---
license: afl-3.0
---


## Model description
MathGLM-10B is finetuned from GLM-10B on a dataset with additional multi-step arithmetic operations and math problems described in text, achieves similar performance to GPT-4 on a 5,000-samples Chinese math problem
test set.


## How to use 
```python
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
tokenizer = AutoTokenizer.from_pretrained("BAAI/glm-10b-chinese", trust_remote_code=True)
model = AutoModelForSeq2SeqLM.from_pretrained("BAAI/glm-10b-chinese", trust_remote_code=True)
model = model.half().cuda()

inputs = tokenizer("凯旋门位于意大利米兰市古城堡旁。1807年为纪念[MASK]而建，门高25米，顶上矗立两武士青铜古兵车铸像。", return_tensors="pt")
inputs = tokenizer.build_inputs_for_generation(inputs, max_gen_length=512)
inputs = {key: value.cuda() for key, value in inputs.items()}
outputs = model.generate(**inputs, max_length=512, eos_token_id=tokenizer.eop_token_id)
print(tokenizer.decode(outputs[0].tolist()))
```

## Citation
Please cite our paper if you find this code useful for your research:
```
@article{yang2023gpt,
  title={GPT Can Solve Mathematical Problems Without a Calculator},
  author={Yang, Zhen and Ding, Ming and Lv, Qingsong and Jiang, Zhihuan and He, Zehai and Guo, Yuyi and Bai, Jinfeng and Tang, Jie},
  journal={arXiv preprint arXiv:2309.03241},
  year={2023}
}
```