Update README.md

README.md

@@ -25,8 +25,20 @@ You may use this model for fine-tuning in downstream tasks, we recommend using o
 Usage:
 
 ```python
-from transformers import AutoModelForCausalLM, AutoTokenizer
+from transformers import AutoModelForCausalLM, AutoTokenizer, TextStreamer
 
 tokenizer = AutoTokenizer.from_pretrained("hiyouga/Baichuan2-7B-Chat-LLaMAfied", use_fast=False)
 model = AutoModelForCausalLM.from_pretrained("hiyouga/Baichuan2-7B-Chat-LLaMAfied").cuda()
+streamer = TextStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True)
+
+query = "<reserved_106>晚上睡不着怎么办<reserved_107>"
+inputs = tokenizer([query], return_tensors="pt")
+inputs = inputs.to("cuda")
+generate_ids = model.generate(**inputs, max_new_tokens=256, streamer=streamer)
+```
+
+You could also alternatively launch a CLI demo by using the script in [LLaMA-Efficient-Tuning](https://github.com/hiyouga/LLaMA-Efficient-Tuning)
+
+```bash
+python src/cli_demo.py --template baichuan2 --model_name_or_path Baichuan2-7B-Chat-LLaMAfied
 ```