---
library_name: peft
base_model: google/gemma-2b
license: mit
tags:
- Mathematical Reasoning
language:
- en
datasets:
- adityasihag/math_QAaugP
---
**This repo contains LoRA adapter weights**. 


### Model Description

- **Project GitHub Page:** https://github.com/adityasihag1996/math_QA.git
- **Developed by:** [Aditya Sihag](https://www.linkedin.com/in/aditya-sihag-ab29681a9/)
- **Model type:** fine-tuned using QLoRA on 1x RTX 4090
- **Finetuned from model:** google/gemma-2b
  
## Results
<table>
    <thead>
        <tr>
            <th>Prompt Approach</th>
            <th>GSM8k</th>
            <th>MATH</th>
        </tr>
    </thead>
    <tbody>
        <tr>
            <td>Zero-Shot CoT</td>
            <td><b>43.66</b></td>
            <td><b>-</b></td>
        </tr>
    </tbody>
</table>

## Training procedure
The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- load_in_8bit: False
- load_in_4bit: True
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: float16

`LoraConfig` params:
- r: 128
- lora_alpha: lora_r * 2
- lora_dropout: 0.05
- bias: "none"
- task_type: "CAUSAL_LM"
- target_modules: ["q_proj", "k_proj", "v_proj", "o_proj", "gate_proj", "up_proj", "down_proj"]
The hyperparameters for the LoRA fine-tuning are listed below:
- epochs: 3
- learning_rate: 5e-5
- batch_size: 256
- max_grad_norm: 1.0
- weight_decay: 0.001
- lr_scheduler_type: "cosine"
- warmup_ratio: 0.03
  
## Dataset
math_QA dataset is prepared as combination of [MetaMathQA](https://huggingface.co/datasets/meta-math/MetaMathQA) and [MathInstruct](https://huggingface.co/datasets/TIGER-Lab/MathInstruct), and some internal data.
Refer [math_QAaugP](https://huggingface.co/datasets/adityasihag/math_QAaugP)

## Model Usage
```
import torch
from transformers import (
    AutoModelForCausalLM,
    AutoTokenizer
)
from peft import PeftModel
model_path = "google/gemma-2b"
model = AutoModelForCausalLM.from_pretrained(
    model_path,
    torch_dtype = torch.float16,
    device_map = {"": 0},
)
# Load LoRA and merge
model = PeftModel.from_pretrained(model, "adityasihag/math_QA-gemma-2B-QLoRA-adapter")
model = model.merge_and_unload()
tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
tokenizer.pad_token = tokenizer.eos_token
question = """Gretchen has 110 coins. There are 30 more gold coins than silver coins. How many gold coins does Gretchen have?"""
sample_input = f"""Question: {question} \n Answer: """
sample_input_tokenised = tokenizer(sample_input, return_tensors = "pt").to("cuda")
generated_ids = model.generate(
                    **sample_input_tokenised,
                    max_new_tokens = 512,
                    temperature = 0.3
                )
output = tokenizer.decode(generated_ids[0], skip_special_tokens = True)
print(output)
```

##### Sample Input:
```
Question: Gretchen has 110 coins. There are 30 more gold coins than silver coins. How many gold coins does Gretchen have? \n Answer: 
```

##### Model Output:
```
Let's assume the number of silver coins is x.
Since there are 30 more gold coins than silver coins, the number of gold coins is x + 30.
The total number of coins is x + (x + 30) = 110.
Combining like terms, we get 2x + 30 = 110.
Subtracting 30 from both sides, we get 2x = 80.
Dividing both sides by 2, we get x = 40.
So, Gretchen has 40 silver coins and 40 + 30 = 70 gold coins.
The answer is: 70
```

#### Prompt Template:
```
Question: <question>
Answer: 
```

## Comparing math_QA models with other SFT LLM models
| Model               | GSM8k Pass@1 | MATH Pass@1 |
|---------------------|--------------|-------------|
| LLaMA-2-7B          | 14.6         | 2.5         |
| gemma-2b         |  17.7        |         |
| LLaMA-2-13B         | 28.7         | 3.9         |
| LLaMA-2-34B         | 42.2         | 6.24        |
| **math_QA-gemma-2B**  | **43.66** |        |
| gemma-7b         |  46.4        |         |
| WizardMath-7B       | 54.9         | 10.7        |
| Mistral-7B         | 35.4         |         |
| WizardMath-13B      | 63.9         | 14.0        |
| MetaMath-7B         | 66.5         | 19.8        |
| MetaMath-13B        | 72.3         | 22.4        |
| **math_QA-Mistral-7B**  | **75.81** |        |
| Arithmo2-Mistral-7B  | 76.4 | 27.2       |
| MetaMath-Mistral-7B  | 77.7 | 28.2       |
| DeepSeekMath-Instruct-7B  | 82.9 | 46.8       |
| GPT4  | 92.0 | 52.9       |