| | --- |
| | 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). |
| |
|
