
Supervised Finetuning the phi1.5 on MetaMathQA datasets. The results are as follows:

| 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         | --          |
| **Phi1.5-1.3B**     | **54.3**         | **15.5**        |
| WizardMath-7B       | 54.9         | 10.7        |
| LLaMA-2-70B         | 56.8         | 13.5        |
| WizardMath-13B      | 63.9         | 14.0        |
| MAmmoTH-7B (COT)    | 50.5         | 10.4        |
| MAmmoTH-7B (POT+COT)| 53.6         | 31.5        |
| Arithmo-Mistral-7B  | 74.7         | 25.3        |
| MetaMath-7B         | 66.5         | 19.8        |
| MetaMath-13B        | 72.3         | 22.4        |
| **MetaMath-Mistral-7B** | **77.7**     | **28.2**        |


It achieves remarkable performance with only 1.3B parameters !!!

You can evaluate the results by [metamath](https://huggingface.co/meta-math/MetaMath-Mistral-7B) evaluation code.
{"language": ["en"], "license": "apache-2.0", "datasets": ["meta-math/MetaMathQA"]}
transformers safetensors phi text-generation custom_code en dataset:meta-math/MetaMathQA license:apache-2.0 autotrain_compatible endpoints_compatible region:us