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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)
# 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
# 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.
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]
> <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}
}
```
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