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--- |
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base_model: unsloth/gemma-2-9b-bnb-4bit |
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library_name: peft |
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license: apache-2.0 |
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datasets: |
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- microsoft/orca-math-word-problems-200k |
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- MathQA |
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metrics: |
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- accuracy |
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pipeline_tag: question-answering |
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tags: |
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- math |
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- gemma |
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- 'LoRA ' |
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--- |
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# Model Card for Gemmath-2-9b |
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This model is based on the Gemma-2-9b architecture and has been fine-tuned using two math problem datasets to improve its accuracy in solving mathematical tasks. |
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## Datasets |
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1. **[Orca-Math](https://huggingface.co/datasets/microsoft/orca-math-word-problems-200k)**: |
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A dataset containing approximately 200K grade school math word problems, with answers generated using Azure GPT-4 Turbo. |
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Designed to help models solve elementary-level math problems. |
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2. **[MathQA](https://math-qa.github.io/)**: |
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An annotated dataset of math word problems derived from the AQuA-RAT dataset using a novel representation language. |
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The dataset includes questions, multiple-choice options, rationales, and correct answers. |
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## Training Details |
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The training process included: |
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- Optimizer: AdamW (8-bit) |
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- Learning Rate: 2e-4 |
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- Epochs: 1 epoch for Orca-Math, 3 epochs for MathQA |
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- Batch Size: 16 |
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- Compute Resources: The model was fine-tuned using a single GPU (A100 80GB) for 14 hours. |
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- Fine-tuning Method: LoRA was used for efficient training and parameter reduction. |
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- Framework: Fine-tuning was conducted using Unsloth, enabling faster training and better memory efficiency. |
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## Evaluation |
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The model was evaluated using the **MathQA test dataset(2985 examples)** with **accuracy** as the primary metric. The following table compares its performance to other models: |
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| Model | Accuracy (%) | |
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|-----------------------|---------------| |
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| Gemma-2-9b (base) | 24.02 | |
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| Mistral-7B-Instruct | 22.61 | |
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| Llama-3.1-8b-Instruct | 27.37 | |
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| Llama-3.2-3b-Instruct | 23.48 | |
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| Qwen2-Math-7B-Instruct| 33.13 | |
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| Qwen2.5-7B-Instruct | 38.69 | |
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| **Gemmath-2-9b** | **48.91** | |
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## How to Get Started with the Model |
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```python |
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from transformers import AutoTokenizer, AutoModelForCausalLM |
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local_model_path = "Dasool/gemmath-2-9b" |
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tokenizer = AutoTokenizer.from_pretrained(local_model_path) |
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model = AutoModelForCausalLM.from_pretrained(local_model_path) |
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alpaca_prompt = """Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request. |
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### Instruction: |
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Please select the correct answer for the following math problem from the options provided. |
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### Input: |
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Problem: Calculate the square root of 144. |
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Options: |
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a) 10 |
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b) 11 |
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c) 12 |
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d) 13 |
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### Response: |
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""" |
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inputs = tokenizer(alpaca_prompt, return_tensors="pt") |
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outputs = model.generate( |
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inputs["input_ids"], |
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max_new_tokens=100, |
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num_beams=5, |
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early_stopping=True, |
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temperature=0.7, |
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no_repeat_ngram_size=2 |
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) |
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answer = tokenizer.decode(outputs[0], skip_special_tokens=True) |
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print(f"Answer: {answer}") |
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``` |
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## Limitations |
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The evaluation is based solely on accuracy for a 5-option multiple-choice task. This provides a high-level performance metric but does not fully capture the model's reasoning ability or performance on more complex, open-ended math problems. Deeper analysis is required to explore the model's problem-solving skills. |
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## Model Card Contact |
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If you have any questions or feedback, feel free to contact: |
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- Email: dasolcoi@yonsei.ac.kr |
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- GitHub: [Repository](https://github.com/Dasol-Choi/gemmath-2-9b) |
