axel-datos/qwen2.5-0.5b-instruct_gsm8k_full-finetuning
axel-datos/qwen2.5-0.5b-instruct_gsm8k_full-finetuning is a fine-tuned variant of the Qwen2.5-0.5B-Instruct model, specifically optimized for mathematical reasoning tasks using the GSM8K dataset. This model leverages a 0.5 billion parameter architecture, making it suitable for applications requiring efficient, specialized performance in arithmetic and problem-solving. Its primary use case is enhancing accuracy in quantitative reasoning within resource-constrained environments.
Loading preview...
Overview
axel-datos/qwen2.5-0.5b-instruct_gsm8k_full-finetuning is a specialized language model derived from the Qwen2.5-0.5B-Instruct base model. It has undergone full fine-tuning on a customized dataset, with a particular focus on the GSM8K dataset, which is designed for grade school math word problems. This targeted training aims to significantly improve the model's capabilities in mathematical reasoning and problem-solving.
Key Capabilities
- Enhanced Mathematical Reasoning: Specifically fine-tuned on the GSM8K dataset to improve performance on arithmetic and logical math problems.
- Instruction Following: Retains the instruction-following capabilities of its base Qwen2.5-0.5B-Instruct model.
- Efficient Architecture: Based on a 0.5 billion parameter model, offering a balance between performance and computational efficiency.
Good For
- Educational Tools: Developing applications that assist with mathematical homework or provide step-by-step solutions.
- Quantitative Analysis: Tasks requiring accurate numerical reasoning and problem-solving in a constrained environment.
- Research in Small Models: Exploring the limits of mathematical reasoning in smaller, more efficient language models.
Training Details
The model was trained with a learning rate of 2e-05, a batch size of 1, and utilized Native AMP for mixed-precision training. The training procedure involved 0.01 epochs, using an AdamW optimizer and a linear learning rate scheduler.