Update README.md

README.md

@@ -15,3 +15,15 @@ Specifically, HMG attempts to measure the difficulty of generating corresponding
 Also, the role of CAM is to measure the difficulty of understanding the long input contexts due to long-range dependencies by evaluating whether the model’s attention is focused on important segments.
 Built upon both proposed methods, we select the most challenging samples as the influential data to effectively frame the long-range dependencies, thereby achieving better performance of LLMs.
 Comprehensive experiments indicate that GATEAU effectively identifies samples enriched with long-range dependency relations and the model trained on these selected samples exhibits better instruction-following and long-context understanding capabilities.
+
+A simple demo for deployment of the model:
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM
+import torch
+tokenizer = AutoTokenizer.from_pretrained("ssz1111/GATEAU-1k-10k", trust_remote_code=True)
+model = AutoModelForCausalLM.from_pretrained("ssz1111/GATEAU-1k-10k", torch_dtype=torch.bfloat16, trust_remote_code=True, device_map="auto")
+model = model.eval()
+query = "\n\n Hello."
+response, history = model.chat(tokenizer, query, history=[], max_new_tokens=512, temperature=1)
+print(response)
+```