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Model: upaya07/Arithmo2-Mistral-7B
Source: Original Platform

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README.md

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+---
+license: mit
+language:
+- en
+datasets:
+- akjindal53244/Arithmo-Data
+tags:
+- Mathematical Reasoning
+---
+
+**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 final merged model**. If you are interested in LoRA adapter, use [LoRA Adapter](https://huggingface.co/upaya07/Arithmo2-Mistral-7B-adapter) 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.
+
+
+## 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 results, 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}
+}
+
+```

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+  "vocab_size": 32000
+}

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special_tokens_map.json

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tokenizer_config.json

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