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README.md

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+
+---
+
+base_model: Qwen/Qwen2.5-1.5B
+language:
+- en
+pipeline_tag: text-generation
+library_name: transformers
+license: apache-2.0
+license_link: https://huggingface.co/Qwen/Qwen2.5-Math-1.5B/blob/main/LICENSE
+
+---
+
+[![QuantFactory Banner](https://lh7-rt.googleusercontent.com/docsz/AD_4nXeiuCm7c8lEwEJuRey9kiVZsRn2W-b4pWlu3-X534V3YmVuVc2ZL-NXg2RkzSOOS2JXGHutDuyyNAUtdJI65jGTo8jT9Y99tMi4H4MqL44Uc5QKG77B0d6-JfIkZHFaUA71-RtjyYZWVIhqsNZcx8-OMaA?key=xt3VSDoCbmTY7o-cwwOFwQ)](https://hf.co/QuantFactory)
+
+
+# QuantFactory/Qwen2.5-Math-1.5B-GGUF
+This is quantized version of [Qwen/Qwen2.5-Math-1.5B](https://huggingface.co/Qwen/Qwen2.5-Math-1.5B) created using llama.cpp
+
+# Original Model Card
+
+
+# Qwen2.5-Math-1.5B
+
+> [!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-1.5B-Instruct** is an instruction model for chatting;
+>
+> **Qwen2.5-Math-1.5B** is a base model typically used for completion and few-shot inference, serving as a better starting point for fine-tuning.
+
+## 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}
+}
+```