Update README.md

README.md

@@ -1,3 +1,67 @@
----
-license: cc-by-nc-4.0
----
+---
+license: cc-by-nc-4.0
+---
+## 🚀Installation
+
+1. Clone the repositories:
+
+   ```bash
+   git clone https://github.com/zjunlp/ChineseHarm-bench
+   cd ChineseHarm-bench
+   git clone https://github.com/hiyouga/LLaMA-Factory
+   ```
+
+2. Install dependencies:
+
+   ```bash
+   cd LLaMA-Factory
+   pip install -e ".[torch,metrics]" 
+   ```
+
+## 📚Inference
+
+We release the following variants of our harmful content detection model:
+
+🔹 Single Inference (Example)
+
+Run single-input inference using the ChineseGuard-1.5B model:
+
+```
+SCRIPT_PATH="../infer/single_infer.py"
+model_name="zjunlp/ChineseGuard-1.5B"
+text="代发短信，有想做的联系我，无押金"
+
+python $SCRIPT_PATH \
+    --model_name $model_name \
+    --text $text
+```
+
+🔸 Batch Inference (Multi-NPU or Multi-GPU)
+
+To run inference on the entire ChineseHarm-Bench using ChineseGuard-1.5B and 8 NPUs:
+
+```
+SCRIPT_PATH="../infer/batch_infer.py"
+model_name="zjunlp/ChineseHarm-1.5B"
+file_name="../benchmark/bench.json"
+output_file="../benchmark/bench_ChineseHarm-1.5B.json"
+
+python $SCRIPT_PATH \
+    --model_name $model_name \
+    --file_name $file_name \
+    --output_file $output_file \
+    --num_npus 8
+
+```
+
+> For more configuration options (e.g., batch size, device selection, custom prompt templates), please refer to `single_infer.py` and `batch_infer.py`.
+>
+> **Note:** The inference scripts support both NPU and GPU devices.
+
+## 🚩Citation
+
+Please cite our repository if you use ChineseGuard in your work. Thanks!
+
+```bibtex
+
+```