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--- |
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base_model: Qwen/Qwen2.5-Math-1.5B |
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language: |
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- en |
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pipeline_tag: text-generation |
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tags: |
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- chat |
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library_name: transformers |
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license: apache-2.0 |
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license_link: https://huggingface.co./Qwen/Qwen2.5-Math-1.5B-Instruct/blob/main/LICENSE |
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--- |
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[](https://hf.co/QuantFactory) |
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# QuantFactory/Qwen2.5-Math-1.5B-Instruct-GGUF |
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This is quantized version of [Qwen/Qwen2.5-Math-1.5B-Instruct](https://huggingface.co./Qwen/Qwen2.5-Math-1.5B-Instruct) created using llama.cpp |
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# Original Model Card |
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# Qwen2.5-Math-1.5B-Instruct |
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> [!Warning] |
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> <div align="center"> |
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> <b> |
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> 🚨 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. |
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> </b> |
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> </div> |
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## Introduction |
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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**. |
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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. |
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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. |
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## Model Details |
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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). |
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## Requirements |
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* `transformers>=4.37.0` for Qwen2.5-Math models. The latest version is recommended. |
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> [!Warning] |
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> <div align="center"> |
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> <b> |
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> 🚨 This is a must because <code>transformers</code> integrated Qwen2 codes since <code>4.37.0</code>. |
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> </b> |
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> </div> |
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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). |
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## Quick Start |
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> [!Important] |
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> |
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> **Qwen2.5-Math-1.5B-Instruct** is an instruction model for chatting; |
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> |
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> **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. |
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> |
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### 🤗 Hugging Face Transformers |
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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`: |
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```python |
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from transformers import AutoModelForCausalLM, AutoTokenizer |
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model_name = "Qwen/Qwen2.5-Math-1.5B-Instruct" |
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device = "cuda" # the device to load the model onto |
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model = AutoModelForCausalLM.from_pretrained( |
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model_name, |
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torch_dtype="auto", |
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device_map="auto" |
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) |
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tokenizer = AutoTokenizer.from_pretrained(model_name) |
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prompt = "Find the value of $x$ that satisfies the equation $4x+5 = 6x+7$." |
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# CoT |
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messages = [ |
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{"role": "system", "content": "Please reason step by step, and put your final answer within \\boxed{}."}, |
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{"role": "user", "content": prompt} |
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] |
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# TIR |
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messages = [ |
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{"role": "system", "content": "Please integrate natural language reasoning with programs to solve the problem above, and put your final answer within \\boxed{}."}, |
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{"role": "user", "content": prompt} |
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] |
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text = tokenizer.apply_chat_template( |
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messages, |
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tokenize=False, |
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add_generation_prompt=True |
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) |
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model_inputs = tokenizer([text], return_tensors="pt").to(device) |
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generated_ids = model.generate( |
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**model_inputs, |
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max_new_tokens=512 |
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) |
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generated_ids = [ |
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output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids) |
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] |
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response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0] |
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``` |
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## Citation |
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If you find our work helpful, feel free to give us a citation. |
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``` |
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@article{yang2024qwen2, |
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title={Qwen2 technical report}, |
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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}, |
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journal={arXiv preprint arXiv:2407.10671}, |
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year={2024} |
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} |
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``` |
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