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## News [12/19/2023] π₯ We released **WizardMath-7B-V1.1** trained from Mistral-7B, the **SOTA 7B math LLM**, achieves **83.2 pass@1** on GSM8k, and **33.0 pass@1** on MATH. Use this [[**Demo**](http://47.103.63.15:50083/)] to chat with it. [12/19/2023] π₯ **WizardMath-7B-V1.1** outperforms **ChatGPT 3.5**, **Gemini Pro**, **Mixtral MOE**, and **Claude Instant** on GSM8K pass@1. [12/19/2023] π₯ **WizardMath-7B-V1.1** is comparable with **ChatGPT 3.5**, **Gemini Pro**, and surpasses **Mixtral MOE** on MATH pass@1. | Model | Checkpoint | Paper | GSM8k | MATH | Demo| | ----- |------| ---- |------|-------|-------| | **WizardMath-7B-V1.1** | π€ HF Link | π [WizardMath]| **83.2** | **33.0** |[[**Demo**](http://47.103.63.15:50083/)] | | WizardMath-70B-V1.0 | π€ HF Link | π [WizardMath]| **81.6** | **22.7** || | WizardMath-13B-V1.0 | π€ HF Link | π [WizardMath]| **63.9** | **14.0** || | WizardMath-7B-V1.0 | π€ HF Link | π [WizardMath]| **54.9** | **10.7** | | ## [12/19/2023] Comparing WizardMath-7B-V1.1 with other open source 7B size math LLMs. | Model | GSM8k Pass@1 | MATH Pass@1 | | ----- |------| ---- | | MPT-7B | 6.8 | 3.0 | |Llama 1-7B | 11.0 | 2.9 | |Llama 2-7B|12.3 |2.8 | |Yi-6b| 32.6 |5.8 | |Mistral-7B|37.8 |9.1 | |Qwen-7b|47.8 |9.3 | | RFT-7B | 50.3 | -- | | MAmmoTH-7B (COT) | 50.5 | 10.4 | | WizardMath-7B-V1.0 | 54.9 | 10.7 | |Abel-7B-001 |59.7 |13 | | MetaMath-7B | 66.5 | 19.8 | | Arithmo-Mistral-7B | 74.7 | 25.3 | |MetaMath-Mistral-7B|77.7 |28.2 | |Abel-7B-002 | 80.4 | 29.5 | | **WizardMath-7B-V1.1** | **83.2** | **33.0** | ## [12/19/2023] Comparing WizardMath-7B-V1.1 with large open source (30B~70B) LLMs. | Model | GSM8k Pass@1 | MATH Pass@1 | | ----- |------| ---- | | Llemma-34B | 51.5 | 25.0 | | Minerva-62B | 52.4 | 27.6 | | Llama 2-70B | 56.8 | 13.5 | | DeepSeek 67B | 63.4 | -- | | Gork 33B | 62.9 | 23.9 | | MAmmoTH-70B | 72.4 | 21.1 | | Yi-34B | 67.9 | 15.9 | | Mixtral 8x7B | 74.4 | 28.4 | | MetaMath-70B | 82.3 | 26.6 | | **WizardMath-7B-V1.1** | **83.2** | **33.0** | ## β Data Contamination Check: Before model training, we carefully and rigorously checked all the training data, and used multiple deduplication methods to verify and prevent data leakage on GSM8k and MATH test set. π₯ βNote for model system prompts usage: Please use **the same systems prompts strictly** with us, and we do not guarantee the accuracy of the **quantified versions**. **Default version:** ``` "Below is an instruction that describes a task. Write a response that appropriately completes the request.\n\n### Instruction:\n{instruction}\n\n### Response:" ``` **CoT Version:** οΌβFor the **simple** math questions, we do NOT recommend to use the CoT prompt.οΌ ``` "Below is an instruction that describes a task. Write a response that appropriately completes the request.\n\n### Instruction:\n{instruction}\n\n### Response: Let's think step by step." ``` ## Inference WizardMath Demo Script We provide the WizardMath inference demo code [here](https://github.com/nlpxucan/WizardLM/tree/main/demo). ## Citation Please cite the repo if you use the data, method or code in this repo. ``` @article{luo2023wizardmath, title={WizardMath: Empowering Mathematical Reasoning for Large Language Models via Reinforced Evol-Instruct}, author={Luo, Haipeng and Sun, Qingfeng and Xu, Can and Zhao, Pu and Lou, Jianguang and Tao, Chongyang and Geng, Xiubo and Lin, Qingwei and Chen, Shifeng and Zhang, Dongmei}, journal={arXiv preprint arXiv:2308.09583}, year={2023} } ```