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--- |
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license: apache-2.0 |
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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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--- |
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# Qwen2-Math-1.5B-Instruct |
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> [!Warning] |
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> <div align="center"> |
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> <b> |
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> 🚨 Temporarily this model mainly supports English. We will release bilingual (English & Chinese) models soon! |
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> </b> |
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> </div> |
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## Introduction |
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Over the past year, we have dedicated significant effort to researching and enhancing the reasoning capabilities of large language models, with a particular focus on their ability to solve arithmetic and mathematical problems. Today, we are delighted to introduce a serise of math-specific large language models of our Qwen2 series, Qwen2-Math and Qwen2-Math-Instruct-1.5B/7B/72B. Qwen2-Math is a series of specialized math language models built upon the Qwen2 LLMs, which significantly outperforms the mathematical capabilities of open-source models and even closed-source models (e.g., GPT4o). We hope that Qwen2-Math can contribute to the scientific community for solving advanced mathematical problems that require complex, multi-step logical reasoning. |
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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-math/) and [GitHub repo](https://github.com/QwenLM/Qwen2-Math). |
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## Requirements |
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* `transformers>=4.40.0` for Qwen2-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 `transformers` integrated Qwen2 codes since `4.37.0`. |
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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-Math-1.5B-Instruct** is an instruction model for chatting; |
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> |
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> **Qwen2-Math-1.5B** 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-Math can be deployed and inferred in the same way as [Qwen2](https://github.com/QwenLM/Qwen2). 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 transformers import AutoModelForCausalLM, AutoTokenizer |
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model_name = "Qwen/Qwen2-Math-1.5B-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 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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``` |
