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--- |
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license: afl-3.0 |
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--- |
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## Model description |
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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 |
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test set. |
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## How to use |
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```python |
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from transformers import AutoTokenizer, AutoModelForSeq2SeqLM |
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tokenizer = AutoTokenizer.from_pretrained("BAAI/glm-10b-chinese", trust_remote_code=True) |
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model = AutoModelForSeq2SeqLM.from_pretrained("BAAI/glm-10b-chinese", trust_remote_code=True) |
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model = model.half().cuda() |
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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") |
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inputs = tokenizer.build_inputs_for_generation(inputs, max_gen_length=512) |
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inputs = {key: value.cuda() for key, value in inputs.items()} |
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outputs = model.generate(**inputs, max_length=512, eos_token_id=tokenizer.eop_token_id) |
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print(tokenizer.decode(outputs[0].tolist())) |
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``` |
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## Citation |
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Please cite our paper if you find this code useful for your research: |
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``` |
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@article{yang2023gpt, |
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title={GPT Can Solve Mathematical Problems Without a Calculator}, |
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author={Yang, Zhen and Ding, Ming and Lv, Qingsong and Jiang, Zhihuan and He, Zehai and Guo, Yuyi and Bai, Jinfeng and Tang, Jie}, |
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journal={arXiv preprint arXiv:2309.03241}, |
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year={2023} |
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} |
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``` |
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