This repository contains a fine-tuned version of the Qwen-2 0.5B model specifically optimized for mathematical instruction understanding and reasoning. It builds upon the Numina dataset, which provides a rich source of mathematical problems and solutions designed to enhance reasoning capabilities even in smaller language models.

Motivation

My primary motivation is the hypothesis that high-quality datasets focused on mathematical reasoning can significantly improve the performance of smaller models on tasks that require logical deduction and problem-solving. Uploading benchmarks is the next step in evaluating this claim.

Model Details

Base Model: Qwen-2 0.5B
Fine-tuning Dataset: Numina COT
Key Improvements: Enhanced ability to parse mathematical instructions, solve problems, and provide step-by-step explanations.

Usage

You can easily load and use this model with the Hugging Face Transformers library:

python

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("xsanskarx/qwen2-0.5b_numina_math-instruct")
model = AutoModelForCausalLM.from_pretrained("xsanskarx/qwen2-0.5b_numina_math-instruct")