metadata
library_name: peft
base_model: Qwen/Qwen2.5-0.5B-Instruct
tags:
- text-to-sql
- lora
- qwen
- fine-tuned
model_name: Qwen2.5-0.5B-SQL
Qwen2.5-0.5B-SQL LoRA Adapter
This model is a LoRA (Low-Rank Adaptation) adapter for Qwen2.5-0.5B-Instruct, specifically fine-tuned to generate SQL queries from natural language questions and database schemas.
Model Details
- Base Model: Qwen/Qwen2.5-0.5B-Instruct
- Task: Text-to-SQL
- Training Data: b-mc2/sql-create-context
- Language: English
Quick Start (How to use)
To use this adapter, you need to load the base model first and then apply the LoRA weights.
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
from peft import PeftModel
model_id = "Qwen/Qwen2.5-0.5B-Instruct"
adapter_id = "azeemazam/Qwen2.5-0.5B-SQL"
tokenizer = AutoTokenizer.from_pretrained(adapter_id)
base_model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.float16, device_map='auto')
model = PeftModel.from_pretrained(base_model, adapter_id)
def generate_sql(schema, question):
messages = [
{"role": "user", "content": f"Generate SQL.\\n\\nDatabase Schema:\\n{schema}\\n\\nQuestion:\\n{question}"}
]
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer(prompt, return_tensors='pt').to(model.device)
outputs = model.generate(**inputs, max_new_tokens=150)
return tokenizer.decode(outputs[0][inputs.input_ids.shape[1]:], skip_special_tokens=True)
schema = "CREATE TABLE employees (id INT, name TEXT, salary INT)"
question = "Who earns more than 50000?"
print(generate_sql(schema, question))