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---
license: apache-2.0
---
# Eurus-2-7B-SFT
## Links
- 📜 [Blog]()
- 🤗 [PRIME Collection](https://huggingface.co/PRIME-RL)
- 🤗 [SFT Data](https://huggingface.co/datasets/PRIME-RL/Eurus-2-SFT-Data)
## Introduction
Eurus-2-7B-SFT is fine-tuned from [Qwen2.5-Math-7B-Base](https://huggingface.co/Qwen/Qwen2.5-Math-7B) for its great mathematical capabilities. It trains on [Eurus-2-SFT-Data](https://huggingface.co/datasets/PRIME-RL/Eurus-2-SFT-Data), which is an action-centric chain-of-thought reasoning dataset.
We apply imitation learning (supervised finetuning) as a warmup stage to teach models to learn reasoning patterns, , serving as a starter model for [Eurus-2-7B-PRIME](https://huggingface.co/PRIME-RL/Eurus-2-7B-PRIME).
## Usage
We apply tailored prompts for coding and math task:
**System Prompt**
```
\nWhen tackling complex reasoning tasks, you have access to the following actions. Use them as needed to progress through your thought process.\n\n[ASSESS]\n\n[ADVANCE]\n\n[VERIFY]\n\n[SIMPLIFY]\n\n[SYNTHESIZE]\n\n[PIVOT]\n\n[OUTPUT]\n\nYou should strictly follow the format below:\n\n[ACTION NAME]\n\n# Your action step 1\n\n# Your action step 2\n\n# Your action step 3\n\n...\n\nNext action: [NEXT ACTION NAME]\n
```
**Coding**
```
{question} + "\n\nWrite Python code to solve the problem. Present the code in \n```python\nYour code\n```\nat the end.
```
**Math**
```
{question} + "\n\nPresent the answer in LaTex format: \\boxed{Your answer}"
```
## Evaluation
After finetuning, the performance of our Eurus-2-7B-SFT is shown in the following figure.
## Citation
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