README.md

---
library_name: peft
base_model: mistralai/Mistral-7B-v0.1
license: mit
tags:
- Mathematical Reasoning
datasets:
- akjindal53244/Arithmo-Data
language:
- en
---

**Arithmo2-Mistral-7B** model improves initially released [Arithmo-Mistral-7B](https://huggingface.co./akjindal53244/Arithmo-Mistral-7B) model on both GSM8K and MATH benchmarks. Specifically, there is **absolute** improvement of:
- +1.7% on GSM8K
- +3.0% on GSM8K PoT
- +1.9% on MATH

**This repo contains LoRA adapter weights**. If you are interested in final merged model, use [Arithmo2-Mistral-7B](https://huggingface.co./upaya07/Arithmo2-Mistral-7B) instead.


### Model Description

- **Project GitHub Page:** https://github.com/akjindal53244/Arithmo
- **Developed by:** [Ashvini Kumar Jindal](https://www.linkedin.com/in/ashvini-jindal-26653262/)
- **Funded by:** self-work
- **Model type:** fine-tuned using QLoRA on Single GPU
- **Language(s) (NLP):** English
- **Finetuned from model:** mistralai/Mistral-7B-v0.1

## Results

Arithmo2-Mistral-7B is improved version of [Arithmo-Mistral-7B](https://huggingface.co./akjindal53244/Arithmo-Mistral-7B) model and is competitive with full fine-tuned state-of-the-art 7B Mathematical Reasoning models. Refer to [Comparing Arithmo models with other SFT LLM models](https://github.com/akjindal53244/Arithmo/tree/master?tab=readme-ov-file#comparing-arithmo-models-with-other-sft-llm-models) section for more details.

<table>
    <thead>
        <tr>
            <th>Prompt Approach</th>
            <th>GSM8k</th>
            <th>MATH</th>
        </tr>
    </thead>
    <tbody>
        <tr>
            <td>Zero-Shot CoT</td>
            <td><b>76.4</b></td>
            <td><b>27.2</b></td>
        </tr>
        <tr>
            <td>Zero-Shot PoT</td>
            <td><b>74.2</b></td>
            <td>-</td>
        </tr>
    </tbody>
</table>

- **Zero-Shot CoT**: On providing a question as prompt, model generates reasoning steps to solve the question along with answer. We check if answer matches with ground-truth.
- **Zero-Shot PoT**: We prompt the model to generate a Python program for the given question. During inference, we execute the Python program generated by the model and check if the program output matches with ground-truth answer.


## Training procedure


The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: bfloat16

### Framework versions


- PEFT 0.6.0.dev0



## Installation

```
pip install transformers >=4.34.0
pip install accelerate
pip install sentencepiece
pip install protobuf

# If you are GPU poor like me
pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cpu

# If you have a GPU.
pip install --pre torch --index-url https://download.pytorch.org/whl/nightly/cu118
pip install scipy
pip install bitsandbytes
```


## How to query the model

```
# Set `run_model_on_gpu` to `False` if you are running on CPU. Model will generate reasoning steps with answer for your question. If you want to generate Python program, uncomment line-69 that adds a Python prompt.
# This script automatically does formatting for you, so you just need to type question (eg: `What is 2+2?`) without any prefix like `Question:`, etc.**

$ python query_model.py
```
**Note:** Above script automatically does formatting for you, so you just need to type question (eg: `What is 2+2?`) without any prefix like `Question:`, etc. Checkout [query_model.py](https://github.com/akjindal53244/Arithmo/blob/master/query_model.py) for more details. <br><br>

##### Sample Input:
```
Question: There are total 10 children. I have to give 1 apple to first child, 2 apples to second child, 3 apples to third child, and so on. How many apples do I need?
```
##### Model Output:
```
Answer: The total number of apples needed is the sum of the first 10 positive integers.
This can be calculated using the formula for the sum of an arithmetic series:
\[S = \frac{n}{2}(a_1 + a_n),\]
where $S$ is the sum, $n$ is the number of terms, $a_1$ is the first term, and $a_n$ is the last term.
In this case, $n = 10$, $a_1 = 1$, and $a_n = 10$.
Plugging these values into the formula, we get:
\[S = \frac{10}{2}(1 + 10) = 5(11) = \boxed{55}.\]
The answer is: 55
```

Arithmo2-Mistral-7B is trained with same format as [Arithmo-Mistral-7B](https://huggingface.co./akjindal53244/Arithmo-Mistral-7B):
#### CoT Format (generate reasoning steps with answer):
```
Question: <question>

Answer:
```

#### PoT Format (generate a python program):
```
Question: <question> <python_prompt>

Answer:
```
It will perform best if queried in this way with your own script.

## Comparing Arithmo models with other SFT LLM models
Results for all models except `Arithmo2-Mistral-7B` are taken from [MetaMath](https://github.com/meta-math/MetaMath/blob/main/README.MD) repository.

| Model               | GSM8k Pass@1 | MATH Pass@1 | Fine-tuning |
|---------------------|--------------|-------------|-------------|
| MPT-7B              | 6.8          | 3.0         |
| Falcon-7B           | 6.8          | 2.3         |
| LLaMA-1-7B          | 11.0         | 2.9         |
| LLaMA-2-7B          | 14.6         | 2.5         |
| MPT-30B             | 15.2         | 3.1         |
| LLaMA-1-13B         | 17.8         | 3.9         |
| GPT-Neo-2.7B        | 19.5         | --          |
| Falcon-40B          | 19.6         | 2.5         |
| Baichuan-chat-13B   | 23.9         | --          |
| Vicuna-v1.3-13B     | 27.6         | --          |
| LLaMA-2-13B         | 28.7         | 3.9         |
| InternLM-7B         | 31.2         | --          |
| ChatGLM-2-6B        | 32.4         | --          |
| GPT-J-6B            | 34.9         | --          |
| LLaMA-1-33B         | 35.6         | 3.9         |
| LLaMA-2-34B         | 42.2         | 6.24        |
| RFT-7B              | 50.3         | --          |
| LLaMA-1-65B         | 50.9         | 10.6        |
| Qwen-7B             | 51.6         | --          |
| WizardMath-7B       | 54.9         | 10.7        |
| LLaMA-2-70B         | 56.8         | 13.5        |
| WizardMath-13B      | 63.9         | 14.0        |
| MetaMath-7B         | 66.5         | 19.8        |
| MetaMath-13B        | 72.3         | 22.4        |
| Arithmo-Mistral-7B (PoT)  | 71.2 | --       | SFT: 4-bit QLoRA |
| Arithmo2-Mistral-7B (PoT)  | 74.2 | --       | SFT: 4-bit QLoRA |
| MetaMath-Mistral-7B  | 77.7 | 28.2       | SFT: Full fine-tuned |
| Arithmo-Mistral-7B| 74.7 | 25.3       | SFT: 4-bit QLoRA |
| 🔥 **Arithmo2-Mistral-7B**  | **76.4** | **27.2**       | **SFT: 4-bit QLoRA** |

If you are interested in reproducing the resullts, visit https://github.com/akjindal53244/Arithmo#reproducing-results section.

### Support My Work

Building LLMs takes time and resources; if you find my work interesting, your support would be epic!
<a href="https://www.buymeacoffee.com/a_little_learner" target="_blank"><img src="https://cdn.buymeacoffee.com/buttons/v2/default-yellow.png" alt="Buy Me A Coffee" style="height: 60px !important;width: 217px !important;" ></a>


### Citation
To cite Arithmo models:
```
@misc{jindal_2023_arithmo,
  author = {Jindal, Ashvini},
  title = {Arithmo-Mistral-7B: Mathematical Reasoning Model},
  howpublished = {Hugging Face},
  month = {October},
  year = {2023},
  url = {https://huggingface.co./akjindal53244/Arithmo-Mistral-7B}
}
```


<h2 id="References">References</h2>

```
@article{yu2023metamath,
  title={MetaMath: Bootstrap Your Own Mathematical Questions for Large Language Models},
  author={Yu, Longhui and Jiang, Weisen and Shi, Han and Yu, Jincheng and Liu, Zhengying and Zhang, Yu and Kwok, James T and Li, Zhenguo and Weller, Adrian and Liu, Weiyang},
  journal={arXiv preprint arXiv:2309.12284},
  year={2023}
}

@article{Yue2023mammoth,
  title={MAmmoTH: Building math generalist models through hybrid instruction tuning},
  author={Xiang Yue, Xingwei Qu, Ge Zhang, Yao Fu, Wenhao Huang, Huan Sun, Yu Su, and Wenhu Chen},
  journal={arXiv preprint arXiv:2309.05653},
  year={2023}
}

@article{mishra2022lila,
  title={Lila: A unified benchmark for mathematical reasoning},
  author={Swaroop Mishra, Matthew Finlayson, Pan Lu, Leonard Tang, Sean Welleck, Chitta Baral, Tanmay Rajpurohit, Oyvind Tafjord, Ashish Sabharwal, Peter Clark, and Ashwin Kalyan},
  journal={arXiv preprint arXiv:2210.17517},
  year={2022}
}

```