Update README.md

README.md

@@ -1,3 +1,42 @@
----
-license: apache-2.0
----
+---
+base_model: Qwen/Qwen2.5-Math-7B-Instruct
+language:
+- en
+pipeline_tag: text-generation
+tags:
+- chat
+quantized_by: mukel
+license: apache-2.0
+license_link: https://huggingface.co/Qwen/Qwen2.5-Math-7B-Instruct/blob/main/LICENSE
+---
+
+> [!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>
+
+# GGUF models for qwen2.java 
+Pure .gguf Q4_0 and Q8_0 quantizations of Qwen 2.5 models, ready to consume by `qwen2.java`.
+
+In the wild, Q8_0 quantizations are fine, but Q4_0 quantizations are rarely pure e.g. the token embeddings are quantized with Q6_K, instead of Q4_0.  
+A pure Q4_0 quantization can be generated from a high precision (F32, F16, BFLOAT16) .gguf source with the llama-quantize utility from llama.cpp as follows:
+```
+./llama-quantize --pure ./Qwen-2.5-7B-Instruct-BF16.gguf ./Qwen-2.5-7B-Instruct-Q4_0.gguf Q4_0
+```
+
+## 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).
+