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jiaowoxiaoguojiuxing
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Model Details
Model Provider
jiaowoxiaoguojiuxing
Model Tree
Qwen/Qwen2.5-Math-7B
Input Modalities
Output Modalities
Supported Functionality
In August 2024, we released the first series of mathematical LLMs - Qwen2-Math - of our Qwen family. A month later, we have upgraded it and open-sourced Qwen2.5-Math series, including base models Qwen2.5-Math-1.5B/7B/72B, instruction-tuned models Qwen2.5-Math-1.5B/7B/72B-Instruct, and mathematical reward model Qwen2.5-Math-RM-72B.
Unlike Qwen2-Math series which only supports using Chain-of-Thught (CoT) to solve English math problems, Qwen2.5-Math series is expanded to support using both CoT and Tool-integrated Reasoning (TIR) to solve math problems in both Chinese and English. The Qwen2.5-Math series models have achieved significant performance improvements compared to the Qwen2-Math series models on the Chinese and English mathematics benchmarks with CoT.
While CoT plays a vital role in enhancing the reasoning capabilities of LLMs, it faces challenges in achieving computational accuracy and handling complex mathematical or algorithmic reasoning tasks, such as finding the roots of a quadratic equation or computing the eigenvalues of a matrix. TIR can further improve the model's proficiency in precise computation, symbolic manipulation, and algorithmic manipulation. Qwen2.5-Math-1.5B/7B/72B-Instruct achieve 79.7, 85.3, and 87.8 respectively on the MATH benchmark using TIR.
For more details, please refer to our blog post and GitHub repo.
transformers>=4.37.0 for Qwen2.5-Math models. The latest version is recommended.[!Warning]
For requirements on GPU memory and the respective throughput, see similar results of Qwen2 here.
[!Important]
Qwen2.5-Math-7B-Instruct is an instruction model for chatting;
Qwen2.5-Math-7B is a base model typically used for completion and few-shot inference, serving as a better starting point for fine-tuning.
Qwen2.5-Math can be deployed and infered in the same way as Qwen2.5. Here we show a code snippet to show you how to use the chat model with transformers:
python
from modelscope import AutoModelForCausalLM, AutoTokenizermodel_name = "qwen/Qwen2.5-Math-7B-Instruct"device = "cuda" # the device to load the model ontomodel = AutoModelForCausalLM.from_pretrained(model_name,torch_dtype="auto",device_map="auto")tokenizer = AutoTokenizer.from_pretrained(model_name)prompt = "Find the value of $x$ that satisfies the equation $4x+5 = 6x+7$."messages = [{"role": "system", "content": "You are Qwen, created by Alibaba Cloud. You are a helpful assistant."},{"role": "user", "content": prompt}]text = tokenizer.apply_chat_template(messages,tokenize=False,add_generation_prompt=True)model_inputs = tokenizer([text], return_tensors="pt").to(device)generated_ids = model.generate(**model_inputs,max_new_tokens=512)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]
We strongly advise users especially those in mainland China to use ModelScope. snapshot_download can help you solve issues concerning downloading checkpoints.
If you find our work helpful, feel free to give us a citation.
markdown
@article{yang2024qwen2,title={Qwen2 technical report},author={Yang, An and Yang, Baosong and Hui, Binyuan and Zheng, Bo and Yu, Bowen and Zhou, Chang and Li, Chengpeng and Li, Chengyuan and Liu, Dayiheng and Huang, Fei and others},journal={arXiv preprint arXiv:2407.10671},year={2024}}