Update README.md

README.md

@@ -72,8 +72,8 @@ Any model can provide inaccurate or incomplete information, and should be used i
 The fastest way to get started with dRAGon is through direct import in transformers:
 
     from transformers import AutoTokenizer, AutoModelForCausalLM  
-    tokenizer = AutoTokenizer.from_pretrained("dragon-deci-7b-v0")  
-    model = AutoModelForCausalLM.from_pretrained("dragon-deci-7b-v0")  
+    tokenizer = AutoTokenizer.from_pretrained("dragon-deci-7b-v0", trust_remote_code=True)  
+    model = AutoModelForCausalLM.from_pretrained("dragon-deci-7b-v0", trust_remote_code=True)  
 
 Please refer to the generation_test .py files in the Files repository, which includes 200 samples and script to test the model.  The **generation_test_llmware_script.py** includes built-in llmware capabilities for fact-checking, as well as easy integration with document parsing and actual retrieval to swap out the test set for RAG workflow consisting of business documents.