Update README.md

README.md

@@ -61,7 +61,7 @@ The following hyperparameters were used during training:
 from transformers import AutoTokenizer, AutoModelForCausalLM
 import torch
 
-model_id = "SagarKrishna/Llama_3_8b_Instruct_Text2Sql_FullPrecision_Finetuned"
+model_id = "ByteForge/Llama_3_8b_Instruct_Text2Sql_FullPrecision_Finetuned"
 
 tokenizer = AutoTokenizer.from_pretrained(model_id)
 
@@ -71,10 +71,50 @@ model = AutoModelForCausalLM.from_pretrained(
     device_map="auto",
 )
 
+prompt="""
+CREATE TABLE stadium (
+    stadium_id number,
+    location text,
+    name text,
+    capacity number,
+    highest number,
+    lowest number,
+    average number
+)
+
+CREATE TABLE singer (
+    singer_id number,
+    name text,
+    country text,
+    song_name text,
+    song_release_year text,
+    age number,
+    is_male others
+)
+
+CREATE TABLE concert (
+    concert_id number,
+    concert_name text,
+    theme text,
+    stadium_id text,
+    year text
+)
+
+CREATE TABLE singer_in_concert (
+    concert_id number,
+    singer_id text
+)
+
+-- Using valid SQLite, answer the following questions for the tables provided above.
+
+-- What is the maximum, the average, and the minimum capacity of stadiums ? (Generate 1 Sql query. No explaination needed)
+
+answer:
+"""
 
 messages = [
     {"role": "system", "content": "You are an text to SQL query translator. Users will ask you questions in English and you will generate a SQL query based on the provided SCHEMA.\nSCHEMA:\nCREATE TABLE match_season (College VARCHAR, POSITION VARCHAR)"},
-    {"role": "user", "content": "Which college have both players with position midfielder and players with position defender?"},
+    {"role": "user", "content": prompt},
 ]
 
 input_ids = tokenizer.apply_chat_template(