Ilia2003Mah/qwen2.5-1.5b-gsm8k-train-step6500
Ilia2003Mah/qwen2.5-1.5b-gsm8k-train-step6500 is a 1.5 billion parameter language model based on the Qwen2.5 architecture, fine-tuned by Ilia2003Mah. With a context length of 32768 tokens, this model is specifically trained on the GSM8K dataset, indicating a specialization in mathematical reasoning and problem-solving tasks. Its primary use case is for applications requiring robust performance on arithmetic and logical challenges.
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Model Overview
Ilia2003Mah/qwen2.5-1.5b-gsm8k-train-step6500 is a 1.5 billion parameter language model built upon the Qwen2.5 architecture. This model has been fine-tuned by Ilia2003Mah with a focus on mathematical reasoning, leveraging the GSM8K dataset during its training process. It supports a substantial context length of 32768 tokens, allowing for processing longer problem descriptions and complex mathematical sequences.
Key Capabilities
- Mathematical Reasoning: Specialized training on the GSM8K dataset suggests strong capabilities in solving grade school level math problems.
- Large Context Window: Benefits from a 32768-token context length, suitable for detailed problem statements and multi-step reasoning.
- Qwen2.5 Architecture: Inherits the foundational strengths of the Qwen2.5 model family.
Good for
- Applications requiring solutions to arithmetic and logical problems.
- Educational tools focused on mathematics.
- Research into small-scale models for specialized reasoning tasks.