| ## 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} | |
| } | |
| ``` | |