Update generate_answer.py

generate_answer.py

@@ -1,14 +1,15 @@
-# from transformers import AutoTokenizer, AutoModelForCausalLM
-from transformers import AutoModelForCausalLM, GemmaTokenizer
+from transformers import AutoTokenizer, AutoModelForCausalLM
+# from transformers import AutoModelForCausalLM, GemmaTokenizer
 from langchain.prompts import PromptTemplate
 import os
 
-os.environ["HF_TOKEN"] = os.getenv('HF_TOKEN')
-# model = AutoModelForCausalLM.from_pretrained("microsoft/Phi-3-mini-4k-instruct", trust_remote_code=True)
-# tokenizer = AutoTokenizer.from_pretrained("microsoft/Phi-3-mini-4k-instruct", trust_remote_code=True)
+# os.environ["HF_TOKEN"] = os.getenv('HF_TOKEN')
+model = AutoModelForCausalLM.from_pretrained("microsoft/Phi-3-mini-4k-instruct", trust_remote_code=True)
+tokenizer = AutoTokenizer.from_pretrained("microsoft/Phi-3-mini-4k-instruct", trust_remote_code=True)
 
-model = AutoModelForCausalLM.from_pretrained('google/codegemma-1.1-2b', trust_remote_code=True)
-tokenizer = GemmaTokenizer.from_pretrained('google/codegemma-1.1-2b', trust_remote_code=True)
+# Model used in code generation
+# model = AutoModelForCausalLM.from_pretrained('google/codegemma-1.1-2b', trust_remote_code=True)
+# tokenizer = GemmaTokenizer.from_pretrained('google/codegemma-1.1-2b', trust_remote_code=True)
 
 
 def generate_answer(question):