Qwen2-Math-7B-Instruct
[!Warning]
Introduction
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.
Model Details
For more details, please refer to our blog post and GitHub repo.
Requirements
transformers>=4.40.0for Qwen2-Math models. The latest version is recommended.
[!Warning]
For requirements on GPU memory and the respective throughput, see similar results of Qwen2 here.
Quick Start
[!Important]
Qwen2-Math-7B-Instruct is an instruction model for chatting;
Qwen2-Math-7B is a base model typically used for completion and few-shot inference, serving as a better starting point for fine-tuning.
🤗 Hugging Face Transformers
Qwen2-Math can be deployed and inferred in the same way as Qwen2. Here we show a code snippet to show you how to use the chat model with transformers:
from transformers import AutoModelForCausalLM, AutoTokenizer
model_name = "Qwen/Qwen2-Math-7B-Instruct"
device = "cuda" # the device to load the model onto
model = 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 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]
🤖 ModelScope
We strongly advise users, especially those in mainland China, to use ModelScope. snapshot_download can help you solve issues concerning downloading checkpoints.
Citation
If you find our work helpful, feel free to give us a citation.
@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}
}