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Model: MetaStoneTec/MetaStone-L1-7B
Source: Original Platform

README.md

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+## 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}
+}
+```
+