Update README.md

README.md

@@ -43,8 +43,8 @@ The models can be used as auto-regressive samplers as follows:
 import torch
 from transformers import AutoTokenizer, AutoModelForCausalLM
 
-tokenizer = AutoTokenizer.from_pretrained("Salesforce/xgen-7b-8k-base", trust_remote_code=True)
-model = AutoModelForCausalLM.from_pretrained("Salesforce/xgen-7b-8k-base", torch_dtype=torch.bfloat16)
+tokenizer = AutoTokenizer.from_pretrained("tridungduong16/xgen-7b-8k-base-orca", trust_remote_code=True)
+model = AutoModelForCausalLM.from_pretrained("tridungduong16/xgen-7b-8k-base-orca", torch_dtype=torch.bfloat16)
 inputs = tokenizer("The world is", return_tensors="pt")
 sample = model.generate(**inputs, max_length=128)
 print(tokenizer.decode(sample[0]))