update

src/llmfunction.jl

@@ -406,9 +406,9 @@ function getdata_decisionMaker(state::Dict, context::Dict, text2textInstructLLM:
       ]
 
     # put in model format
-    prompt = GeneralUtils.formatLLMtext(_prompt; formatname="qwen")
+    prompt = GeneralUtils.formatLLMtext(_prompt; formatname="llama3instruct")
     try
-      response = text2textInstructLLM(prompt)
+      response = text2textInstructLLM(prompt, modelsize="medium")
 
       header = ["Comprehension:", "Plan:", "Code:"]
       dictkey = ["comprehension", "plan", "code"]
@@ -627,12 +627,12 @@ function extractContent_dataframe(df::DataFrame, text2textInstructLLM::Function,
     ]
 
   # put in model format
-  prompt = GeneralUtils.formatLLMtext(_prompt; formatname="qwen")
+  prompt = GeneralUtils.formatLLMtext(_prompt; formatname="llama3instruct")
   header = ["About_resulting_table:", "Search_summary:"]
   dictkey = ["about_resulting_table", "search_summary"]
 
   for i in 1:5
-    response = text2textInstructLLM(prompt)
+    response = text2textInstructLLM(prompt, modelsize="medium")
 
     kw = []
     # use for loop and detect_keyword function to get the exact variation of each keyword in the text then push to kw list
@@ -762,13 +762,13 @@ function getTableNameFromSQL(sql::T, text2textInstructLLM::Function)::Vector{Str
     ]
 
   # put in model format
-  prompt = GeneralUtils.formatLLMtext(_prompt; formatname="qwen")
+  prompt = GeneralUtils.formatLLMtext(_prompt; formatname="llama3instruct")
   header = ["Table_name:"]
   dictkey = ["table_name"]
 
   for attempt in 1:5
     try
-      response = text2textInstructLLM(prompt)
+      response = text2textInstructLLM(prompt, modelsize="medium")
       responsedict = GeneralUtils.textToDict(response, header; 
                                             dictKey=dictkey, symbolkey=true)
       response = copy(JSON3.read(responsedict[:table_name]))
@@ -914,12 +914,12 @@ function compareState(question::String, highValueStateList::Vector{T},
     ]
 
     # put in model format
-    prompt = GeneralUtils.formatLLMtext(_prompt; formatname="qwen")
+    prompt = GeneralUtils.formatLLMtext(_prompt; formatname="llama3instruct")
 
     header = ["Comparison:", "Rationale:", "Selected_response_number:"]
     dictkey = ["comparison", "rationale", "selected_response_number"]
 
-    response = text2textInstructLLM(prompt)
+    response = text2textInstructLLM(prompt, modelsize="medium")
 
     # sometime LLM output something like **Comprehension**: which is not expected
     response = replace(response, "**"=>"")