nilgeoutim/RLCR-0.00005smCE-hotpot-seed41
The nilgeoutim/RLCR-0.00005smCE-hotpot-seed41 model is a 3.1 billion parameter language model, fine-tuned from Qwen/Qwen2.5-3B. It was trained using the TRL framework and the GRPO method, which is designed to enhance mathematical reasoning. This model is particularly optimized for tasks requiring advanced reasoning capabilities, leveraging techniques from mathematical reasoning research.
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Model Overview
nilgeoutim/RLCR-0.00005smCE-hotpot-seed41 is a 3.1 billion parameter language model, fine-tuned from the Qwen/Qwen2.5-3B base model. It leverages the TRL (Transformer Reinforcement Learning) framework for its training process.
Key Capabilities
- Enhanced Reasoning: This model was trained using the GRPO (Gradient-based Reward 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 improve the model's ability to handle complex reasoning tasks.
- Qwen2.5-3B Foundation: Built upon the robust Qwen2.5-3B architecture, providing a strong base for general language understanding and generation.
Training Details
The model's training procedure specifically incorporates GRPO, a technique known for its application in mathematical reasoning. The training utilized TRL version 0.16.0.dev0, Transformers 4.48.3, Pytorch 2.5.1+cu124, Datasets 4.0.0, and Tokenizers 0.21.1.
Good For
- Applications requiring advanced reasoning, particularly those that could benefit from methods inspired by mathematical reasoning research.
- Developers looking for a Qwen2.5-3B based model with specialized fine-tuning for complex problem-solving.