# xsanskarx/qwen2-0.5b_numina_math-instruct

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:

```
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")
```

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