Kxck/Self_Correction_v1
Kxck/Self_Correction_v1 is a 7.6 billion parameter Qwen2.5-7B-Instruct model fine-tuned for self-correction in mathematical and coding tasks. It leverages verified correction examples where training loss is computed only on the corrected response, using failed attempts and objective verifier feedback as context. This model is optimized for improving accuracy and reliability in complex reasoning problems, particularly in math and code generation. It is designed to enhance performance by learning from its own errors and external feedback.
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Kxck/Self_Correction_v1: Enhanced Reasoning through Self-Correction
Kxck/Self_Correction_v1 is a 7.6 billion parameter language model built upon the Qwen2.5-7B-Instruct architecture. Its core innovation lies in its fine-tuning methodology, which specifically targets self-correction capabilities, particularly in mathematical and coding domains.
Key Capabilities & Training
- Self-Correction Mechanism: The model is trained using a unique approach where it learns from its own "failed attempts" and "objective verifier feedback." This feedback serves as context, and the training loss is exclusively computed on the verified corrected response, rather than the initial incorrect output.
- Improved Accuracy: By focusing on correcting errors, the model aims to significantly enhance its accuracy and reliability when tackling complex reasoning tasks in math and code.
- Contextual Learning: The training process leverages both the initial incorrect attempt and the verifier's feedback as rich context, allowing the model to understand why an answer was wrong and how to fix it.
- Direct Loading: This repository provides merged BF16 weights, enabling direct loading and use with vLLM, facilitating efficient deployment.
Ideal Use Cases
- Mathematical Problem Solving: Excels in tasks requiring precise calculations and logical deduction, learning to correct errors in multi-step math problems.
- Code Generation & Debugging: Highly suitable for generating accurate code and identifying/correcting logical flaws in programming tasks.
- Automated Reasoning: Applications where robust and verifiable outputs are critical, benefiting from the model's self-correction capabilities.