DeepThought-8B showcases that smaller models can achieve sophisticated reasoning capabilities by detailing its problem-solving process in a transparent and controllable manner.
Ruliad emphasizes users’ ability to customize the model's reasoning patterns without needing to retrain, thereby enhancing user engagement and adaptability in various tasks.
The model breaks down problems into specific steps, such as problem understanding, data gathering, analysis, and conclusion drawing, which enables users to validate the reasoning effectively.
While DeepThought-8B performs strongly on reasoning tasks compared to larger models, it still finds itself outmatched by established models like GPT-4o and Claude-3.5-Sonnet.
Collection
[
|
...
]