cjiao/goldengoose-divsweepv2_lowdiv_goose_n128_random_n25
The cjiao/goldengoose-divsweepv2_lowdiv_goose_n128_random_n25 model is a 1.5 billion parameter language model fine-tuned from Qwen/Qwen2.5-1.5B-Instruct. Developed by cjiao, it was trained using the GRPO method, which is designed to enhance mathematical reasoning capabilities. This model is optimized for tasks requiring improved logical and mathematical problem-solving, building upon its Qwen2.5 base.
Loading preview...
Model Overview
The cjiao/goldengoose-divsweepv2_lowdiv_goose_n128_random_n25 is a 1.5 billion parameter language model that has been fine-tuned from the Qwen/Qwen2.5-1.5B-Instruct base model. It was developed by cjiao and trained using the TRL framework.
Key Training Details
A significant aspect of this model's development is its training procedure, which utilized GRPO (Gradient-based Reward Policy Optimization). GRPO is a method introduced in the research paper "DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models." This suggests an optimization focus on enhancing the model's capabilities in areas related to mathematical reasoning and problem-solving.
Intended Use Cases
Given its fine-tuning with the GRPO method, this model is likely to perform well in applications that require:
- Mathematical reasoning: Tasks involving numerical problems, logical deductions, or mathematical explanations.
- Instruction following: Building upon its
Qwen2.5-Instructbase, it should maintain strong instruction-following capabilities.
Developers can integrate this model using the Hugging Face transformers library, as demonstrated in the quick start example provided in its model card.