jklj077 commited on
Commit
5f4bbc4
1 Parent(s): 03004f9

Create README.md

Browse files
Files changed (1) hide show
  1. README.md +70 -0
README.md ADDED
@@ -0,0 +1,70 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ ---
2
+ base_model: Qwen/Qwen2.5-7B
3
+ language:
4
+ - en
5
+ pipeline_tag: text-generation
6
+ tags:
7
+ - chat
8
+ library_name: transformers
9
+ license: apache-2.0
10
+ ---
11
+
12
+
13
+ # Qwen2.5-Math-7B-Instruct
14
+
15
+ > [!Warning]
16
+ > <div align="center">
17
+ > <b>
18
+ > 🚨 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.
19
+ > </b>
20
+ > </div>
21
+
22
+ ## Introduction
23
+
24
+ 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**.
25
+
26
+ 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.
27
+
28
+ ![](http://qianwen-res.oss-accelerate-overseas.aliyuncs.com/Qwen2.5/qwen2.5-math-pipeline.jpeg)
29
+
30
+ 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.
31
+
32
+ ## Model Details
33
+
34
+
35
+ 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).
36
+
37
+
38
+ ## Requirements
39
+ * `transformers>=4.37.0` for Qwen2.5-Math models. The latest version is recommended.
40
+
41
+ > [!Warning]
42
+ > <div align="center">
43
+ > <b>
44
+ > 🚨 This is a must because <code>transformers</code> integrated Qwen2 codes since <code>4.37.0</code>.
45
+ > </b>
46
+ > </div>
47
+
48
+ 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).
49
+
50
+ ## Quick Start
51
+
52
+ > [!Important]
53
+ >
54
+ > **Qwen2.5-Math-7B-Instruct** is an instruction model for chatting;
55
+ >
56
+ > **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.
57
+ >
58
+
59
+ ## Citation
60
+
61
+ If you find our work helpful, feel free to give us a citation.
62
+
63
+ ```
64
+ @article{yang2024qwen2,
65
+ title={Qwen2 technical report},
66
+ 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},
67
+ journal={arXiv preprint arXiv:2407.10671},
68
+ year={2024}
69
+ }
70
+ ```