cjiao/goldengoose-corr-v4-0.50-200
The cjiao/goldengoose-corr-v4-0.50-200 model is a 1.5 billion parameter language model, fine-tuned from Qwen/Qwen2.5-1.5B-Instruct, with a context length of 32768 tokens. It was trained using the GRPO method, which is designed to enhance mathematical reasoning capabilities. This model is particularly suited for tasks requiring improved logical and mathematical problem-solving, building upon the Qwen2.5 architecture.
Loading preview...
Overview
cjiao/goldengoose-corr-v4-0.50-200 is a 1.5 billion parameter language model, fine-tuned from the Qwen/Qwen2.5-1.5B-Instruct base model. It supports a substantial context length of 32768 tokens, making it suitable for processing longer inputs and generating more extensive responses. The model's training incorporated the GRPO (Gradient-based Reward Policy Optimization) method, as detailed in the research paper "DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models". This specific training approach aims to improve the model's performance in tasks that require advanced mathematical and logical reasoning.
Key Capabilities
- Enhanced Mathematical Reasoning: Benefits from GRPO training, which is designed to push the limits of mathematical reasoning in language models.
- Large Context Window: Utilizes a 32768-token context length, allowing for comprehensive understanding and generation based on extensive input.
- Qwen2.5 Architecture: Built upon the robust Qwen2.5-1.5B-Instruct foundation, inheriting its general language understanding and generation capabilities.
Good For
- Applications requiring improved mathematical problem-solving.
- Tasks where logical deduction and reasoning are critical.
- Scenarios benefiting from a large context window for detailed analysis or generation.