115 lines
4.6 KiB
Markdown
115 lines
4.6 KiB
Markdown
---
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base_model: Qwen/Qwen2.5-Math-7B
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language:
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- en
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pipeline_tag: text-generation
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tags:
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- chat
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library_name: transformers
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license: apache-2.0
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license_link: https://huggingface.co/Qwen/Qwen2.5-Math-7B-Instruct/blob/main/LICENSE
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---
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# Qwen2.5-Math-7B-Instruct
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> [!Warning]
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> <div align="center">
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> <b>
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> 🚨 Qwen2.5-Math mainly supports solving English and Chinese math problems through CoT and TIR. We do not recommend using this series of models for other tasks.
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> </b>
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> </div>
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## Introduction
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In August 2024, we released the first series of mathematical LLMs - [Qwen2-Math](https://qwenlm.github.io/blog/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**.
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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.
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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.
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## Model Details
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For more details, please refer to our [blog post](https://qwenlm.github.io/blog/qwen2.5-math/) and [GitHub repo](https://github.com/QwenLM/Qwen2.5-Math).
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## Requirements
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* `transformers>=4.37.0` for Qwen2.5-Math models. The latest version is recommended.
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> [!Warning]
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> <div align="center">
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> <b>
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> 🚨 This is a must because <code>transformers</code> integrated Qwen2 codes since <code>4.37.0</code>.
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> </b>
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> </div>
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For requirements on GPU memory and the respective throughput, see similar results of Qwen2 [here](https://qwen.readthedocs.io/en/latest/benchmark/speed_benchmark.html).
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## Quick Start
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> [!Important]
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>
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> **Qwen2.5-Math-7B-Instruct** is an instruction model for chatting;
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>
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> **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.
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>
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### 🤗 Hugging Face Transformers
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Qwen2.5-Math can be deployed and infered in the same way as [Qwen2.5](https://github.com/QwenLM/Qwen2.5). Here we show a code snippet to show you how to use the chat model with `transformers`:
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```python
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from modelscope import AutoModelForCausalLM, AutoTokenizer
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model_name = "qwen/Qwen2.5-Math-7B-Instruct"
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device = "cuda" # the device to load the model onto
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model = AutoModelForCausalLM.from_pretrained(
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model_name,
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torch_dtype="auto",
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device_map="auto"
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)
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tokenizer = AutoTokenizer.from_pretrained(model_name)
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prompt = "Find the value of $x$ that satisfies the equation $4x+5 = 6x+7$."
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messages = [
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{"role": "system", "content": "You are Qwen, created by Alibaba Cloud. You are a helpful assistant."},
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{"role": "user", "content": prompt}
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]
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text = tokenizer.apply_chat_template(
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messages,
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tokenize=False,
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add_generation_prompt=True
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)
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model_inputs = tokenizer([text], return_tensors="pt").to(device)
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generated_ids = model.generate(
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**model_inputs,
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max_new_tokens=512
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)
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generated_ids = [
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output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
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]
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response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
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```
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### 🤖 ModelScope
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We strongly advise users especially those in mainland China to use ModelScope. `snapshot_download` can help you solve issues concerning downloading checkpoints.
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## Citation
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If you find our work helpful, feel free to give us a citation.
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```
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@article{yang2024qwen2,
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title={Qwen2 technical report},
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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},
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journal={arXiv preprint arXiv:2407.10671},
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year={2024}
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}
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``` |