bunnycore/QwQen-3B-LCoT
bunnycore/QwQen-3B-LCoT is a 3.1 billion parameter language model created by bunnycore, merged using the linear method with bunnycore/Qwen2.5-3B-RP-Mix as its base. This model integrates capabilities from prithivMLmods/QwQ-LCoT-3B-Instruct, aiming to combine their strengths. It is designed for general language tasks, with its performance evaluated on the Open LLM Leaderboard across various benchmarks.
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Model Overview
bunnycore/QwQen-3B-LCoT is a 3.1 billion parameter language model developed by bunnycore. It was created using the linear merge method via mergekit, building upon bunnycore/Qwen2.5-3B-RP-Mix as its base model. The merge also incorporated prithivMLmods/QwQ-LCoT-3B-Instruct, combining the characteristics of these two models.
Key Capabilities
- Merged Architecture: Leverages a linear merge of
bunnycore/Qwen2.5-3B-RP-MixandprithivMLmods/QwQ-LCoT-3B-Instructto synthesize their respective strengths. - Parameter Efficiency: At 3.1 billion parameters, it offers a relatively compact size for deployment while aiming for robust performance.
- Evaluation: Performance is tracked on the Open LLM Leaderboard, with detailed results available here. Key metrics include:
- Avg. Score: 22.11
- IFEval (0-Shot): 60.25
- BBH (3-Shot): 28.50
- MMLU-PRO (5-shot): 29.99
Intended Use Cases
This model is suitable for general language generation and understanding tasks where a balance between model size and performance is desired. Its merged origin suggests potential for diverse applications, particularly those benefiting from the combined features of its constituent models.