OpenThinker2-32B

Maintained By
open-thoughts

OpenThinker2-32B

PropertyValue
Base ModelQwen2.5-32B-Instruct
Training DataOpenThoughts2-1M
LicenseApache 2.0
Training Infrastructure128 4xA100 nodes, 50 hours

What is OpenThinker2-32B?

OpenThinker2-32B represents a significant advancement in open-source language models, built upon the Qwen2.5-32B-Instruct architecture and fine-tuned on the comprehensive OpenThoughts2-1M dataset. This model demonstrates exceptional performance across various mathematical and reasoning benchmarks, including AIME24 (76.7%), AIME25 (58.7%), and MATH500 (90.8%).

Implementation Details

The model was trained using 512 GPUs across 128 nodes, with sophisticated hyperparameters including a learning rate of 8e-05 and a cosine scheduler with 0.1 warmup ratio. The training process utilized AdamW optimizer and ran for 5 epochs with a total batch size of 512.

  • Utilizes state-of-the-art training infrastructure with 128 4xA100 nodes
  • Implements advanced optimization techniques with AdamW optimizer
  • Leverages the OpenThoughts2-1M dataset with 26 different question generation methodologies

Core Capabilities

  • Exceptional performance in mathematical reasoning tasks
  • Strong results in code reasoning and problem-solving
  • Improved accuracy compared to predecessor models across multiple benchmarks
  • Versatile application in complex mathematical and logical reasoning scenarios

Frequently Asked Questions

Q: What makes this model unique?

OpenThinker2-32B stands out for its superior performance on mathematical and reasoning tasks, achieving the highest scores among open-data models. Its training on the OpenThoughts2-1M dataset, which incorporates diverse question generation methodologies, makes it particularly effective for complex problem-solving.

Q: What are the recommended use cases?

The model excels in mathematical reasoning, competitive mathematics problems (like AIME), and general problem-solving tasks. It's particularly suitable for educational applications, mathematical research, and scenarios requiring advanced logical reasoning capabilities.

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