cjiao/goldengoose-gumbel_tau0.50-25grp
The cjiao/goldengoose-gumbel_tau0.50-25grp is a 1.5 billion parameter causal language model, fine-tuned from Qwen/Qwen2.5-1.5B-Instruct with a 32K context length. 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 the base Qwen2.5 architecture.
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Model Overview
The cjiao/goldengoose-gumbel_tau0.50-25grp is a 1.5 billion parameter language model derived from the Qwen/Qwen2.5-1.5B-Instruct base model. It features a substantial context length of 32,768 tokens, making it suitable for processing longer inputs.
Key Differentiator: GRPO Training
This model's primary distinction lies in its training methodology. It was fine-tuned using the GRPO (Gumbel-softmax Reinforcement Learning with Policy Optimization) method, as introduced in the research paper "DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models." This technique is specifically designed to improve a model's capabilities in mathematical reasoning and problem-solving.
Training Details
The fine-tuning process utilized the TRL (Transformer Reinforcement Learning) library. The training leveraged specific versions of key frameworks:
- TRL: 0.19.1
- Transformers: 4.57.6
- Pytorch: 2.5.1
- Datasets: 4.8.4
- Tokenizers: 0.22.2
Use Cases
Given its GRPO-enhanced training, this model is particularly well-suited for applications requiring:
- Mathematical problem-solving: Tasks that involve numerical reasoning, equations, and logical deduction.
- Instruction following: Leveraging its
Qwen2.5-Instructbase for general instruction-tuned capabilities. - Long-context understanding: Benefiting from its 32K context window for complex, multi-step reasoning problems.