Upload folder using huggingface_hub

.gitattributes

@@ -34,3 +34,7 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
 tokenizer.json filter=lfs diff=lfs merge=lfs -text
+figures/environment_preparation.png filter=lfs diff=lfs merge=lfs -text
+figures/feasibility_check.png filter=lfs diff=lfs merge=lfs -text
+figures/overall_pipeline.png filter=lfs diff=lfs merge=lfs -text
+figures/task_sampling.png filter=lfs diff=lfs merge=lfs -text

README.md

@@ -0,0 +1,69 @@
+## 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) framework.
+
+## Join Us
+
+Join the discussion on our [Discord](https://discord.gg/EX9qZe8B).