| | ---
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| | base_model: Qwen/Qwen2.5-1.5B
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| | language:
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| | - zho
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| | - eng
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| | - fra
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| | - spa
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| | - por
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| | - deu
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| | - ita
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| | - rus
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| | - jpn
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| | - kor
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| | - vie
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| | - tha
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| | - ara
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| | pipeline_tag: text-generation
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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/blob/main/LICENSE
|
| | ---
|
| |
|
| | [](https://hf.co/QuantFactory)
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| |
|
| |
|
| | # QuantFactory/Qwen2.5-Math-1.5B-GGUF
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| | This is quantized version of [Qwen/Qwen2.5-Math-1.5B](https://huggingface.co/Qwen/Qwen2.5-Math-1.5B) created using llama.cpp
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| |
|
| | # Original Model Card
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| |
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| |
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| | # Qwen2.5-Math-1.5B
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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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| |
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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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| |
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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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| | 
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| |
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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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| |
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| |
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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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| |
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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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| |
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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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| |
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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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| |
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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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| |
|
| | ## 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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| |
|
