--- 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.
Prompt Approach GSM8k MATH
Zero-Shot CoT 76.4 27.2
Zero-Shot PoT 74.2 -
- **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. ## 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.

##### 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: Answer: ``` #### PoT Format (generate a python program): ``` Question: 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! 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} } ```