Update README.md

README.md

@@ -1,5 +1,5 @@
 ---
-base_model: Qwen/Qwen2.5-7B
+base_model: Qwen/Qwen2.5-Math-7B
 language:
 - en
 pipeline_tag: text-generation
@@ -9,55 +9,13 @@ license_link: https://huggingface.co/Qwen/Qwen2.5-Math-7B/blob/main/LICENSE
 ---
 
 
-# Qwen2.5-Math-7B
+# Qwen2.5-Math-7B-RoPE-300k
 
-> [!Warning]
-> <div align="center">
-> <b>
-> 🚨 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.
-> </b>
-> </div>
-
-## 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. 
-
-![](http://qianwen-res.oss-accelerate-overseas.aliyuncs.com/Qwen2.5/qwen2.5-math-pipeline.jpeg)
-
-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]
-> <div align="center">
-> <b>
-> 🚨 This is a must because <code>transformers</code> integrated Qwen2 codes since <code>4.37.0</code>.
-> </b>
-> </div>
-
-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-7B-Instruct** is an instruction model for chatting;
->
-> **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.
-> 
+This model is a variant of [Qwen/Qwen2.5-Math-7B](https://huggingface.co/Qwen/Qwen2.5-Math-7B), whose RoPE base frequency was increased to 300k in order to extend the model's context from 4k to 32k tokens.
 
 ## Citation
 
-If you find our work helpful, feel free to give us a citation.
+If you find this model useful in your work, please cite the original source:
 
 ```
 @article{yang2024qwen25mathtechnicalreportmathematical,