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---
license: apache-2.0
library_name: transformers
language:
- en
tags:
- code
- software-engineering
- testing
- unit-tests
- r2e-gym
- swe-bench
base_model: Qwen/Qwen2.5-Coder-32B-Instruct
datasets:
- R2E-Gym/R2EGym-TestingAgent-SFT-Trajectories
model_type: qwen2
---
# R2E-TestgenAgent
A specialized execution-based testing agent for generating targeted unit tests in software engineering tasks.
## Model Details
- **Model Type**: Qwen2.5-Coder-32B fine-tuned for test generation
- **Training Data**: R2E-Gym SFT trajectories for testing tasks
- **Use Case**: Automated unit test generation for software engineering
- **Framework**: R2E-Gym ecosystem
## Usage
```python
from transformers import AutoTokenizer, AutoModelForCausalLM
model_name = "r2e-gym/R2E-TestgenAgent"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)
# Use with R2E-Gym framework for best results
from r2egym.agenthub.agent.agent import Agent, AgentArgs
agent_args = AgentArgs.from_yaml("testing_agent_config.yaml")
agent = Agent(name="TestingAgent", args=agent_args)
```
## Training
- **Base Model**: Qwen/Qwen2.5-Coder-32B-Instruct
- **Training Method**: Full fine-tuning with DeepSpeed
- **Learning Rate**: 1e-5
- **Epochs**: 2
- **Context Length**: 20,480 tokens
## Citation
```bibtex
@article{jain2025r2e,
title={R2e-gym: Procedural environments and hybrid verifiers for scaling open-weights swe agents},
author={Jain, Naman and Singh, Jaskirat and Shetty, Manish and Zheng, Liang and Sen, Koushik and Stoica, Ion},
journal={arXiv preprint arXiv:2504.07164},
year={2025}
}
```
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