Update README.md

README.md

@@ -34,7 +34,6 @@ from transformers import AutoTokenizer, pipeline
 
 peft_model_id = "frankmorales2020/Mistral-7B-text-to-sql-without-flash-attention-2"
 
-# Load Model with PEFT adapter
 model = AutoPeftModelForCausalLM.from_pretrained(
   peft_model_id,
   device_map="auto",
@@ -42,11 +41,9 @@ model = AutoPeftModelForCausalLM.from_pretrained(
 )
 
 tokenizer = AutoTokenizer.from_pretrained(peft_model_id)
-
-# load into pipeline
 pipe = pipeline("text-generation", model=model, tokenizer=tokenizer)
 
-# CASE Number 1:
+CASE Number 1:
 prompt='What was the first album Beyoncé released as a solo artist?'
 prompt = f"Instruct: generate a SQL query.\n{prompt}\nOutput:\n" # for dataset b-mc2/sql-create-context
 outputs = pipe(prompt, max_new_tokens=1024, do_sample=True, temperature=0.9, top_k=50, top_p=0.1, eos_token_id=pipe.tokenizer.eos_token_id, pad_token_id=pipe.tokenizer.eos_token_id)
@@ -62,7 +59,7 @@ Output:
 Generated Answer:
 SELECT first_album FROM table_name_82 WHERE solo_artist = "beyoncé"
 
-# CASE Number 2:
+CASE Number 2:
 prompt='What was the first album Beyoncé released as a solo artist?'
 prompt = f"Instruct: Answer the following question.\n{prompt}\nOutput:\n" 
 outputs = pipe(prompt, max_new_tokens=1024, do_sample=True, temperature=0.9, top_k=50, top_p=0.1, eos_token_id=pipe.tokenizer.eos_token_id, pad_token_id=pipe.tokenizer.eos_token_id)