初始化项目，由ModelHub XC社区提供模型

Model: IIGroup/X-Coder-SFT-Qwen3-8B
Source: Original Platform

README.md

@@ -0,0 +1,100 @@
+---
+license: apache-2.0
+base_model:
+  - Qwen/Qwen3-8B-Base
+datasets:
+  - IIGroup/X-Coder-SFT-376k
+language:
+  - en
+tags:
+  - code
+  - sft
+  - competitive-programming
+---
+
+# X-Coder-SFT-Qwen3-8B
+
+X-Coder-SFT-Qwen3-8B is a code generation model fine-tuned on fully synthetic instruction data, designed for competitive programming tasks. It serves as the foundation for subsequent RLVR training.
+
+## Model Description
+
+- **Base Model**: [Qwen/Qwen3-8B-Base](https://huggingface.co/Qwen/Qwen3-8B-Base)
+- **Training Method**: Supervised Fine-Tuning (SFT)
+- **Training Data**: [IIGroup/X-Coder-SFT-376k](https://huggingface.co/datasets/IIGroup/X-Coder-SFT-376k)
+- **Parameters**: 8B
+
+## Training
+
+This model was trained using [ms-swift](https://github.com/modelscope/ms-swift). For training details and code, please refer to the [X-Coder GitHub repository](https://github.com/JieWu02/X-Coder).
+
+### Training Hyperparameters
+
+| Parameter | Value |
+|-----------|-------|
+| Base Model | Qwen/Qwen3-8B-Base |
+| Training Type | Full Parameter |
+| Epochs | 8 |
+| Global Batch Size | 128 |
+| Learning Rate | 5e-5 |
+| Max Grad Norm | 1.0 |
+| Max Length | 32768 |
+| Torch Dtype | bfloat16 |
+| DeepSpeed | Zero3 Offload (80GB VRAM) / Zero2 (142GB VRAM) |
+| Packing | True (2x faster training, slightly worse performance) |
+
+## Performance on LiveCodeBench v5.
+
+![Results](results.png)
+
+## Recommended Inference Parameters
+
+| Parameter | Value |
+|-----------|-------|
+| temperature | 0.6 |
+| top_p | 0.95 |
+| top_k | 20 (or -1 to disable) |
+| max_new_tokens | 32768 |
+
+## Usage
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model_name = "IIGroup/X-Coder-SFT-Qwen3-8B"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto")
+
+prompt = "Write a Python function to solve the two sum problem."
+inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
+outputs = model.generate(
+    **inputs,
+    max_new_tokens=32768,
+    temperature=0.6,
+    top_p=0.95,
+    top_k=20,
+    do_sample=True
+)
+print(tokenizer.decode(outputs[0], skip_special_tokens=True))
+```
+
+## Related Models
+
+- **RL Model**: [IIGroup/X-Coder-RL-Qwen3-8B](https://huggingface.co/IIGroup/X-Coder-RL-Qwen3-8B) - RLVR trained version achieving 64.0 on LiveCodeBench
+
+## Citation
+
+```bibtex
+@misc{wu2026xcoderadvancingcompetitiveprogramming,
+      title={X-Coder: Advancing Competitive Programming with Fully Synthetic Tasks, Solutions, and Tests}, 
+      author={Jie Wu and Haoling Li and Xin Zhang and Jiani Guo and Jane Luo and Steven Liu and Yangyu Huang and Ruihang Chu and Scarlett Li and Yujiu Yang},
+      year={2026},
+      eprint={2601.06953},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL},
+      url={https://arxiv.org/abs/2601.06953}, 
+}
+```
+
+## License
+
+This project is licensed under the Apache License 2.0.