logan7000/cogrpo-n3-ring-qwen25-3b-x-llama32-3b-x-granite2b-math345-groupC-granite2b-end
This model is a 2 billion parameter language model, fine-tuned from ibm-granite/granite-3.3-2b-instruct. It was trained using the GRPO method, which is designed to enhance mathematical reasoning capabilities. This model is particularly suited for tasks requiring advanced mathematical problem-solving and logical deduction.
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Model Overview
This model is a fine-tuned version of the ibm-granite/granite-3.3-2b-instruct base model, featuring 2 billion parameters and a 32768 token context length. It has been specifically trained using the GRPO (Gradient Regularized Policy Optimization) method, as introduced in the research paper "DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models". This training approach aims to significantly improve the model's performance in mathematical reasoning tasks.
Key Capabilities
- Enhanced Mathematical Reasoning: Optimized through the GRPO method for complex mathematical problem-solving.
- Instruction Following: Built upon an instruct-tuned base model, it is designed to follow user instructions effectively.
- Fine-tuned Performance: Leverages the
TRL(Transformers Reinforcement Learning) framework for its fine-tuning process.
When to Use This Model
This model is ideal for applications requiring strong mathematical and logical reasoning. Consider using it for:
- Solving mathematical problems.
- Tasks that benefit from advanced logical deduction.
- Research and development in improving LLM mathematical capabilities.