Undi95/ReasoningEngine
Undi95/ReasoningEngine is a 13 billion parameter language model built upon Stability AI's StableBeluga-13B, further enhanced with a 0.42 weight from jondurbin/airoboros-lmoe-13b-2.1's reasoning adapter. This model is specifically designed to improve reasoning capabilities, making it suitable for tasks requiring logical inference and problem-solving. With a context length of 4096 tokens, it aims to provide enhanced analytical performance.
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Undi95/ReasoningEngine: Enhanced Reasoning Model
Undi95/ReasoningEngine is a 13 billion parameter language model that integrates components from two established models to specialize in reasoning tasks. It is primarily based on Stability AI's StableBeluga-13B, a fine-tuned LLaMA 2 model known for its strong general capabilities.
Key Enhancements
- Reasoning Adapter Integration: The model incorporates a 0.42 weight from the reasoning adapter of jondurbin/airoboros-lmoe-13b-2.1. This specific integration is designed to boost the model's ability to handle complex logical problems and inferential tasks.
- Parameter Count: With 13 billion parameters, it offers a balance between performance and computational efficiency for specialized reasoning.
- Context Length: The model supports a context length of 4096 tokens, allowing it to process and reason over moderately long inputs.
Good For
- Logical Problem Solving: Ideal for applications requiring the model to deduce, infer, and solve problems based on provided information.
- Analytical Tasks: Suitable for scenarios where enhanced analytical capabilities are beneficial, such as data interpretation or complex query resolution.
- Specialized Reasoning Applications: Developers looking for a model with a focused improvement in reasoning over its base components.
Top 3 parameter combinations used by Featherless users for this model. Click a tab to see each config.