AdarshSingh7647/Eklav-8B-Math
Eklav-8B-Math by AdarshSingh7647 is an 8 billion parameter language model based on Qwen3-8B, specifically fine-tuned for mathematical reasoning tasks. It utilizes the Eklav training method, which involves hint-conditioned supervised fine-tuning, allowing the model to learn to continue partial reasoning traces. This approach aims to improve the model's ability to generate independent reasoning steps for math problems, making it suitable for applications requiring robust mathematical problem-solving capabilities.
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Eklav-8B-Math: Enhanced Mathematical Reasoning
Eklav-8B-Math is an 8 billion parameter model developed by AdarshSingh7647, built upon the Qwen3-8B base architecture. Its core innovation lies in the Eklav training method, which focuses on teaching the model to complete reasoning steps from partial teacher traces, rather than simply imitating full reasoning chains. This "hint-conditioned SFT" approach encourages the model to develop its own reasoning continuation abilities.
Key Capabilities
- Specialized Math Reasoning: The model is specifically optimized for mathematical problem-solving, aiming to generate accurate reasoning steps.
- Eklav Training Advantage: Compared to standard full-trace CoT SFT, the Eklav method shows a +0.6% average pass@1 improvement across 6 math benchmarks (excluding MMLU).
- Performance on Math Benchmarks: Demonstrates strong performance on benchmarks like MATH-500 (90.8% pass@1) and Omni-MATH (37.7% pass@1), indicating its proficiency in complex mathematical tasks.
Good For
- Mathematical Problem Solving: Ideal for applications requiring the generation of step-by-step solutions to math problems.
- Educational Tools: Can be integrated into systems that help users understand and solve mathematical challenges by providing reasoning hints.
- Research in Reasoning: Useful for exploring alternative training methodologies for improving LLM reasoning capabilities beyond direct imitation.