Update README.md

README.md

@@ -4,7 +4,9 @@ I use the 50k [Chinese data](https://huggingface.co/datasets/Chinese-Vicuna/inst
 
     from transformers import LlamaForCausalLM, LlamaTokenizer
     from peft import PeftModel
-  
+    import torch
+    
+    
     tokenizer = LlamaTokenizer.from_pretrained("decapoda-research/llama-7b-hf")
     
     model = LlamaForCausalLM.from_pretrained(
@@ -15,7 +17,18 @@ I use the 50k [Chinese data](https://huggingface.co/datasets/Chinese-Vicuna/inst
     )
     model = PeftModel.from_pretrained(
         model,
-        "Laurie/lora-instruct-chat-50k-cn-en"
+        "Laurie/lora-instruct-chat-50k-cn-en",
         torch_dtype=torch.float16,
         device_map={'': 0}
     )
+    
+    device = "cuda" if torch.cuda.is_available() else "cpu"
+    
+    inputs = tokenizer("什么是自然语言处理？",return_tensors="pt" )
+    
+    model.to(device)
+    
+    with torch.no_grad():
+        inputs = {k: v.to(device) for k, v in inputs.items()}
+        outputs = model.generate(input_ids=inputs["input_ids"], max_new_tokens=129)
+        print(tokenizer.batch_decode(outputs.detach().cpu().numpy(), skip_special_tokens=True))