Update qa_summary.py

qa_summary.py

@@ -1,7 +1,7 @@
 import spaces
 from transformers import AutoModelForCausalLM, AutoTokenizer
 
-@spaces.GPU(duration=120)
+@spaces.GPU(duration=60)
 def generate_answer(llm_name, texts, query, queries, mode='validate'):
     
     if llm_name == 'solar':
@@ -40,7 +40,8 @@ def generate_answer(llm_name, texts, query, queries, mode='validate'):
     elif mode == 'h_summarize':
         conversation = [ {'role': 'user', 'content': f'The documents below describe a developing disaster event. Based on these documents, write a brief summary in the form of a paragraph, highlighting the most crucial information. \nDocuments: {template_texts}'} ]
     elif mode == "multi_summarize":
-        conversation = [ {'role': 'user', 'content': f'For the following queries and documents, try to answer the given queries based on the documents.\nQueries: {queries} \nDocuments: {template_texts}.'} ]
+        # conversation = [ {'role': 'user', 'content': f'For the following queries and documents, try to answer the given queries based on the documents. Also, return the top 5 unaltered documents that answer the queries.\nQueries: {queries} \nDocuments: {template_texts}.'} ]
+        conversation = [ {'role': 'user', 'content': f'For the following queries and documents, in a brief paragraph try to answer the given queries based on the documents. Then, return the top 5 documents as provided that answer the queries.\nQueries: {queries} \nDocuments: {template_texts}.'} ]
         
 
     prompt = tokenizer.apply_chat_template(conversation, tokenize=False, add_generation_prompt=True)