From 0f4226be58b13a6917790f942a5aa3f5d9c598e6 Mon Sep 17 00:00:00 2001 From: ModelHub XC Date: Sat, 25 Jul 2026 06:42:06 +0800 Subject: [PATCH] =?UTF-8?q?=E5=88=9D=E5=A7=8B=E5=8C=96=E9=A1=B9=E7=9B=AE?= =?UTF-8?q?=EF=BC=8C=E7=94=B1ModelHub=20XC=E7=A4=BE=E5=8C=BA=E6=8F=90?= =?UTF-8?q?=E4=BE=9B=E6=A8=A1=E5=9E=8B?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Model: QuantFactory/Qwen2.5-Math-7B-Instruct-GGUF Source: Original Platform --- .gitattributes | 49 ++++++++++ Qwen2.5-Math-7B-Instruct.Q2_K.gguf | 3 + Qwen2.5-Math-7B-Instruct.Q3_K_L.gguf | 3 + Qwen2.5-Math-7B-Instruct.Q3_K_M.gguf | 3 + Qwen2.5-Math-7B-Instruct.Q3_K_S.gguf | 3 + Qwen2.5-Math-7B-Instruct.Q4_0.gguf | 3 + Qwen2.5-Math-7B-Instruct.Q4_1.gguf | 3 + Qwen2.5-Math-7B-Instruct.Q4_K_M.gguf | 3 + Qwen2.5-Math-7B-Instruct.Q4_K_S.gguf | 3 + Qwen2.5-Math-7B-Instruct.Q5_0.gguf | 3 + Qwen2.5-Math-7B-Instruct.Q5_1.gguf | 3 + Qwen2.5-Math-7B-Instruct.Q5_K_M.gguf | 3 + 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https://huggingface.co/Qwen/Qwen2.5-Math-7B-Instruct/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-7B-Instruct-GGUF +This is quantized version of [Qwen/Qwen2.5-Math-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-Math-7B-Instruct) created using llama.cpp + +# Original Model Card + + + +# Qwen2.5-Math-7B-Instruct + +> [!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. + +![](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] +>
+> +> 🚨 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-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. +> + +### 🤗 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 transformers import AutoModelForCausalLM, AutoTokenizer + +model_name = "Qwen/Qwen2.5-Math-7B-Instruct" +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 $4x+5 = 6x+7$." + +# CoT +messages = [ + {"role": "system", "content": "Please reason step by step, and put your final answer within \\boxed{}."}, + {"role": "user", "content": prompt} +] + +# TIR +messages = [ + {"role": "system", "content": "Please integrate natural language reasoning with programs to solve the problem above, and put your final answer within \\boxed{}."}, + {"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] +``` + +## 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} +} +``` diff --git a/configuration.json b/configuration.json new file mode 100644 index 0000000..bbeeda1 --- /dev/null +++ b/configuration.json @@ -0,0 +1 @@ +{"framework": "pytorch", "task": "text-generation", "allow_remote": true} \ No newline at end of file