---
license: apache-2.0
datasets:
- openbmb/UltraInteract
- stingning/ultrachat
- openchat/openchat_sharegpt4_dataset
- Open-Orca/OpenOrca
tags:
- reasoning
pipeline_tag: text-generation
---


# Links

- 📜 [Paper](https://github.com/OpenBMB/Eurus/blob/main/paper.pdf)
- 🤗 [Eurus Collection](https://huggingface.co/collections/openbmb/eurus-660bc40bec5376b3adc9d1c5)
- 🤗 [UltraInteract](https://huggingface.co/datasets/openbmb/UltraInteract)

# Introduction

Eurus-7B-SFT is fine-tuned from Mistral-7B on all correct actions in UltraInteract, mixing a small proportion of UltraChat, ShareGPT, and OpenOrca examples.

It achieves better performance than other open-source models of similar sizes and even outperforms specialized models in corresponding domains in many cases. 

## Usage

We apply tailored prompts for coding and math, consistent with UltraInteract data formats:

**Coding**

```
[INST] Write Python code to solve the task:
{Instruction} [/INST]
```
**Math-CoT**

```
[INST] Solve the following math problem step-by-step.
Simplify your answer as much as possible. Present your final answer as \\boxed{Your Answer}.
{Instruction} [/INST]
```

**Math-PoT**

```
[INST] Tool available:
[1] Python interpreter
When you send a message containing Python code to python, it will be executed in a stateful Jupyter notebook environment.
Solve the following math problem step-by-step.
Simplify your answer as much as possible.
{Instruction} [/INST]
```

## Citation
```
@misc{yuan2024advancing,
      title={Advancing LLM Reasoning Generalists with Preference Trees}, 
      author={Lifan Yuan and Ganqu Cui and Hanbin Wang and Ning Ding and Xingyao Wang and Jia Deng and Boji Shan and Huimin Chen and Ruobing Xie and Yankai Lin and Zhenghao Liu and Bowen Zhou and Hao Peng and Zhiyuan Liu and Maosong Sun},
      year={2024},
      primaryClass={cs.CL}
}
```