moazeldegwy/Qwen2.5-1.5B-Reasoning-Hybrid-SFT
The moazeldegwy/Qwen2.5-1.5B-Reasoning-Hybrid-SFT is a 1.5 billion parameter instruction-tuned causal language model, based on the Qwen2.5 architecture, with a context length of 32768 tokens. This model is fine-tuned for reasoning tasks, aiming to provide enhanced logical capabilities within a compact size. It is designed for applications requiring efficient processing and improved reasoning performance.
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Overview
This model, moazeldegwy/Qwen2.5-1.5B-Reasoning-Hybrid-SFT, is a 1.5 billion parameter language model built upon the Qwen2.5 architecture. It features a substantial context window of 32768 tokens, allowing it to process lengthy inputs and maintain context over extended interactions. The model has undergone Supervised Fine-Tuning (SFT) with a focus on hybrid reasoning capabilities.
Key Capabilities
- Reasoning-focused Fine-tuning: The model is specifically fine-tuned to enhance its reasoning abilities, making it suitable for tasks that require logical deduction and problem-solving.
- Large Context Window: With a 32768-token context length, it can handle complex queries and retain information over long conversations or documents.
- Compact Size: At 1.5 billion parameters, it offers a balance between performance and computational efficiency, making it accessible for various deployment scenarios.
Good for
- Applications requiring efficient reasoning capabilities.
- Scenarios where a balance between model size and performance is crucial.
- Tasks benefiting from a large context window for understanding complex information.