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|
| --- |
| |
| base_model: Qwen/Qwen2.5-Math-1.5B |
| language: |
| - en |
| pipeline_tag: text-generation |
| tags: |
| - chat |
| library_name: transformers |
| license: apache-2.0 |
| license_link: https://huggingface.co/Qwen/Qwen2.5-Math-1.5B-Instruct/blob/main/LICENSE |
|
|
| --- |
| |
| [](https://hf.co/QuantFactory) |
|
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|
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| # QuantFactory/Qwen2.5-Math-1.5B-Instruct-GGUF |
| 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 |
|
|
| # Original Model Card |
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|
|
| # Qwen2.5-Math-1.5B-Instruct |
|
|
| > [!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. |
| |
|  |
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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 |
| * `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. |
| > |
| |
| ### 🤗 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-1.5B-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} |
| } |
| ``` |
|
|
