Update README.md

README.md

@@ -71,5 +71,46 @@ import torch
 from transformers import AutoTokenizer, AutoModelForCausalLM
 tokenizer = AutoTokenizer.from_pretrained("sambanovasystems/nova-nsql-Llama-2-70B")
 model = AutoModelForCausalLM.from_pretrained("sambanovasystems/nova-nsql-Llama-2-70B", torch_dtype=torch.bfloat16)
-text = "CREATE TABLE stadium (\n    stadium_id number,\n    location text,\n    name text,\n    capacity number,\n    highest number,\n    lowest number,\n    average number\n)\n\nCREATE TABLE singer (\n    singer_id number,\n    name text,\n    country text,\n    song_name text,\n    song_release_year text,\n    age number,\n    is_male others\n)\n\nCREATE TABLE concert (\n    concert_id number,\n    concert_name text,\n    theme text,\n    stadium_id text,\n    year text\n)\n\nCREATE TABLE singer_in_concert (\n    concert_id number,\n    singer_id text\n)\n\n\n-- Using valid SQLite, answer the following questions for the tables provided above.\n\n-- What is the average, minimum, and maximum age of all singers from France?\nSELECT"
+text = "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 average, minimum, and maximum age of all singers from France?
+SELECT"
+input_ids = tokenizer(text, return_tensors="pt").input_ids
+
+generated_ids = model.generate(input_ids, max_length=500)
+print(tokenizer.decode(generated_ids[0], skip_special_tokens=True))
 ```