Instructions to use igmoiiz/math-tutor-qwen-lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use igmoiiz/math-tutor-qwen-lora with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-1.5B-Instruct") model = PeftModel.from_pretrained(base_model, "igmoiiz/math-tutor-qwen-lora") - Notebooks
- Google Colab
- Kaggle
Math Tutor - Qwen (LoRA)
Model Description
Math Tutor - Qwen is a QLoRA fine-tuned version of Qwen2.5-1.5B-Instruct trained on the GSM8K dataset. The objective is to improve multi-step mathematical reasoning for grade-school arithmetic word problems.
This repository contains only the LoRA adapter weights and must be loaded together with the original Qwen2.5-1.5B-Instruct base model.
Model Details
- Developed by: Moiz Baloch
- Model type: LoRA Adapter (PEFT)
- Base model: Qwen/Qwen2.5-1.5B-Instruct
- Language: English
- License: MIT
- Fine-tuning method: QLoRA (4-bit)
Intended Use
This model is intended for:
- Mathematical reasoning
- Step-by-step arithmetic solutions
- Educational tutoring
- Grade-school math assistance
- Learning demonstrations for LLM fine-tuning
Out-of-Scope Use
This model is not intended for:
- Medical advice
- Legal advice
- Financial advice
- High-stakes decision making
- General knowledge evaluation
Training Dataset
Dataset:
GSM8K (Grade School Math 8K)
- 7,473 training samples
- 1,319 evaluation samples
Dataset: https://huggingface.co/datasets/openai/gsm8k
Training Procedure
Fine-tuning Method
- QLoRA
- 4-bit Quantization
- PEFT
Hyperparameters
- Epochs: 2
- Learning Rate: 2e-4
- Batch Size: 2
- Gradient Accumulation: 4
- Optimizer: paged_adamw_8bit
Hardware
Training performed on:
- Kaggle Notebook
- NVIDIA Tesla T4 GPU (16 GB)
Evaluation
The model was evaluated on the GSM8K evaluation split during training.
Future work includes comparison against the base Qwen model on unseen mathematical reasoning tasks.
Limitations
The model has only been fine-tuned on GSM8K.
It may perform poorly on:
- Advanced mathematics
- Symbolic algebra
- Geometry proofs
- Scientific reasoning
- Non-English problems
Loading the Model
from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel
base_model = AutoModelForCausalLM.from_pretrained(
"Qwen/Qwen2.5-1.5B-Instruct"
)
tokenizer = AutoTokenizer.from_pretrained(
"Qwen/Qwen2.5-1.5B-Instruct"
)
model = PeftModel.from_pretrained(
base_model,
"igmoiiz/math-tutor-qwen-lora"
)
Example
Input
John has 12 apples. He gives 5 to his friend and buys 8 more. How many apples does he have now?
Output
John starts with 12 apples.
After giving away 5:
12 − 5 = 7
After buying 8 more:
7 + 8 = 15
Final Answer: 15 apples.
Citation
If you use this model, please cite the original GSM8K paper:
@article{cobbe2021gsm8k,
title={Training Verifiers to Solve Math Word Problems},
author={Cobbe, Karl and others},
journal={arXiv preprint arXiv:2110.14168},
year={2021}
}
Author
Moiz Baloch
Computer Science Undergraduate
Machine Learning & Deep Learning Engineer
GitHub: https://github.com/igmoiiz LinkedIn: https://linkedin.com/in/moizbaloch
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