From f1e4e77fe8052efd93b12c2fa9ae32d40ca363ab Mon Sep 17 00:00:00 2001 From: ModelHub XC Date: Sat, 9 May 2026 11:50:53 +0800 Subject: [PATCH] =?UTF-8?q?=E5=88=9D=E5=A7=8B=E5=8C=96=E9=A1=B9=E7=9B=AE?= =?UTF-8?q?=EF=BC=8C=E7=94=B1ModelHub=20XC=E7=A4=BE=E5=8C=BA=E6=8F=90?= =?UTF-8?q?=E4=BE=9B=E6=A8=A1=E5=9E=8B?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Model: akjindal53244/Arithmo-Mistral-7B Source: Original Platform --- .gitattributes | 35 ++++ README.md | 215 ++++++++++++++++++++++ config.json | 25 +++ generation_config.json | 6 + pytorch_model-00001-of-00002.bin | 3 + pytorch_model-00002-of-00002.bin | 3 + pytorch_model.bin.index.json | 298 +++++++++++++++++++++++++++++++ special_tokens_map.json | 6 + tokenizer.model | 3 + tokenizer_config.json | 45 +++++ 10 files changed, 639 insertions(+) create mode 100644 .gitattributes create mode 100644 README.md create mode 100644 config.json create mode 100644 generation_config.json create mode 100644 pytorch_model-00001-of-00002.bin create mode 100644 pytorch_model-00002-of-00002.bin create mode 100644 pytorch_model.bin.index.json create mode 100644 special_tokens_map.json create mode 100644 tokenizer.model create mode 100644 tokenizer_config.json diff --git a/.gitattributes b/.gitattributes new file mode 100644 index 0000000..a6344aa --- /dev/null +++ b/.gitattributes @@ -0,0 +1,35 @@ +*.7z filter=lfs diff=lfs merge=lfs -text +*.arrow filter=lfs diff=lfs merge=lfs -text +*.bin filter=lfs diff=lfs merge=lfs -text +*.bz2 filter=lfs diff=lfs merge=lfs -text +*.ckpt filter=lfs diff=lfs merge=lfs -text +*.ftz filter=lfs diff=lfs merge=lfs -text +*.gz filter=lfs diff=lfs merge=lfs -text +*.h5 filter=lfs diff=lfs merge=lfs -text +*.joblib filter=lfs diff=lfs merge=lfs -text +*.lfs.* filter=lfs diff=lfs merge=lfs -text +*.mlmodel filter=lfs diff=lfs merge=lfs -text +*.model filter=lfs diff=lfs merge=lfs -text +*.msgpack filter=lfs diff=lfs merge=lfs -text +*.npy filter=lfs diff=lfs merge=lfs -text +*.npz filter=lfs diff=lfs merge=lfs -text +*.onnx filter=lfs diff=lfs merge=lfs -text +*.ot filter=lfs diff=lfs merge=lfs -text +*.parquet filter=lfs diff=lfs merge=lfs -text +*.pb filter=lfs diff=lfs merge=lfs -text +*.pickle filter=lfs diff=lfs merge=lfs -text +*.pkl filter=lfs diff=lfs merge=lfs -text +*.pt filter=lfs diff=lfs merge=lfs -text +*.pth filter=lfs diff=lfs merge=lfs -text +*.rar filter=lfs diff=lfs merge=lfs -text +*.safetensors filter=lfs diff=lfs merge=lfs -text +saved_model/**/* filter=lfs diff=lfs merge=lfs -text +*.tar.* filter=lfs diff=lfs merge=lfs -text +*.tar filter=lfs diff=lfs merge=lfs -text +*.tflite filter=lfs diff=lfs merge=lfs -text +*.tgz filter=lfs diff=lfs merge=lfs -text +*.wasm filter=lfs diff=lfs merge=lfs -text +*.xz filter=lfs diff=lfs merge=lfs -text +*.zip filter=lfs diff=lfs merge=lfs -text +*.zst filter=lfs diff=lfs merge=lfs -text +*tfevents* filter=lfs diff=lfs merge=lfs -text diff --git a/README.md b/README.md new file mode 100644 index 0000000..7b20c3e --- /dev/null +++ b/README.md @@ -0,0 +1,215 @@ +--- +license: apache-2.0 +language: +- en +tags: +- Mathematical Reasoning +datasets: +- akjindal53244/Arithmo-Data +--- + +## [January 2024] New Model Release: Arithmo2-Mistral-7B + +**Arithmo2-Mistral-7B** model improves initially released 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 + +Note: It is recommended to use Arithmo2-Mistral-7B model. Here is the [merged model](https://huggingface.co/upaya07/Arithmo2-Mistral-7B) and corresponding [LoRA Adapter](https://huggingface.co/upaya07/Arithmo2-Mistral-7B-adapter). + + +# Model Card for Model ID + +[![Code License](https://img.shields.io/badge/Code%20License-Apache_2.0-green.svg)](CODE_LICENSE) +[![Model Weight License](https://img.shields.io/badge/Model%20Weights%20License-Apache_2.0-green.svg)](LICENSE) +[![Python 3.9+](https://img.shields.io/badge/python-3.9+-blue.svg)](https://www.python.org/downloads/release/python-390/) + +**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! :) + +## Model Details + +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**. + +### Model Description + +- **Project GitHub Page:** https://github.com/akjindal53244/Arithmo-Mistral-7B +- **Developed by:** [Ashvini Kumar Jindal](https://www.linkedin.com/in/ashvini-jindal-26653262/), [Ankur Parikh](https://www.linkedin.com/in/ankurnlpexpert/) +- **Funded by:** self-work +- **Model type:** fine-tuned +- **Language(s) (NLP):** English +- **Finetuned from model:** mistralai/Mistral-7B-v0.1 + +## Results + +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. + + + + + + + + + + + + + + + + + + + + + +
Prompt ApproachGSM8kMATH
Zero-Shot CoT74.725.3
Zero-Shot PoT71.2-
+ +- **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-Mistral-7B/blob/master/query_model.py) for more details.

+ +##### 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 +``` + +Arithmo-Mistral-7B is trained with the following format: +#### CoT Format (generate reasoning steps with answer): +``` +Question: + +Answer: +``` + +#### PoT Format (generate a python program): +``` +Question: + +Answer: +``` +It will perform best if queried in this way with your own script. + +## Comparing Arithmo-Mistral-7B with other LLM models. +Results for all models except `Arithmo-Mistral-7B` are taken from [MetaMath](https://github.com/meta-math/MetaMath/blob/main/README.MD) repository. + +| Model | GSM8k Pass@1 | MATH Pass@1 | +|---------------------|--------------|-------------| +| 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 Zero-Shot PoT** | **71.2** | -- | +| 🔥 **Arithmo-Mistral-7B Zero-Shot CoT** | **74.7** | **25.3** | +| WizardMath-70B | **81.6** | 22.7 | +| MetaMath-70B | **82.3** | **26.6** | + + +If you are interested in reproducing the resullts, visit https://github.com/akjindal53244/Arithmo-Mistral-7B#reproducing-results section. + + +### Support My Work + +Building LLMs takes time and resources; if you find my work interesting, your support would be epic! +Buy Me A Coffee + + +### 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} +} +``` + + + +

References

+ +``` +@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} +} + +``` diff --git a/config.json 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