## Introduction
MetaStone-L1 is the lite reasoning model of the MetaStone series, which aims to enhance the performance in hard downstream tasks.

On core reasoning benchmarks including mathematics and code, MetaStone-L1-7B achieved SOTA results in the parallel-level models, and it also achieved the comparable results as the API models such as Claude-3.5-Sonnet-1022 and GPT4o-0513.
<img src="./introduction.png" alt="Logo" width="800">

This repo contains the MetaStone-L1-7B model, which is trained based on DeepSeek-R1-Distill-Qwen-7B by GRPO. For full details of this model please refer to our release blog.

## Requirements
We advise you to use the latest version of transformers(```transformers==4.48.3```).  For the best experience, please review the [Usage Guidelines](#usage-guidelines).

## Quickstart
Here give the example of how to use our model.
```
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "MetaStoneTec/MetaStone-L1-7B"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

messages = [
    {"role": "user", "content": "Complete the square for the following quadratic: $-x^2+7 x-11$\n\nPlease reason step by step, and put your final answer within \\boxed{}."}
]

text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)

generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=32768
)
generated_ids = [
    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]

response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
```

## Usage Guidelines
To achieve optimal performance, we recommend the following settings:

1. Enhace the thoughful output：

    a. Make sure the model starts with ```<think>\n``` to prevent generating empty think content. If you use ```apply_chat_template``` and set ```add_generation_prompt=True```, this is automatically implemented, but this may result in replies not having a <think> tag at the beginning, which is normal.

    b. Ensure the final input of the model is in the format of ```<｜User｜> [your prompt] <｜Assistant｜><think>```.

2. Use a temperature of 0.6, a top sampling probability of 0.95, a maximum generation length of 32k.

3. Standardize output format: We recommend using hints to standardize model outputs when benchmarking.

    a. Math questions: Add a statement "```Please reason step by step, and put your final answer within \\boxed{}.```" to the prompt.

    b. Code problems: Add "### Format: Read the inputs from stdin solve the problem and write the answer to stdout. Enclose your code within delimiters as follows.\n \```python\n# YOUR CODE HERE\n\```\n### Answer: (use the provided format with backticks)" to the prompt.

4. In particular, we use ```latex2sympy2``` and ```sympy``` to assist in judging complex Latex formats for the Math500 evaluation script. For all datasets, we generate 64 responses per query to estimate pass@1.

## Citation
If you find our work helpful, feel free to give us a cite.
```
@misc{MetaStoneL17B,
    title = {MetastoneL17B},
    url = {https://huggingface.co/MetaStoneTec/MetaStone-L1-7B},
    author = {MetaStone Team},
    month = {March},
    year = {2025}
}
```

```
@article{wang2024graph,
  title={A Graph-Based Synthetic Data Pipeline for Scaling High-Quality Reasoning Instructions},
  author={Wang, Jiankang and Xu, Jianjun and Wang, Xiaorui and Wang, Yuxin and Xing, Mengting and Fang, Shancheng and Chen, Zhineng and Xie, Hongtao and Zhang, Yongdong},
  journal={arXiv preprint arXiv:2412.08864},
  year={2024}
}
```