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Model: akjindal53244/Arithmo-Mistral-7B Source: Original Platform
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README.md
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---
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license: apache-2.0
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language:
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- en
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tags:
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- Mathematical Reasoning
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datasets:
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- akjindal53244/Arithmo-Data
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---
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## [January 2024] New Model Release: Arithmo2-Mistral-7B
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**Arithmo2-Mistral-7B** model improves initially released Arithmo-Mistral-7B model on both GSM8K and MATH benchmarks. Specifically, there is **absolute** improvement of:
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- +1.7% on GSM8K
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- +3.0% on GSM8K PoT
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- +1.9% on MATH
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<b>Note</b>: <span style="color:red"><b>It is recommended to use Arithmo2-Mistral-7B model</b></span>. Here is the [merged model](https://huggingface.co/upaya07/Arithmo2-Mistral-7B) and corresponding [LoRA Adapter](https://huggingface.co/upaya07/Arithmo2-Mistral-7B-adapter).
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# Model Card for Model ID
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[](CODE_LICENSE)
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[](LICENSE)
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[](https://www.python.org/downloads/release/python-390/)
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**P.S.:** Please reach out to [Ashvini Jindal](https://www.linkedin.com/in/ashvini-jindal-26653262/) if you would be interested in supporting compute need. We are looking for small-scale support so we'd appreciate any kind of help! :)
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## Model Details
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Arithmo-Mistral-7B is trained to reason and answer mathematical problems and is also capable of writing a Python program that upon execution prints answer to the question. We used [Mistral-7B](https://huggingface.co/mistralai/Mistral-7B-v0.1) as a base model and used **QLoRA to fine-tune it on a single RTX 4090 GPU**.
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### Model Description
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- **Project GitHub Page:** https://github.com/akjindal53244/Arithmo-Mistral-7B
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- **Developed by:** [Ashvini Kumar Jindal](https://www.linkedin.com/in/ashvini-jindal-26653262/), [Ankur Parikh](https://www.linkedin.com/in/ankurnlpexpert/)
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- **Funded by:** self-work
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- **Model type:** fine-tuned
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- **Language(s) (NLP):** English
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- **Finetuned from model:** mistralai/Mistral-7B-v0.1
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## Results
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Arithmo-Mistral-7B outperforms existing 7B and 13B state-of-the-art Mathematical Reasoning models. Refer to [Comparing Arithmo-Mistral-7B with other LLM models](https://github.com/akjindal53244/Arithmo-Mistral-7B/tree/master#comparing-arithmo-mistral-7b-with-other-llm-models) section for more details.
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<table>
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<thead>
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<tr>
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<th>Prompt Approach</th>
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<th>GSM8k</th>
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<th>MATH</th>
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</tr>
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</thead>
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<tbody>
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<tr>
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<td>Zero-Shot CoT</td>
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<td><b>74.7</b></td>
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<td><b>25.3</b></td>
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</tr>
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<tr>
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<td>Zero-Shot PoT</td>
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<td><b>71.2</b></td>
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<td>-</td>
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</tr>
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</tbody>
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</table>
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- **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.
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- **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.
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## Installation
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```
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pip install transformers >=4.34.0
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pip install accelerate
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pip install sentencepiece
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pip install protobuf
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# If you are GPU poor like me
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pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cpu
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# If you have a GPU.
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pip install --pre torch --index-url https://download.pytorch.org/whl/nightly/cu118
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pip install scipy
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pip install bitsandbytes
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```
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## How to query the model
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```
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# 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.
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# 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.**
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$ python query_model.py
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```
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**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-Mistral-7B/blob/master/query_model.py) for more details. <br><br>
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##### Sample Input:
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```
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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?
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```
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##### Model Output:
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```
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Answer: The total number of apples needed is the sum of the first 10 positive integers.
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This can be calculated using the formula for the sum of an arithmetic series:
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\[S = \frac{n}{2}(a_1 + a_n),\]
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where $S$ is the sum, $n$ is the number of terms, $a_1$ is the first term, and $a_n$ is the last term.
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In this case, $n = 10$, $a_1 = 1$, and $a_n = 10$.
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Plugging these values into the formula, we get:
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\[S = \frac{10}{2}(1 + 10) = 5(11) = \boxed{55}.\]
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The answer is: 55
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```
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Arithmo-Mistral-7B is trained with the following format:
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#### CoT Format (generate reasoning steps with answer):
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```
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Question: <question>
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Answer:
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```
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#### PoT Format (generate a python program):
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```
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Question: <question> <python_prompt>
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Answer:
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```
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It will perform best if queried in this way with your own script.
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## Comparing Arithmo-Mistral-7B with other LLM models.
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Results for all models except `Arithmo-Mistral-7B` are taken from [MetaMath](https://github.com/meta-math/MetaMath/blob/main/README.MD) repository.
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| Model | GSM8k Pass@1 | MATH Pass@1 |
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|---------------------|--------------|-------------|
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| MPT-7B | 6.8 | 3.0 |
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| Falcon-7B | 6.8 | 2.3 |
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| LLaMA-1-7B | 11.0 | 2.9 |
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| LLaMA-2-7B | 14.6 | 2.5 |
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| MPT-30B | 15.2 | 3.1 |
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| LLaMA-1-13B | 17.8 | 3.9 |
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| GPT-Neo-2.7B | 19.5 | -- |
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| Falcon-40B | 19.6 | 2.5 |
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| Baichuan-chat-13B | 23.9 | -- |
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| Vicuna-v1.3-13B | 27.6 | -- |
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| LLaMA-2-13B | 28.7 | 3.9 |
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| InternLM-7B | 31.2 | -- |
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| ChatGLM-2-6B | 32.4 | -- |
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| GPT-J-6B | 34.9 | -- |
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| LLaMA-1-33B | 35.6 | 3.9 |
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| LLaMA-2-34B | 42.2 | 6.24 |
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| RFT-7B | 50.3 | -- |
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| LLaMA-1-65B | 50.9 | 10.6 |
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| Qwen-7B | 51.6 | -- |
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| WizardMath-7B | 54.9 | 10.7 |
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| LLaMA-2-70B | 56.8 | 13.5 |
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| WizardMath-13B | 63.9 | 14.0 |
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| MetaMath-7B | 66.5 | 19.8 |
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| MetaMath-13B | 72.3 | 22.4 |
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| 🔥 **Arithmo-Mistral-7B Zero-Shot PoT** | **71.2** | -- |
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| 🔥 **Arithmo-Mistral-7B Zero-Shot CoT** | **74.7** | **25.3** |
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| WizardMath-70B | **81.6** | 22.7 |
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| MetaMath-70B | **82.3** | **26.6** |
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If you are interested in reproducing the resullts, visit https://github.com/akjindal53244/Arithmo-Mistral-7B#reproducing-results section.
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### Support My Work
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Building LLMs takes time and resources; if you find my work interesting, your support would be epic!
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<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>
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### Citation
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To cite Arithmo models:
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```
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@misc{jindal_2023_arithmo,
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author = {Jindal, Ashvini},
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title = {Arithmo-Mistral-7B: Mathematical Reasoning Model},
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howpublished = {Hugging Face},
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month = {October},
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year = {2023},
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url = {https://huggingface.co/akjindal53244/Arithmo-Mistral-7B}
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}
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```
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<h2 id="References">References</h2>
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```
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@article{yu2023metamath,
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title={MetaMath: Bootstrap Your Own Mathematical Questions for Large Language Models},
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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},
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journal={arXiv preprint arXiv:2309.12284},
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year={2023}
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}
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@article{Yue2023mammoth,
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title={MAmmoTH: Building math generalist models through hybrid instruction tuning},
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author={Xiang Yue, Xingwei Qu, Ge Zhang, Yao Fu, Wenhao Huang, Huan Sun, Yu Su, and Wenhu Chen},
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journal={arXiv preprint arXiv:2309.05653},
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year={2023}
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}
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@article{mishra2022lila,
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title={Lila: A unified benchmark for mathematical reasoning},
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author={Swaroop Mishra, Matthew Finlayson, Pan Lu, Leonard Tang, Sean Welleck, Chitta Baral, Tanmay Rajpurohit, Oyvind Tafjord, Ashish Sabharwal, Peter Clark, and Ashwin Kalyan},
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journal={arXiv preprint arXiv:2210.17517},
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year={2022}
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}
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```
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"vocab_size": 32000
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||||
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||||
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||||
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|
||||
6
special_tokens_map.json
Normal file
6
special_tokens_map.json
Normal file
@@ -0,0 +1,6 @@
|
||||
{
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
}
|
||||
BIN
tokenizer.model
(Stored with Git LFS)
Normal file
BIN
tokenizer.model
(Stored with Git LFS)
Normal file
Binary file not shown.
45
tokenizer_config.json
Normal file
45
tokenizer_config.json
Normal file
@@ -0,0 +1,45 @@
|
||||
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|
||||
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|
||||
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|
||||
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||||
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||||
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||||
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||||
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||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
"tokenizer_class": "LlamaTokenizer",
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
"use_fast": true
|
||||
}
|
||||
Reference in New Issue
Block a user