---
title: Qwen2.5-Math-72B-Instruct-AWQ
canonical_url: "https://www.modelscope.cn/models/ShelterW/Qwen2.5-Math-72B-Instruct-AWQ"
md_url: "https://www.modelscope.cn/models/ShelterW/Qwen2.5-Math-72B-Instruct-AWQ.md"
repository: ShelterW/Qwen2.5-Math-72B-Instruct-AWQ
chinese_name: "通义千问2.5-数学-72B-Instruct-AWQ"
last_updated: 2024-11-18
license: other
pipeline_tag: text-generation
tasks:
- text-generation
model_type:
- qwen2
architectures:
- Qwen2ForCausalLM
base_model:
- Qwen/Qwen2.5-Math-72B
base_model_relation: quantized
parameters: 11.9B
tensor_type:
- F16
- I32
- BF16
library_name:
- safetensors
language:
- en
inference_backends:
- "deploy_task text/emb"
- "lmdeploy 0.9.1"
- "lmdeploy_turbomind 0.9.1"
- "sglang 0.5.2"
- "vllm 0.9.2"
downloads: 181
stars: 1
tags:
- chat
---
# Qwen2.5-Math-72B-Instruct-AWQ
> Qwen2.5-Math-72B-Instruct-AWQ - ShelterW 在 ModelScope 开源的模型。Qwen2.5-Math-72B-Instruct-AWQ
ShelterW/Qwen2.5-Math-72B-Instruct-AWQ 是 ModelScope 魔搭社区上的 11.9B 参数text-generation模型，采用 other 许可，基于 Qwen/Qwen2.5-Math-72B 构建，可用 deploy_task text/emb、lmdeploy 0.9.1、lmdeploy_turbomind 0.9.1 部署。
- **Repository**: ShelterW/Qwen2.5-Math-72B-Instruct-AWQ
- **License**: other
- **Tasks**: text-generation
- **Parameters**: 11.9B
- **Base model**: Qwen/Qwen2.5-Math-72B
- **Inference backends**: deploy_task text/emb, lmdeploy 0.9.1, lmdeploy_turbomind 0.9.1, sglang 0.5.2, vllm 0.9.2
- **Tags**: chat
- **Downloads**: 181
- **Stars**: 1
- **Last updated**: 2024-11-18
Source: https://www.modelscope.cn/models/ShelterW/Qwen2.5-Math-72B-Instruct-AWQ
---
# Qwen2.5-Math-72B-Instruct-AWQ
> [!Warning]
>
>
> 🚨 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.
>
>
## Introduction
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**.
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.
## Model Details
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).
## Requirements
* `transformers>=4.37.0` for Qwen2.5-Math models. The latest version is recommended.
> [!Warning]
>
>
> 🚨 This is a must because transformers integrated Qwen2 codes since 4.37.0.
>
>
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).
## Quick Start
> [!Important]
>
> **Qwen2.5-Math-72B-Instruct-AWQ** is an instruction model for chatting;
>
> **Qwen2.5-Math-72B** is a base model typically used for completion and few-shot inference, serving as a better starting point for fine-tuning.
>
### 🤗 Hugging Face Transformers
Qwen2.5-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`:
```python
from modelscope import AutoModelForCausalLM, AutoTokenizer
model_name = "ShelterW/Qwen2.5-Math-72B-Instruct-AWQ"
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 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]
```
### 🤖 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}
}
```