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| license: mit | |
| base_model: | |
| - Qwen/Qwen3-4B-Instruct-2507 | |
| ## LiteCoder-4b-Terminal-preview | |
| **LiteCoder-4b-Terminal-preview** is part of our series of models specialized in terminal-based interactions and stems from our recent efforts to develop capable small and medium-sized code agent models. The model is fine-tuned from ` | |
| Qwen3-4B-Instruct-2507` on the [LiteCoder-SFT-Terminal-preview](https://huggingface.co/datasets/Lite-Coder/LiteCoder-SFT-Terminal-preview) dataset. | |
| **Notably, this model achieves competitive results using fewer than 1,000 training samples.** By relying entirely on a fully synthetic pipeline—without converting any existing datasets—we were able to secure significant gains on the challenging Terminal Bench, matching the performance of leading open-source models with extreme data efficiency. | |
| ## Released Artifacts | |
| | 2025/12/17 | | | | |
| | --- | --- | --- | | |
| | LiteCoder-4b-Terminal-preview | Model | https://huggingface.co/Lite-Coder/LiteCoder-4b-Terminal-preview | | |
| | LiteCoder-SFT-Terminal-preview | Dataset | https://huggingface.co/datasets/Lite-Coder/LiteCoder-SFT-Terminal-preview | | |
| ## Results | |
| Our models achieve competitive results on **Terminal Bench**, significantly outperforming general-purpose models of similar (and even larger) sizes. | |
| **Terminal Bench 1.0 Performance** | |
| | **Model** | **Agent** | **Results** | | |
| | --- | --- | --- | | |
| | **LiteCoder-30a3b-Terminal-preview** | Terminus 2 | **18.75%** | | |
| | Qwen3-30B-A3B-Nex-N1 | Terminus 2 | 18.75% | | |
| | **LiteCoder-4b-Terminal-preview** | Terminus 2 | **13.75%** | | |
| | Qwen3-30B-A3B-Instruct | Terminus 2 | 12.5% | | |
| | Qwen3-4B-Instruct | Terminus 2 | 5.0% | | |
| **Terminal Bench 2.0 Performance** | |
| | **Model** | **Agent** | **Results** | | |
| | --- | --- | --- | | |
| | **LiteCoder-30a3b-Terminal-preview** | Terminus 2 | **5.6%** | | |
| | **LiteCoder-4b-Terminal-preview** | Terminus 2 | **3.3%** | | |
| | Qwen3-32B | Terminus 2 | 1.9% | | |
| | InternLM3-8B-Nex-N1 | Terminus 2 | 0% | | |
| | Qwen3-8B | Terminus 2 | 0% | | |
| ## Citation | |
| ```latex | |
| @misc{LiteCoder Team, | |
| title={LiteCoder: Advancing Small and Medium-sized Code Agents}, | |
| author={Xiaoxuan Peng and Xinyu Lu and Kaiqi Zhang and Taosong Fang and Boxi Cao and Yaojie Lu}, | |
| year={2025}, | |
| } | |
| ``` | |
| ## Future Directions | |
| - **Scaling Environments:** Expanding the diversity of Docker environments and teacher models to improve generalization. | |
| - **Agentic RL:** Implementing Reinforcement Learning specifically for multi-turn agentic workflows. | |
| ## Team & Contributions | |
| - **Xiaoxuan Peng:** Main Contributor | |
| - [Xinyu Lu](https://scholar.google.com/citations?user=_OsLG8EAAAAJ&hl=zh-CN)**:** Project Lead | |
| - **Kaiqi Zhang:** Contributor | |
| - **Taosong Fang**: Contributor | |
| - **Boxi Cao:** Contributor | |
| - **Yaojie Lu:** Contributor | |
| ## Acknowledgements | |
| LiteCoder builds upon multiple open-source projects, including [Harbor](https://github.com/laude-institute/harbor). The models are trained using [AutoAlign](https://github.com/icip-cas/AutoAlign). | |
| ## Join Us | |
| Join the discussion on our [Discord](https://discord.gg/EX9qZe8B). | |