初始化项目，由ModelHub XC社区提供模型

Model: KickItLikeShika/qwen-2.5-7b-instruct-sdft-science
Source: Original Platform

README.md

@@ -0,0 +1,32 @@
+# qwen-2.5-instruct-sdft-science
+This model is a fine-tuned version of [Qwen/Qwen2.5-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-7B-Instruct).
+It has been trained using [TRL](https://github.com/huggingface/trl).
+
+This model has been trained using [Self-Distillation Fine-Tuning](https://arxiv.org/abs/2601.19897) on the Released Science dataset.
+Within this repo, you can find `/eval` directory, containing the evaluation results on the Tool Use evaluation split.
+The model has been trained for 300 steps (the best checkopint we have obtained), scoring 64.5% on the evaluation set.
+
+Our Reproduction Report of Tool Use: https://github.com/KickItLikeShika/sdft-reproduction-note
+
+## Quick start
+
+```python
+from transformers import pipeline
+
+question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
+generator = pipeline("text-generation", model="KickItLikeShika/qwen-2.5-7b-instruct-sdft-science", device="cuda")
+output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
+print(output["generated_text"])
+```
+
+## Training procedure
+W&B Report Tool Use Reproduction Report
+https://api.wandb.ai/links/egyttsteam/d97ty5d9
+
+### Framework versions
+
+- TRL: 0.24.0
+- Transformers: 4.57.1
+- Pytorch: 2.9.0
+- Datasets: 4.3.0
+- Tokenizers: 0.22.2