurable Recurrent Steps: Users can adjust the number of recurrent steps (
total_ut_steps) and an adaptive exit mechanism (early_exit_threshold) viaconfig.jsonto balance performance and computation.
Training Details
The model underwent supervised fine-tuning on approximately 8.3 million examples, including 3.5M mathematics, 3.2M code, 808K science, and 767K chat examples. It was trained for 2 epochs with a maximum sequence length of 32K tokens.
When to Use This Model
This model is particularly well-suited for applications requiring strong logical deduction and step-by-step problem-solving, especially in mathematical and scientific domains, where explicit reasoning paths are beneficial. Its compact size makes it efficient for deployment where computational resources are a consideration.