| | --- |
| | 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} |
| | } |
| | ``` |
| |
|
