hkr04/distill-1.5b-grpo-minmax
The hkr04/distill-1.5b-grpo-minmax model is a 1.5 billion parameter language model developed by hkr04, fine-tuned using Grouped Reinforcement Learning from Policy Optimization (GRPO). It was specifically trained on the DAPO-Math-17k dataset, indicating an optimization for mathematical reasoning and problem-solving tasks. With a context length of 32768 tokens, this model is designed for applications requiring robust mathematical capabilities and extended input sequences.
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Model Overview
The hkr04/distill-1.5b-grpo-minmax is a 1.5 billion parameter language model developed by hkr04. This model has been specifically fine-tuned using a technique called Grouped Reinforcement Learning from Policy Optimization (GRPO), an implementation provided by VeRL.
Key Training Details
The model's training focused on the DAPO-Math-17k dataset, suggesting a specialization in mathematical reasoning and problem-solving. The training process involved specific parameters:
- Batch Size: 32
- Group Size: 8
- Training Steps: 280
- Maximum Response Length: 8192 tokens
With a total context length of 32768 tokens, the model is capable of processing and generating extended sequences, which is beneficial for complex mathematical problems or detailed explanations.
Potential Use Cases
Given its training on a mathematical dataset and the GRPO fine-tuning approach, this model is likely well-suited for:
- Mathematical problem-solving: Generating solutions or explanations for math-related queries.
- Quantitative analysis: Assisting with tasks that involve numerical reasoning.
- Educational tools: Developing AI tutors or learning aids focused on mathematics.
- Data interpretation: Processing and understanding data presented in a mathematical context.